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Author
Title Automated Optimization of Multisensor - Multitarget Trackers
URL
Publication Date
Date Available
Degree MASc
Degree Level masters
University/Publisher McMaster University
Abstract

Almost every module or project needs to be optimized to get the best results and reduce costs. Multi-sensor, multi-input trackers require a huge number of parameters to run, which have an undefined or unknown to the output of the tracker. It becomes very difficult to manually initialize these parameters to get a good output and there was a need to automate the process of selecting the parameters, validating them and initialing the tracker. The optimizer built to cater for these issues uses heuristic genetic algorithms – Particle Swarm Optimization and Gravitational Search Algorithm to find the best solutions for the problem. The optimizer works with the help of a Parameter Evaluator (developed earlier) to study the output of the tracker and incorporate the multi objective (Pareto) aspect of the problem. The Optimizer can find solutions to any optimization problem if hooked to a corresponding evaluator or fitness function calculator. This feature makes the Optimizer not just another module to the tracker but an independent application that could be used for general purpose optimization solutions.

Thesis

Master of Applied Science (MASc)

Subjects/Keywords OPTIMIZATION
Contributors Kirubarajan, Thia; Electrical and Computer Engineering
Language en
Country of Publication ca
Record ID handle:11375/18364
Repository mcmaster
Date Indexed 2019-01-09

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