Design of multiple frequency continuous wave radar hardware and micro-Doppler based detection and classification algorithms

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Title: Design of multiple frequency continuous wave radar hardware and micro-Doppler based detection and classification algorithms
Author: Anderson, Michael Glen, 1979-
Abstract: Micro -Doppler is defined as scattering produced by non -rigid -body motion . This dissertation involves the design of a multiple frequency continuous wave (MFCW ) radar for micro -Doppler research and detection and classification algorithm design . First , sensor hardware is developed and tested . Various design tradeoffs are considered , with the application of micro -Doppler based detection and classification in mind . A diverse database of MFCW radar micro -Doppler signatures was collected for this dissertation . The micro -Doppler signature database includes experimental data from human , vehicle , and animal targets . Signatures are acquired from targets with varying ranges , velocities , approach angles , and postures . The database is analyzed for micro -Doppler content with a focus on its application to target classification . Joint time -frequency detection algorithms are developed to improve detection performance by exploiting noise -spreading and the micro -Doppler phenomenon . Following detection algorithm development , this dissertation covers the design of micro - Doppler feature extraction , feature selection , and classification algorithms . Feature selection is performed automatically via a Fisher score initialized sequential backward selection algorithm . Classification is performed using two distinct approaches : a generative statistical classification algorithm based on Gaussian mixture models (GMMs ) and a discriminative statistical classification algorithm based on support vector machines (SVMs ) . Classifier performance is analyzed in detail on a micro -Doppler signature database acquired over a three -year period . Both the SVM and GMM classifiers perform well on the radar target classification task (high accuracy , low nuisance alarm probability , high F -measure , etc . ) . The performance of both classifiers is remarkably similar , and neither algorithm dominates the other in any performance metric when using the chosen feature set . (However , the difference between SVM and GMM classification accuracy becomes statistically significant when many redundant features are present in the feature set . ) The accuracy of both classifiers is shown to vary as a function of approach angle , which physically corresponds to the angular dependence of micro -Doppler . The results suggest that overall classifier performance is more sensitive to feature selection than classifier selection (with GMM being more sensitive to redundant features than SVM ) . Both classifiers are robust enough to handle human targets attempting to evade detection by either army crawling or hands -and -knees crawling .
URI: http : / /hdl .handle .net /2152 /4000
Date: 2008-08-29

Citation

Design of multiple frequency continuous wave radar hardware and micro-Doppler based detection and classification algorithms. Doctoral dissertation, The University of Texas at Austin. Available electronically from http : / /hdl .handle .net /2152 /4000 .

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