3D Localization and Tracking Methods for Multi-Platform Radar Networks

Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario

Novel methods for multi-target tracking with applications in sensor registration and fusion

Maintaining surveillance over vast volumes of space is an increasingly important capability for the defence industry. A clearer and more accurate picture of a surveillance region could be obtained through sensor fusion between a network of sensors. However, this accurate picture is dependent on the sensor registration being resolved. Any inaccuracies in sensor location or orientation can manifest themselves into the sensor measurements that are used in the fusion process, and lead to poor target tracking performance. Solutions previously proposed in the literature for the sensor registration problem have been based on a number of assumptions that do not always hold in practice, such as having a synchronous network and having small, static registration errors. This thesis will propose a number of solutions to resolving the sensor registration and sensor fusion problems jointly in an efficient manner. The assumptions made in previous works will be loosened or removed, making the solutions more applicable to problems that we are likely to see in practice. The proposed methods will be applied to both simulated data, and a segment of data taken from a live trial in the field

Multiple-Object Estimation Techniques for Challenging Scenarios

A series of methods for solving the multi-object estimation problem in the context sequential Bayesian inference is presented. These methods concentrate on dealing with challenging scenarios of multiple target tracking, involving fundamental problems of nonlinearity and non-Gaussianity of processes, high state dimensionality, high number of targets, statistical dependence between target states, and degenerate cases of low signal-to-noise ratio, high uncertainty, lowly observable states or uninformative observations. These difficulties pose obstacles to most practical multi-object inference problems, lying at the heart of the shortcomings reported for state-of-the-art methods, and so elicit novel treatments to enable tackling a broader class of real problems. The novel algorithms offered as solutions in this dissertation address such challenges by acting on the root causes of the associated problems. Often this involves essential dilemmas commonly manifested in Statistics and Decision Theory, such as trading off estimation accuracy with algorithm complexity, soft versus hard decision, generality versus tractability, conciseness versus interpretativeness etc. All proposed algorithms constitute stochastic filters, each of which is formulated to address specific aspects of the challenges at hand while offering tools to achieve judicious compromises in the aforementioned dilemmas. Two of the filters address the weight degeneracy observed in sequential Monte Carlo filters, particularly for nonlinear processes. One of these filters is designed for nonlinear non-Gaussian high-dimensional problems, delivering representativeness of the uncertainty in high-dimensional states while mitigating part of the inaccuracies that arise from the curse of dimensionality. This filter is shown to cope well with scenarios of multimodality, high state uncertainty, uninformative observations and high number of false alarms. A multi-object filter deals with the problem of considering dependencies between target states in a way that is scalable to a large number of targets, by resorting to probabilistic graphical structures. Another multi-object filter treats the problem of reducing the computational complexity of a state-of-the-art cardinalized filter to deal with a large number of targets, without compromising accuracy significantly. Finally, a framework for associating measurements across observation sessions for scenarios of low state observability is proposed, with application to an important Space Surveillance task: cataloging of space debris in the geosynchronous/geostationary belt. The devised methods treat the considered challenges by bringing about rather general questions, and provide not only principled solutions but also analyzes the essence of the investigated problems, extrapolating the implemented techniques to a wider spectrum of similar problems in Signal Processing

Trajectory Poisson multi-Bernoulli filters

This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion. After the update step, the posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a Kullback-Leibler divergence minimisation on an augmented space is performed. The developed filters are computationally lighter alternatives to the trajectory PMBM filters, which provide the closed-form recursion for sets of trajectories with Poisson birth model, and are shown to outperform previous multi-target tracking algorithms