Bibliography: p. 231-232. / xvi, 232 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The aim of this thesis is detecting and tracking objects at large ranges, when no target features are visible, with imaging type sensors. A system which estimates the optical flow of the scene in a parallel architecture is developed. The architecture is similar to that of an artifical neural network. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994

The probability hypothesis density (PHD), popularized by Ronald Mahler, presents a novel and theoretically-rigorous approach to multitarget, multisensor tracking. Based on random set theory, the PHD is the first moment of a point process of a random track set, and it can be propagated by Bayesian prediction and observation equations to form a multitarget, multisensor tracking filter. The advantage of the PHD filter lies in its ability to estimate automatically the expected number of targets present, to fuse easily different kinds of data observations, and to locate targets without performing any explicit report-to-track association. We apply a particle-filter implementation of the PHD filter to realistic multitarget, multisensor tracking using passive coherent location (PCL) systems that exploit illuminators of opportunity such as FM radio stations. The objective of this dissertation is to enhance the usefulness of the PHD particle filter for multitarget, multisensor tracking, in general, and within the context of PCL, in particular. This involves a number of thrusts, including: 1) devising intelligent proposal densities for particle placement, 2) devising a peak-extraction algorithm for extracting information from the PHD, 3) incorporating realistic probabilities of detection and signal-to-noise ratios (including multipath effects) to model realistic PCL scenarios, 4) using range, Doppler, and direction of arrival (DOA) observations to test the target detection and data fusion capabilities of the PHD filter, and 5) clarifying the concepts behind FISST and the PHD to make them more accessible to the practicing engineer. A goal of this dissertation is to serve as a tutorial for anyone interested in becoming familiar with the probability hypothesis density and associated PHD particle filter. It is hoped that, after reading this thesis, the reader will have gained a clearer understanding of the PHD and the functionality and effectiveness of the PHD particle filter.

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

The purpose of this report is to investigate the processing of tracking data for acoustic targets. The programs developed for two- and three- dimensional space calculate the target's position via "hyperbolic-fix" navigation (geometric) considerations using the Newton-Raphson algorithm. The computer programs and the tracking solution approach contained herein is based on knowledge of only the sensors' locations and the relative time-difference at which a target's referenced, singular, acoustic pressure wavefronts are received at the sensors. Omnidirectional sensors are found to be sufficient for the two-dimensional space tracking problem. However, it is found that the three-space problem required usage of directional frequency and ranging (DIFAR) sensors. Line printer plots are provided for the target position solutions; also; tabular track position solutions are provided.

In this paper we address radar-communication coexistence by modelling the radar environment as a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to successfully avoid interfering with the communication system to improve its performance. We also show that the DQN method outperforms previous methods in terms of memory and handling new situations. In this thesis we also address the application of the MDP into a software defined radio (SDR) USRP X310 by utilizing the software LabVIEW to communicate with and control the SDR. / Master of Science / In this thesis we develop methods for creating and implementing algorithms for a cognitive radar. A cognitive radar is a radar that is able to sense its environment and avoid any other communication system that may interfere with its operation. We discuss the predictive methods we used to sense and avoid the other communication systems as well as how we implemented this using a software defined radar based on the USRP X310.

Thesis (MScEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: This work entails the development and performance analysis of a human gait detection system based on radar micro-Doppler signals. The system consists of a tracking functionality and a target classifier. Target micro-Doppler signatures are extracted with Short-Time Fourier Transform (STFT) based spectrogram providing a high-resolution signatures with the radar that is used. A feature extraction mechanism is developed to extract six features from the signature and an artificial neural network (A-NN) based classifier is designed to carry out the classification process. The system is tested on real X-band radar data of human subjects performing six activities. Those activities are walking and speed walking, walking with hands in pockets, marching, running, walking with a weapon, and walking with arms swaying. The multiclass classifier was designed to discriminate between those activities. High classification accuracy of 96% is demonstrated. / AFRIKAANSE OPSOMMING: Hierdie werk behels die ontwikkeling, en analise van werksverrigting, van ’n menslike stapdetekor gebaseer op radar-mikrodoppleranalise. Die stelsel bestaan uit ’n teikenvolger en -klassifiseerder. Die mikrodoppler-kenmerke van ’n teiken word met behulp van die korttyd-Fourier-transform onttrek, en verskaf hoe-resolusie-kenmerke met die radar wat vir die implementering gebruik word. ’n Kenmerkontrekkingstelsel is ontwikkel om ses kenmerke vanuit die spektrogram te onttrek, en ’n kunsmatige neurale netwerk word as klassifiseerder gebruik. Die stelsel is met ’n X-band radar op werklike menslike beweging getoets, terwyl vrywilligers ses aktiwiteite uitgevoer het: loop, loop (hand in die sakke), marsjeer, hardloop, loop met ’n wapen, loop met arms wat swaai. Die multiklas-klassifiseerder is ontwerp om tussen hierdie aktiwiteite te onderskei. ’n Hoe klassifiseringsakkuraatheid van 96% word gedemonstreer.

Thesis (MSc (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2006. / In this work we present a novel method to do measurement correlation between target observations made by two ormore radar systems. Some of the most common radar sensors available are those measuring only range (distance to the target) and bearing (azimuth angle). We use these measurements to determine the correlation between two di¤erent sensors observing the same target. As a by-product of the correlation algorithm, we nd a way to estimate the target height for a target observed by at least two radar sensors. The correlation method is expounded upon, where we discuss measurement correlation for moving targets. Targets are tracked using a Kalman Filter, and correlation is done between new observations and existing target tracks. Finally, the correlation algorithm is implemented in an interactive 3D computer simulation. Results obtained indicate a high success rate, with false correlations only obtained where sensor accuracy is the limiting factor.

In this study, an expert system based EA and tracking system is developed and the performances of these systems are optimized. Tracking system consists of a monopulse tracking radar and a Multiple Hypothesis Tracking (MHT) algorithm. MHT is modelled as a measurement&amp / #8208 / oriented approach, which is capable of initiating tracks. As each measurement is received, probabilities are calculated for the hypotheses and target states are estimated using a Kalman filter. Range Gate Pull-Off (RGPO) is selected as an EA technique to be developed because it is accepted to be the primary deception technique employed against tracking radar. Two modes of RGPO technique / linear and parabolic, according to time delay controller are modelled. Genetic Algorithm (GA) Toolbox of MATLAB is used for the optimization of these systems over some predetermined scenarios. It is observed that the performance of the tracking radar system is improved significantly and successful tracking is achieved over all given scenarios, even for closely spaced targets. RGPO models are developed against this improved tracking performance and deception of tracking radar is succeeded for all given target models.

An analysis and simulation tool is developed for optimizing system parameters of the monopulse target tracking radar and observing effects of the system parameters on the performance of the system over different scenarios. A monopulse tracking radar is modeled for measuring the performance of the radar with given parameters, during the thesis studies. The radar model simulates the operation of a Class IA type monopulse automatic tracking radar, which uses a planar phased array. The interacting multiple model (IMM) estimator with the Probabilistic Data Association (PDA) technique is used as the tracking filter. In addition to modeling of the tracking radar model, an optimization tool is developed to optimize system parameters of this tracking radar model. The optimization tool implements a Genetic Algorithm (GA) belonging to a GA Toolbox distributed by Department of Automatic Control and System Engineering at University of Sheffield. The thesis presents optimization results over some given optimization scenarios and concludes on effect of tracking filter parameters, beamwidth and dwell interval for the confirmed track.

Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2005. / Tracking manoeuvring military airborne targets with radar is problematic due to the low scan rates and the high levels of measurement noise. Surveillance systems using multiple radars have the benefit of an increased rate of observation and noise reduction but also have the problem of correlating observations from multiple sensors. Mehtods are discussed to correlate single observations from multiple radar sensors as well as assigning observations to existing tracks. Filtering methods to reduce measurement noise of the target tracks and methods to extrapolate the predicted position of targets are also explored.