This thesis investigates the detection and classification of small boats using a passive sonar system. Noise radiated from a small boats consists of broadband noise and harmonically related tones that correspond to parameters in the boats engine and propeller. A novel signal processing method for detection and discrimination of noise radiated from small boats has been developed. There are two main components to the algorithm. The first component detects the presence of small boats by the harmonic tonals radiated from the boat propeller and engine. The second component was designed to extract the a signature from passive sonar data. The Harmonic Extraction and Analysis Tool (HEAT) was designed to estimate the fundamental frequency of the harmonic tones, track the fundamental frequency using a Kalman filter, and automatically extract the amplitudes of the harmonic tonals to generate a harmonic signature for the boat. The algorithm is shown to accurately extract theses signatures, and results show that the signatures are unique enough that the same boat passing by the hydrophone multiple times can be recognized.

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 (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.

This thesis contributes new algorithms and implementations for particle filter-based target tracking. From an algorithmic perspective, modifications that improve a batch-based acoustic direction-of-arrival (DOA), multi-target, particle filter tracker are presented. The main improvements are reduced execution time and increased robustness to target maneuvers. The key feature of the batch-based tracker is an image template-matching approach that handles data association and clutter in measurements. The particle filter tracker is compared to an extended Kalman filter~(EKF) and a Laplacian filter and is shown to perform better for maneuvering targets. Using an approach similar to the acoustic tracker, a radar range-only tracker is also developed. This includes developing the state update and observation models, and proving observability for a batch of range measurements. From an implementation perspective, this thesis provides new low-power and real-time implementations for particle filters. First, to achieve a very low-power implementation, two mixed-mode implementation strategies that use analog and digital components are developed. The mixed-mode implementations use analog, multiple-input translinear element (MITE) networks to realize nonlinear functions. The power dissipated in the mixed-mode implementation of a particle filter-based, bearings-only tracker is compared to a digital implementation that uses the CORDIC algorithm to realize the nonlinear functions. The mixed-mode method that uses predominantly analog components is shown to provide a factor of twenty improvement in power savings compared to a digital implementation. Next, real-time implementation strategies for the batch-based acoustic DOA tracker are developed. The characteristics of the digital implementation of the tracker are quantified using digital signal processor (DSP) and field-programmable gate array (FPGA) implementations. The FPGA implementation uses a soft-core or hard-core processor to implement the Newton search in the particle proposal stage. A MITE implementation of the nonlinear DOA update function in the tracker is also presented.