subject:(Multitarget Tracking)

1. Brendel, William. From multitarget tracking to event recognition in videos.

Degree: PhD, Computer Science, 2011, Oregon State University

URL: http://hdl.handle.net/1957/21315

► This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from… (more)

▼ This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from a host of sources, including motion blur, occlusions, and dynamic cluttered backgrounds. We show that these challenges can be successfully addressed by using a multiscale, volumetric video representation, and taking into account various constraints between events offered by domain knowledge. The dissertation presents our two alternative approaches to multitarget tracking. The first approach seeks to transitively link object detections across consecutive video frames by finding the maximum independent set of a graph of all object detections. Two maximum-independent-set algorithms are specified, and their convergence properties theoretically analyzed. The second approach hierarchically partitions the space-time volume of a video into tracks of objects, producing a segmentation graph of that video. The resulting tracks encode rich contextual cues between salient video parts in space and time, and thus facilitate event recognition, and segmentation in space and time. We also describe our two alternative approaches to event recognition. The first approach seeks to learn a structural probabilistic model of an event class from training videos represented by hierarchical segmentation graphs. The graph model is then used for inference of event occurrences in new videos. Learning and inference algorithms are formulated within the same framework, and their convergence rates theoretically analyzed. The second approach to event recognition uses probabilistic first-order logic for reasoning over continuous time intervals. We specify the syntax, learning, and inference algorithms of this probabilistic event logic. Qualitative and quantitative results on benchmark video datasets are also presented. The results demonstrate that our approaches provide consistent video interpretation with respect to acquired domain knowledge. We outperform most of the state-of-the-art approaches on benchmark datasets. We also present our new basketball dataset that complements existing benchmarks with new challenges. Advisors/Committee Members: Todorovic, Sinisa (advisor), Dietterich, Thomas (committee member).

Subjects/Keywords: multitarget tracking; Computer vision

…fundamental problems in computer vision: multitarget tracking and activity recognition. Given a… …the goals G1–G3 with the following contributions: • Multitarget tracking is formulated as a… …theoretical analysis. • A multiscale multitarget tracking has been formulated as blocky video… …representation. Multitarget tracking is addressed in Chapter 3. Given a set of objects extracted from… …pairwise constraints – for example in the context of segmentation or multitarget tracking – can…

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APA (6^th Edition):

Brendel, W. (2011). From multitarget tracking to event recognition in videos. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/21315

Chicago Manual of Style (16^th Edition):

Brendel, William. “From multitarget tracking to event recognition in videos.” 2011. Doctoral Dissertation, Oregon State University. Accessed April 16, 2021. http://hdl.handle.net/1957/21315.

MLA Handbook (7^th Edition):

Brendel, William. “From multitarget tracking to event recognition in videos.” 2011. Web. 16 Apr 2021.

Vancouver:

Brendel W. From multitarget tracking to event recognition in videos. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1957/21315.

Council of Science Editors:

Brendel W. From multitarget tracking to event recognition in videos. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/21315

Georgia Tech

2. Leven, William Franklin. Approximate Cramer-Rao Bounds for Multiple Target Tracking.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

URL: http://hdl.handle.net/1853/10507

► The main objective of this dissertation is to develop mean-squared error performance predictions for multiple target tracking. Envisioned as an approximate Cramer-Rao lower bound, these… (more)

▼ The main objective of this dissertation is to develop mean-squared error performance predictions for multiple target tracking. Envisioned as an approximate Cramer-Rao lower bound, these performance predictions allow a tracking system designer to quickly and efficiently predict the general performance trends of a tracking system. The symmetric measurement equation (SME) approach to multiple target tracking (MTT) lies at the heart of our method. The SME approach, developed by Kamen et al., offers a unique solution to the data association problem. Rather than deal directly with this problem, the SME approach transforms it into a nonlinear estimation problem. In this way, the SME approach sidesteps report-to-track associations. Developing performance predictions using the SME approach requires work in several areas: (1) extending SME tracking theory, (2) developing nonlinear filters for SME tracking, and (3) understanding techniques for computing Cramer-Rao error bounds in nonlinear filtering. First, on the SME front, we extend SME tracking theory by deriving a new set of SME equations for motion in two dimensions. We also develop the first realistic and efficient method for SME tracking in three dimensions. Second, we apply, for the first time, the unscented Kalman filter (UKF) and the particle filter to SME tracking. Using Taylor series analysis, we show how different SME implementations affect the performance of the EKF and UKF and show how Kalman filtering degrades for the SME approach as the number of targets rises. Third, we explore the Cramer-Rao lower bound (CRLB) and the posterior Cramer-Rao lower bound (PCRB) for computing MTT error predictions using the SME. We show how to compute performance predictions for multiple target tracking using the PCRB, as well as address confusion in the tracking community about the proper interpretation of the PCRB for tracking scenarios. Advisors/Committee Members: Aaron D. Lanterman (Committee Chair), James McClellan (Committee Member), Magnus Egerstedt (Committee Member), Mark Richards (Committee Member), Nicoleta Serban (Committee Member).

Subjects/Keywords: Multitarget tracking; UKF; SME; EKF

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APA (6^th Edition):

Leven, W. F. (2006). Approximate Cramer-Rao Bounds for Multiple Target Tracking. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/10507

Chicago Manual of Style (16^th Edition):

Leven, William Franklin. “Approximate Cramer-Rao Bounds for Multiple Target Tracking.” 2006. Doctoral Dissertation, Georgia Tech. Accessed April 16, 2021. http://hdl.handle.net/1853/10507.

MLA Handbook (7^th Edition):

Leven, William Franklin. “Approximate Cramer-Rao Bounds for Multiple Target Tracking.” 2006. Web. 16 Apr 2021.

Vancouver:

Leven WF. Approximate Cramer-Rao Bounds for Multiple Target Tracking. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1853/10507.

Council of Science Editors:

Leven WF. Approximate Cramer-Rao Bounds for Multiple Target Tracking. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/10507

University of Oulu

3. Macagnano, D. (Davide). Multitarget localization and tracking:active and passive solutions.

Degree: 2012, University of Oulu

URL: http://urn.fi/urn:isbn:9789514298592

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Abstract Localization and tracking of multiple targets is becoming an essential feature of modern communication services and systems. Although necessary in many contexts, such as… (more)

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Abstract Localization and tracking of multiple targets is becoming an essential feature of modern communication services and systems. Although necessary in many contexts, such as surveillance and monitoring applications, low-complexity and reliable solutions capable of coping with different degrees of information are not yet available. This thesis deals with different problems that are encountered in localization and tracking applications and aims to establish a broad understanding of multitarget systems ranging from complete active to incomplete passive solutions in dynamic scenarios. Thereby we start by investigating a fully algebraic framework which is proved to be advantageous in dynamic contexts characterized by no a-priori knowledge. Subsequently we extend the approach to improve its robustness versus corrupted observations. Finally we focus on a Bayesian formulation of the passive multitarget tracking (MTT) problem. The Thesis is based on three parts. The ﬁrst part focuses on a low complexity mathematical representation of the active problem (i.e manifold-based solution). In particular, the spectrum of the matrices used to represent target locations within an algebraic, multidimensional scaling (MDS) based, solution is characterized statistically. In so doing we propose a novel Jacobi-based eigenspace tracking algorithms for Gramian matrices which is shown to be particularly convenient in a multidimensional scaling formulation of the multitarget tracking problem. The second part deals with incomplete-active multitarget scenarios as well as eventual disturbances on the ranging measurements such as bias due to non-line-of-sight conditions. In particular the aforementioned algebraic solution is extended to cope with heterogeneous information and to incorporate eventual knowledge on the conﬁdence of the measurement information. To do so we solve the classical multidimensional scaling (C-MDS) over a novel kernel matrix and show how the intrinsic nature of this formulation allows to deal with heterogeneous information, speciﬁcally angle and distance measurements. Finally, the third part focuses on the random ﬁnite sets formulation of Bayesian multisensor MTT problem for passive scenarios. In this area a new gating strategy is proposed to lower the computational complexity of the algorithms without compromising their performance.

Tiivistelmä Useiden kohteiden yhtäaikaisesta paikannuksesta ja seurannasta on tulossa olennainen osa nykyaikaisia viestinnän palveluita ja järjestelmiä. Huolimatta siitä, että yhtäaikainen paikannus on erittäin tarpeellinen osa monissa yhteyksissä, kuten valvonnan ja kontrolloinnin sovelluksissa, siihen ei ole olemassa kompleksisuudeltaan alhaista ratkaisua, joka ottaisi huomioon kaiken saatavilla olevan informaation. Väitöskirja käsittelee useiden kohteiden paikannukseen ja seurantaan liittyviä ongelmia, ja se keskittyy antamaan laajan ymmärryksen aktiivisista täydellisistä menetelmistä passiivisiin epätäydellisiin menetelmiin dynaamisissa ympäristöissä. Saavuttaakseen…

Advisors/Committee Members: Iinatti, J. (Jari), Abreu, G. (Giuseppe).

Subjects/Keywords: Jacobi algorithm; adaptive gating; multidimensional scaling (MDS); multitarget localization; passive multitarget tracking; subspace tracking; Jacobin algoritmi; adaptiivinen portitus; alivaruusseuranta; monikohdepaikannus; moniulotteinen skaalaus (MDS); passiivinen monikohdeseuranta

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Macagnano, D. (. (2012). Multitarget localization and tracking:active and passive solutions. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789514298592

Chicago Manual of Style (16^th Edition):

Macagnano, D (Davide). “Multitarget localization and tracking:active and passive solutions.” 2012. Doctoral Dissertation, University of Oulu. Accessed April 16, 2021. http://urn.fi/urn:isbn:9789514298592.

MLA Handbook (7^th Edition):

Macagnano, D (Davide). “Multitarget localization and tracking:active and passive solutions.” 2012. Web. 16 Apr 2021.

Vancouver:

Macagnano D(. Multitarget localization and tracking:active and passive solutions. [Internet] [Doctoral dissertation]. University of Oulu; 2012. [cited 2021 Apr 16]. Available from: http://urn.fi/urn:isbn:9789514298592.

Council of Science Editors:

Macagnano D(. Multitarget localization and tracking:active and passive solutions. [Doctoral Dissertation]. University of Oulu; 2012. Available from: http://urn.fi/urn:isbn:9789514298592

KTH

4. Andersson, Anton. Offline Sensor Fusion for Multitarget Tracking using Radar and Camera Detection.

Degree: Computer Science and Communication (CSC), 2017, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208344

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Autonomous driving systems are rapidly improving and may have the ability to change society in the coming decade. One important part of these systems… (more)

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Autonomous driving systems are rapidly improving and may have the ability to change society in the coming decade. One important part of these systems is the interpretation of sensor information into trajectories of objects. In this master’s thesis, we study an energy minimisation method with radar and camera measurements as inputs. An energy is associated with the trajectories; this takes the measurements, the objects’ dynamics and more factors into consideration. The trajectories are chosen to minimise this energy, using a gradient descent method. The lower the energy, the better the trajectories are expected to match the real world. The processing is performed offline, as opposed to in real time. Offline tracking can be used in the evaluation of the sensors’ and the real time tracker’s performance. Offline processing allows for the use of more computer power. It also gives the possibility to use data that was collected after the considered point in time. A study of the parameters of the used energy minimisation method is presented, along with variations of the initial method. The results of the method is an improvement over the individual inputs, as well as over the real time processing used in the cars currently. In the parameter study it is shown which components of the energy function are improving the results.

Mycket resurser läggs på utveckling av självkörande bilsystem. Dessa kan komma att förändra samhället under det kommande decenniet. En viktig del av dessa system är behandling och tolkning av sensordata och skapande av banor för objekt i omgivningen. I detta examensarbete studeras en energiminimeringsmetod tillsammans med radar- och kameramätningar. En energi beräknas för banorna. Denna tar mätningarna, objektets dynamik och fler faktorer i beaktande. Banorna väljs för att minimera denna energi med hjälp av gradientmetoden. Ju lägre energi, desto bättre förväntas banorna att matcha verkligheten. Bearbetning sker offline i motsats till i realtid; offline-bearbetning kan användas då prestandan för sensorer och realtidsbehandlingen utvärderas. Detta möjliggör användning av mer datorkraft och ger möjlighet att använda data som samlats in efter den aktuella tidpunkten. En studie av de ingående parametrarna i den använda energiminimeringsmetoden presenteras, tillsammans med justeringar av den ursprungliga metoden. Metoden ger ett förbättrat resultat jämfört med de enskilda sensormätningarna, och även jämfört med den realtidsmetod som används i bilarna för närvarande. I parameterstudien visas vilka komponenter i energifunktionen som förbättrar metodens prestanda.

Subjects/Keywords: Sensor Fusion; Energy Minimisation; Multitarget Tracking; Autonomous Vehicles; Computer Sciences; Datavetenskap (datalogi)

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APA (6^th Edition):

Andersson, A. (2017). Offline Sensor Fusion for Multitarget Tracking using Radar and Camera Detection. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Andersson, Anton. “Offline Sensor Fusion for Multitarget Tracking using Radar and Camera Detection.” 2017. Thesis, KTH. Accessed April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Andersson, Anton. “Offline Sensor Fusion for Multitarget Tracking using Radar and Camera Detection.” 2017. Web. 16 Apr 2021.

Vancouver:

Andersson A. Offline Sensor Fusion for Multitarget Tracking using Radar and Camera Detection. [Internet] [Thesis]. KTH; 2017. [cited 2021 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Andersson A. Offline Sensor Fusion for Multitarget Tracking using Radar and Camera Detection. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

McMaster University

5. Punithakumar, K. Nonlinear Filtering Algorithms for Multitarget Tracking.

Degree: 2007, McMaster University

URL: http://hdl.handle.net/11375/16632

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Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. Random finite set theory provides a rigorous… (more)

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Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. Random finite set theory provides a rigorous foundation to multitarget tracking problems. It provides a framework to represent the full multitarget posterior in contrast to other conventional approaches. However, the computational complexity of performing multitarget recursion grows exponentially with the number of targets. The Probability Hypothesis Density (PHD) filter, which only propagates the first moment of the multitarget posterior, requires much less computational complexity. This thesis addresses some of the essential issues related to practical multitarget tracking problems such as tracking target maneuvers, stealthy targets, multitarget tracking in a distributed framework. With maneuvering targets, detecting and tracking the changes in the target motion model also becomes important and an effective solution for this problem using multiple-model based PHD filter is proposed. The proposed filter has the advantage over the other methods in that it can track a timevarying number of targets in nonlinear/ non-Gaussian systems. Recent developments in stealthy military aircraft and cruise missiles have emphasized the need to t rack low SNR targets. The conventional approach of thresholding the measurements throws away potential information and thus results in poor performance in tracking dim targets. The problem becomes even more complicated when multiple dim targets are present in the surveillance region. A PHD filter based recursive track-before-detect approach is proposed in this thesis to track multiple dim targets in a computationally efficient way. This thesis also investigates multiple target tracking using a network of sensors. Generally, sensor networks have limited energy, communication capability and computational power. The crucial consideration is what information needs to be transmitted over the network in order to perform online estimation of the current state of the monitored system, whilst attempting to minimize communication overhead. Finally, a novel continuous approximation approach for nonlinear/ non-Gaussian Bayesian tracking system based on spline interpolation is presented. The resulting filter has the advantages over the widely-known discrete particle based approximation approach in that it does not suffer from degeneracy problems and retains accurate density over the state space. The filter is general enough to be applicable to nonlinear/non-Gaussian system and the density could even be multi-modal.

Thesis

Candidate in Philosophy

Advisors/Committee Members: Kirubarajan, T, Electrical and Computer Engineering.

Subjects/Keywords: Probability Hypothesis Density filter; multitarget tracking; SNR targets; sensors

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APA (6^th Edition):

Punithakumar, K. (2007). Nonlinear Filtering Algorithms for Multitarget Tracking. (Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/16632

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Punithakumar, K. “Nonlinear Filtering Algorithms for Multitarget Tracking.” 2007. Thesis, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/16632.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Punithakumar, K. “Nonlinear Filtering Algorithms for Multitarget Tracking.” 2007. Web. 16 Apr 2021.

Vancouver:

Punithakumar K. Nonlinear Filtering Algorithms for Multitarget Tracking. [Internet] [Thesis]. McMaster University; 2007. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/16632.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Punithakumar K. Nonlinear Filtering Algorithms for Multitarget Tracking. [Thesis]. McMaster University; 2007. Available from: http://hdl.handle.net/11375/16632

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

McMaster University

6. Akselrod , D. Sensor Management and Information Flow Control for Multisensor Multitarget Tracking and Data Fusion.

Degree: PhD, 2008, McMaster University

URL: http://hdl.handle.net/11375/16710

► In this thesis, we address the problem of sensor management with particular application to using unmanned aerial vehicles (U AV s) for multi target… (more)

▼ In this thesis, we address the problem of sensor management with particular application to using unmanned aerial vehicles (U AV s) for multi target tracking. Also, we present a decision based approach for controlling information flow in decentralized multi-target multi-sensor data fusion. Considering the problem of sensor management for multitarget tracking, we study the problem of decision based control of a group of UAVs carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. We propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov Decision Processes (MDPs) based on Dynamic Element Matching (DEM) algorithms. DEM algorithms, widely used for reducing harmonic distortion in Digital-to-Analog converters, are used as a core element in the modified algorithm. We introduce and demonstrate a number of new performance metrics, to verify the effectiveness of an MDP policy, especially useful for quantifying the impact of the modified DEM-based Value Iteration algorithm on an MDP policy. Also, we introduce a multi-level hierarchy of MDPs controlling each of the UAV s. Each level in the hierarchy solves a problem at a different level of abstraction. Simulation results are presented on a representative multisensor-multitarget tracking problem showing a significant improvement in performance compared to the classical algorithm. The proposed method demonstrated robust performance while guaranteeing polynomial computational complexity. Decentralized multisensor-multitarget tracking has numerous advantages over singlesensor or single-platform tracking. In this thesis, we present a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. We present a decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on Markov Decision Processes with introduced hierarchial structure will control the information exchange and data fusion process. The information based objective function is based on the Posterior Cramer-Rao lower bound and constitutes the basis of a reward structure for Markov decision processes which are used, together with decentralized lookup substrate, to control the data fusion process. We analyze three distributed data fusion algorithms - associated measurement fusion, tracklet fusion and… Advisors/Committee Members: Kirubarajan, T., Electrical and Computer Engineering.

Subjects/Keywords: sensor management; unmanned aerial vehicles; multitarget tracking

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APA (6^th Edition):

Akselrod , D. (2008). Sensor Management and Information Flow Control for Multisensor Multitarget Tracking and Data Fusion. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/16710

Chicago Manual of Style (16^th Edition):

Akselrod , D. “Sensor Management and Information Flow Control for Multisensor Multitarget Tracking and Data Fusion.” 2008. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/16710.

MLA Handbook (7^th Edition):

Akselrod , D. “Sensor Management and Information Flow Control for Multisensor Multitarget Tracking and Data Fusion.” 2008. Web. 16 Apr 2021.

Vancouver:

Akselrod D. Sensor Management and Information Flow Control for Multisensor Multitarget Tracking and Data Fusion. [Internet] [Doctoral dissertation]. McMaster University; 2008. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/16710.

Council of Science Editors:

Akselrod D. Sensor Management and Information Flow Control for Multisensor Multitarget Tracking and Data Fusion. [Doctoral Dissertation]. McMaster University; 2008. Available from: http://hdl.handle.net/11375/16710

McMaster University

7. CHEN, XIN. Spatial Clutter Intensity Estimation for Multitarget Tracking.

Degree: PhD, 2012, McMaster University

URL: http://hdl.handle.net/11375/12397

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In this thesis, the problem of estimating the clutter spatial intensity function for the multitarget tracking algorithms has been considered. In many scenarios, after… (more)

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In this thesis, the problem of estimating the clutter spatial intensity function for the multitarget tracking algorithms has been considered. In many scenarios, after the signal detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region as assumed by most tracking algorithms. On the other hand, in order to obtain accurate results, the multitarget tracking algorithm requires information about clutter’s spatial intensity. Thus, non-homogeneous clutter spatial intensity has to be estimated from the measurement set and the tracking filter’s output. Also, in order to take advantage of existing tracking algorithms, it is desirable for the clutter estimation method to be integrated into the tracker itself. In this thesis, the clutter is modeled by a non-homogeneous Poisson point (NHPP) process with a spatial intensity function g(z). To calculate the value of the clutter spatial intensity, all we need to do is estimating g(z). First, two new methods for joint spatial clutter intensity estimation and multitarget tracking using the Probability Hypothesis Density (PHD) Filter are presented. Then, based on NHPP process, multitarget multi-Bernoulli processes and set calculus, the approximated Bayesian method is extended to joint the non–homogeneous clutter background estimation and multitarget tracking with standard multitarget tracking algorithms, like the Multiple Hypothesis Tracking (MHT) and the Joint Integrated Probabilistic Data Association (JIPDA) tracker. Finally, a kernel density method is proposed for the clutter spatial intensity estimation problem. Simulation results illustrate the performance of the above algorithms, both in terms of the false track number and the true track initialization speed. All proposed algorithms show the ability to improve the performance of the multitarget tracker in the presence of slowly time varying non–homogeneous clutter background.

Doctor of Philosophy (PhD)

Advisors/Committee Members: Kirubarajan, Thiagalingam, Electrical and Computer Engineering.

Subjects/Keywords: multitarget tracking; clutter estimation; spatial intensity; point process; Controls and Control Theory; Signal Processing; Controls and Control Theory

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APA (6^th Edition):

CHEN, X. (2012). Spatial Clutter Intensity Estimation for Multitarget Tracking. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/12397

Chicago Manual of Style (16^th Edition):

CHEN, XIN. “Spatial Clutter Intensity Estimation for Multitarget Tracking.” 2012. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/12397.

MLA Handbook (7^th Edition):

CHEN, XIN. “Spatial Clutter Intensity Estimation for Multitarget Tracking.” 2012. Web. 16 Apr 2021.

Vancouver:

CHEN X. Spatial Clutter Intensity Estimation for Multitarget Tracking. [Internet] [Doctoral dissertation]. McMaster University; 2012. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/12397.

Council of Science Editors:

CHEN X. Spatial Clutter Intensity Estimation for Multitarget Tracking. [Doctoral Dissertation]. McMaster University; 2012. Available from: http://hdl.handle.net/11375/12397

McMaster University

8. Sivagnanam, Sutharsan. Joint Multitarget Tracking and Classification Using Aspect-Dependent Measurements.

Degree: PhD, 2009, McMaster University

URL: http://hdl.handle.net/11375/17353

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In this thesis new joint target tracking and classification techniques for aspect-dependent measurements are developed. Joint target tracking and classification methods can result in… (more)

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In this thesis new joint target tracking and classification techniques for aspect-dependent measurements are developed. Joint target tracking and classification methods can result in better tracking and classification performance than those treating these as two separate problems. Significant improvement in state estimation and classification performance can be achieved by exchanging useful information between the tracker and the classifier. Target classification in many target tracking algorithms is not typically done by taking into consideration the target-to-sensor orientation. However, the feature information extracted from the signal that originated from the target is generally a strong function of the target-to-sensor orientation. Since sensor returns are sensitive to this orientation, classification from a single sensor may not give exact target classes. Better classification results can be obtained by fusing feature measurements from multiple views of a target. In multitarget scenarios, handling the classification becomes more challenging due to the identifying the feature information corresponding to a target. That is, it is difficult to identify the origin of measurements. In this case, feature measurement origin ambiguities can be eliminated by integrating the classifier into multiframe data association. This technique reduces the ambiguity in feature measurements while improving track purity. A closed form expression for multiaspect target classification is not feasible. Then, training based statistical modeling can be used to model the unknown feature measurements of a target. In this thesis, the Observable Operator Model (OOM), a better alternative to the Hidden Markov Model (HMM), is used to capture unknown feature distribution of each target and thus can be used as a classifier. The proposed OOM based classification technique incorporates target-to-sensor orientation with a sequence of feature information from multiple sensors. Further, the multi-aspect classifier can be modeled using the OOM to handle unknown target orientation. The target orientation estimation using OOM can also be used to find improved estimates of the states of highly maneuverable targets with noisy kinematic measurements. One limiting factor in obtaining accurate estimates of highly maneuvering target states is the high level of uncertainty in velocity and acceleration components. The target orientation information is helpful in alleviating this problem to accurately determine the velocity and acceleration components. Various simulation studies based on two-dimensional scenarios are presented in this thesis to demonstrate the merits of the proposed joint target tracking and classification algorithms that use aspect-dependent feature measurements.

Thesis

Doctor of Philosophy (PhD)

Advisors/Committee Members: Kirubarajan, T., Electrical and Computer Engineering.

Subjects/Keywords: joint; target; tracking; classification; aspect-dependent; multitarget

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APA (6^th Edition):

Sivagnanam, S. (2009). Joint Multitarget Tracking and Classification Using Aspect-Dependent Measurements. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/17353

Chicago Manual of Style (16^th Edition):

Sivagnanam, Sutharsan. “Joint Multitarget Tracking and Classification Using Aspect-Dependent Measurements.” 2009. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/17353.

MLA Handbook (7^th Edition):

Sivagnanam, Sutharsan. “Joint Multitarget Tracking and Classification Using Aspect-Dependent Measurements.” 2009. Web. 16 Apr 2021.

Vancouver:

Sivagnanam S. Joint Multitarget Tracking and Classification Using Aspect-Dependent Measurements. [Internet] [Doctoral dissertation]. McMaster University; 2009. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/17353.

Council of Science Editors:

Sivagnanam S. Joint Multitarget Tracking and Classification Using Aspect-Dependent Measurements. [Doctoral Dissertation]. McMaster University; 2009. Available from: http://hdl.handle.net/11375/17353

McMaster University

9. Nadarajah, N. Retrodiction for Multitarget Tracking.

Degree: PhD, 2009, McMaster University

URL: http://hdl.handle.net/11375/17364

► Multi-Target Tracking (MTT), where the number of targets as well as their states are time-varying, concerns with the estimation of both the number of… (more)

▼ Multi-Target Tracking (MTT), where the number of targets as well as their states are time-varying, concerns with the estimation of both the number of targets and the individual states from noisy sensor measurements, whose origins are unknown. Filtering typically produces the best estimates of the target state based on all measurements up to current estimation time. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimation of target states. This thesis proposes smoothing methods for various estimation methods that produce delayer, but better, estimates of the target states. First, we propose a novel smoothing method for the Probability Hypothesis Density (PHD) estimator. The PHD filer, which propagates the first order statistical moment of the multitarget state density, a computationally efficient MTT algorithm. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent Sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. The proposed PHD smoothing method involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula. Second, we propose a Multiple Model PH (MMPHD) smoothing method for tracking of maneuvering targets. Multiple model approaches have been shown to be effective for tracking maneuvering targets. MMPHD filter propagates mode-conditioned PHD recursively. The proposed backward MMPHD smoothing algorithm involves the estimation of a continuous state for target dynamic as well as a discrete state vector for the mode of target dynamics. Third, we present a smoothing method for the Gaussian Mixture PHD (GMPHD) state estimator using multiple sensors. Under linear Gaussian assumptions, the PHD filter can be implemented using a closed-form recursion, where the PHD is represented by a mixture of Gaussian functions. This can be extended to nonlinear systems by using the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF). In the case of multisenor systems, a sequential update of the PHD has been suggested in literature. However, this sequential update is susceptible to imperfections in the last sensor. In this thesis, a parallel update for GMPHD filter is proposed. The resulting filter outputs are further improved using a novel closed-form backward smoothing recursion. Finally, we propose a novel smoothing method for Kalman based Interacting Multiple Model (IMM) estimator for tracking agile targets. The new method involves forwarding filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother… Advisors/Committee Members: Kirubarajan, T., Electrical and Computer Engineering.

Subjects/Keywords: electrical and computer engineering; retrodiction; multitarget tracking; smoothing; filtering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Nadarajah, N. (2009). Retrodiction for Multitarget Tracking. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/17364

Chicago Manual of Style (16^th Edition):

Nadarajah, N. “Retrodiction for Multitarget Tracking.” 2009. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/17364.

MLA Handbook (7^th Edition):

Nadarajah, N. “Retrodiction for Multitarget Tracking.” 2009. Web. 16 Apr 2021.

Vancouver:

Nadarajah N. Retrodiction for Multitarget Tracking. [Internet] [Doctoral dissertation]. McMaster University; 2009. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/17364.

Council of Science Editors:

Nadarajah N. Retrodiction for Multitarget Tracking. [Doctoral Dissertation]. McMaster University; 2009. Available from: http://hdl.handle.net/11375/17364

University of Michigan

10. Kreucher, Christopher M. An information-based approach to sensor resource allocation.

Degree: PhD, Electrical engineering, 2005, University of Michigan

URL: http://hdl.handle.net/2027.42/124852

► This work addresses the problem of scheduling the resources of agile sensors. We advocate an information-based approach, where sensor tasking decisions are made based on… (more)

▼ This work addresses the problem of scheduling the resources of agile sensors. We advocate an information-based approach, where sensor tasking decisions are made based on the principle that actions should be chosen to maximize the information expected to be extracted from the scene. This approach provides a single metric able to automatically capture the complex tradeoffs involved when choosing between possible sensor allocations. We apply this principle to the problem of tracking multiple moving ground targets from an airborne sensor. The aim is to task the sensor to most efficiently estimate both the number of targets and the state of each target simultaneously. The state of a target includes kinematic quantities like position and velocity and also discrete variables such as target class and target mode (e.g., turning or stopped). In many experiments presented herein, target motion is taken from real recorded vehicle histories. The information-based approach to sensor management involves the development of three interrelated elements. First, we form the joint multitarget probability density (JMPD), which is the fundamental entity capturing knowledge about the number of targets and the states of the individual targets. Unlike traditional methods, the JMPD does not assume any independence, but instead explicitly models coupling in uncertainty between target states, between targets, and between target state and the number of targets. Furthermore, the JMPD is not assumed to be of some parametric form (e.g., Gaussian). Because of this generality, the JMPD must be estimated using sophisticated numerical techniques. Our representation of the JMPD is via a novel multitarget particle filter with an adaptive sampling scheme. Second, we use the estimate of the JMPD to perform (myopic) sensor resource allocation. The philosophy is to choose actions that are expected to maximize information extracted from the scene. This metric trades automatically between allocations that provide different types of information (e.g., actions that provide information about position versus actions that provide information about target class) without ad hoc assumptions as to the relative utility of each. Finally, we extend the information-based paradigm to non-myopic sensor scheduling. This extension is computationally challenging due to an exponential growth in action sequences with horizon time. We investigate two approximate methods to address this complexity. First, we directly approximate Bellman's equation by replacing the value-to-go function with an easily computed function of the ability to gain information in the future. Second, we apply reinforcement learning as a means of learning a non-myopic policy from a set of example episodes. Advisors/Committee Members: III, Alfred O. Hero, (advisor).

Subjects/Keywords: Approach; Based; Information Theory; Multitarget Tracking; Particle Filtering; Resource Allocation; Sensor

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kreucher, C. M. (2005). An information-based approach to sensor resource allocation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/124852

Chicago Manual of Style (16^th Edition):

Kreucher, Christopher M. “An information-based approach to sensor resource allocation.” 2005. Doctoral Dissertation, University of Michigan. Accessed April 16, 2021. http://hdl.handle.net/2027.42/124852.

MLA Handbook (7^th Edition):

Kreucher, Christopher M. “An information-based approach to sensor resource allocation.” 2005. Web. 16 Apr 2021.

Vancouver:

Kreucher CM. An information-based approach to sensor resource allocation. [Internet] [Doctoral dissertation]. University of Michigan; 2005. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2027.42/124852.

Council of Science Editors:

Kreucher CM. An information-based approach to sensor resource allocation. [Doctoral Dissertation]. University of Michigan; 2005. Available from: http://hdl.handle.net/2027.42/124852

McMaster University

11. Dunne, Darcy. Random Finite Set Methods for Multitarget Tracking.

Degree: DEng, 2013, McMaster University

URL: http://hdl.handle.net/11375/12941

►

Multiple target tracking (MTT) is a major area that occurs in a variety of real world systems. The problem involves the detection and estimation… (more)

Subjects/Keywords: Multiple target tracking; Random Finite Sets; Probability Hypothesis Density; Multitarget Multi-Bernoulli; Intensity filter; Multi-Vehicle Systems and Air Traffic Control; Multi-Vehicle Systems and Air Traffic Control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dunne, D. (2013). Random Finite Set Methods for Multitarget Tracking. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/12941

Chicago Manual of Style (16^th Edition):

Dunne, Darcy. “Random Finite Set Methods for Multitarget Tracking.” 2013. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/12941.

MLA Handbook (7^th Edition):

Dunne, Darcy. “Random Finite Set Methods for Multitarget Tracking.” 2013. Web. 16 Apr 2021.

Vancouver:

Dunne D. Random Finite Set Methods for Multitarget Tracking. [Internet] [Doctoral dissertation]. McMaster University; 2013. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/12941.

Council of Science Editors:

Dunne D. Random Finite Set Methods for Multitarget Tracking. [Doctoral Dissertation]. McMaster University; 2013. Available from: http://hdl.handle.net/11375/12941

12. Hyllengren, Jonas. Clustering for Multi-Target Tracking.

Degree: Automatic Control, 2017, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143807

► This thesis presents a clustering-based approach to decrease the computational cost of data association in multi-target tracking. This is achieved by clustering the sensor… (more)

Subjects/Keywords: Multitarget Tracking MTT Clustering Association; Control Engineering; Reglerteknik

…tracking Non-deterministic, polynomial time (complexity class) Global nearest neighbor… …Joint-probabilistic data association Multi-hypothesis tracking Density-based spatial… …multiple targets. There is a problem of association in this multi-sensor multi-target tracking… …complexity of the data association required in multi-target tracking by clustering the data in… …target tracking feasible in applications where today’s algorithms are too slow. Clustering has…

10 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hyllengren, J. (2017). Clustering for Multi-Target Tracking. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143807

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hyllengren, Jonas. “Clustering for Multi-Target Tracking.” 2017. Thesis, Linköping University. Accessed April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143807.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hyllengren, Jonas. “Clustering for Multi-Target Tracking.” 2017. Web. 16 Apr 2021.

Vancouver:

Hyllengren J. Clustering for Multi-Target Tracking. [Internet] [Thesis]. Linköping University; 2017. [cited 2021 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143807.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hyllengren J. Clustering for Multi-Target Tracking. [Thesis]. Linköping University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143807

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

McMaster University

13. Sithiravel, Rajiv. B-Spline Based Multitarget Tracking.

Degree: PhD, 2014, McMaster University

URL: http://hdl.handle.net/11375/16068

► Multitarget tracking in the presence of false alarm is a difficult problem to consider. The objective of multitarget tracking is to estimate the number of… (more)

▼ Multitarget tracking in the presence of false alarm is a difficult problem to consider. The objective of multitarget tracking is to estimate the number of targets and their states recursively from available observations. At any given time, targets can be born, die and spawn from already existing targets. Sensors can detect these targets with a defined threshold, where normally the observation is influenced by false alarm. Also if the targets are with low signal to noise ratio (SNR) then the targets may not be detected. The Random Finite Set (RFS) filters can be used to solve such multitarget problem efficiently. Specially, one of the best and most widely used RFS based filter is the Probability Hypothesis Density (PHD) filter. The PHD filter approximates the posterior probability density function (PDF) by the first order moment only, where the targets SNR assumed to be much higher. The PHD filter supports targets die, born, spawn and missed-detection by using the well known implementations including Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) and Gaussian Mixture Probability Hypothesis Density (GM-PHD) methods. The SMC-PHD filter suffers from the well known degeneracy problems while GM-PHD filter may not be suitable for nonlinear and non-Gaussian target tracking problems. It is desirable to have a filter that can provide continuous estimates for any distribution. This is the motivation for the use of B-Splines in this thesis. One of the main focus of the thesis is the B-Spline based PHD (SPHD) filters. The Spline is a well developed theory and been used in academia and industry for more than five decades. The B-Spline can represent any numerical, geometrical and statistical functions and models including the PDF and PHD. The SPHD filter can be applied to linear, nonlinear, Gaussian and non-Gaussian multitarget tracking applications. The SPHD continuity can be maintained by selecting splines with order of three or more, which avoids the degeneracy-related problem. Another important characteristic of the SPHD filter is that the SPHD can be locally controlled, which allow the manipulations of the SPHD and its natural tendency for handling the nonlinear problems. The SPHD filter can be further extended to support maneuvering multitarget tracking, where it can be an alternative to any available PHD filter implementations. The PHD filter does not work well for very low observable (VLO) target tracking problems, where the targets SNR is normally very low. For very low SNR scenarios the PDF must be approximated by higher order moments. Therefore the PHD implementations may not be suitable for the problem considered in this thesis. One of the best estimator to use in VLO target tracking problem is the Maximum-Likelihood Probability Data Association (ML-PDA) algorithm. The standard ML-PDA algorithm is widely used in single target initialization or geolocation problems with high false alarm. The B-Spline is also used in the ML-PDA (SML-PDA) implementations. The SML-PDA algorithm has the capability… Advisors/Committee Members: Thiagalingam, Kirubarajan, Electrical and Computer Engineering.

Subjects/Keywords: Multitarget tracking, Nonlinear filtering, Probability Hypothesis Density filter, Splines; Maneuvering target tracking, Maximum-Likelihood-Probabilistic Data Association Algorithm, Track initialization, Low observable target tracking, Joint Maximum-Likelihood-Probabilistic Data Association Algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sithiravel, R. (2014). B-Spline Based Multitarget Tracking. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/16068

Chicago Manual of Style (16^th Edition):

Sithiravel, Rajiv. “B-Spline Based Multitarget Tracking.” 2014. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/16068.

MLA Handbook (7^th Edition):

Sithiravel, Rajiv. “B-Spline Based Multitarget Tracking.” 2014. Web. 16 Apr 2021.

Vancouver:

Sithiravel R. B-Spline Based Multitarget Tracking. [Internet] [Doctoral dissertation]. McMaster University; 2014. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/16068.

Council of Science Editors:

Sithiravel R. B-Spline Based Multitarget Tracking. [Doctoral Dissertation]. McMaster University; 2014. Available from: http://hdl.handle.net/11375/16068

Missouri University of Science and Technology

14. McCabe, James Samuel. Multitarget tracking and terrain-aided navigation using square-root consider filters.

Degree: PhD, Aerospace Engineering, Missouri University of Science and Technology

URL: https://scholarsmine.mst.edu/doctoral_dissertations/2726

► "Filtering is a term used to describe methods that estimate the values of partially observed states, such as the position, velocity, and attitude of… (more)

▼ "Filtering is a term used to describe methods that estimate the values of partially observed states, such as the position, velocity, and attitude of a vehicle, using current observations that are corrupted due to various sources, such as measurement noise, transmission dropouts, and spurious information. The study of filtering has been an active focus of research for decades, and the resulting filters have been the cornerstone of many of humankind's greatest technological achievements. However, these achievements are enabled principally by the use of specialized techniques that seek to, in some way, combat the negative impacts that processor roundoff and truncation error have on filtering. Two of these specialized techniques are known as square-root filters and consider filters. The former alleviates the fragility induced from estimating error covariance matrices by, instead, managing a factorized representation of that matrix, known as a square-root factor. The latter chooses to account for the statistical impacts a troublesome system parameter has on the overall state estimate without directly estimating it, and the result is a substantial reduction in numerical sensitivity to errors in that parameter. While both of these techniques have found widespread use in practical application, they have never been unified in a common square-root consider framework. Furthermore, consider filters are historically rooted to standard, vector-valued estimation techniques, and they have yet to be generalized to the emerging, set-valued estimation tools for multitarget tracking. In this dissertation, formulae for the square-root consider filter are derived, and the result is extended to finite set statistics-based multitarget tracking tools. These results are used to propose a terrain-aided navigation concept wherein data regarding a vehicle's environment is used to improve its state estimate, and square-root consider techniques provide the numerical stability necessary for an onboard navigation application. The newly developed square-root consider techniques are shown to be much more stable than standard formulations, and the terrain-aided navigation concept is applied to a lunar landing scenario to illustrate its applicability to navigating in challenging environments" – Abstract, page iii.

Subjects/Keywords: Consider filtering; Estimation; Multitarget tracking; Navigation; Square-root filtering; Terrain relative navigation; Aerospace Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

McCabe, J. S. (n.d.). Multitarget tracking and terrain-aided navigation using square-root consider filters. (Doctoral Dissertation). Missouri University of Science and Technology. Retrieved from https://scholarsmine.mst.edu/doctoral_dissertations/2726

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16^th Edition):

McCabe, James Samuel. “Multitarget tracking and terrain-aided navigation using square-root consider filters.” Doctoral Dissertation, Missouri University of Science and Technology. Accessed April 16, 2021. https://scholarsmine.mst.edu/doctoral_dissertations/2726.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7^th Edition):

McCabe, James Samuel. “Multitarget tracking and terrain-aided navigation using square-root consider filters.” Web. 16 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

McCabe JS. Multitarget tracking and terrain-aided navigation using square-root consider filters. [Internet] [Doctoral dissertation]. Missouri University of Science and Technology; [cited 2021 Apr 16]. Available from: https://scholarsmine.mst.edu/doctoral_dissertations/2726.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

McCabe JS. Multitarget tracking and terrain-aided navigation using square-root consider filters. [Doctoral Dissertation]. Missouri University of Science and Technology; Available from: https://scholarsmine.mst.edu/doctoral_dissertations/2726

Note: this citation may be lacking information needed for this citation format:
No year of publication.

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Open Access Theses and Dissertations