Estimation and control of multi-object systems with high-fidenlity sensor models: A labelled random finite set approach

Principled and novel multi-object tracking algorithms are proposed, that have the ability to optimally process realistic sensor data, by accommodating complex observational phenomena such as merged measurements and extended targets. Additionally, a sensor control scheme based on a tractable, information theoretic objective is proposed, the goal of which is to optimise tracking performance in multi-object scenarios. The concept of labelled random finite sets is adopted in the development of these new techniques

Sensor Control for Multi-Object Tracking Using Labeled Multi-Bernoulli Filter

The recently developed labeled multi-Bernoulli (LMB) filter uses better approximations in its update step, compared to the unlabeled multi-Bernoulli filters, and more importantly, it provides us with not only the estimates for the number of targets and their states, but also with labels for existing tracks. This paper presents a novel sensor-control method to be used for optimal multi-target tracking within the LMB filter. The proposed method uses a task-driven cost function in which both the state estimation errors and cardinality estimation errors are taken into consideration. Simulation results demonstrate that the proposed method can successfully guide a mobile sensor in a challenging multi-target tracking scenario

Sensor management for multi-target tracking via multi-bernoulli filtering

In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli filter is used in conjunction with two different control objectives: maximizing the expected Rényi divergence between the predicted and updated densities, and minimizing the expected posterior cardinality variance. Numerical studies are presented in two scenarios where a mobile sensor tracks five moving targets with different levels of observability

Optimal Bernoulli point estimation with applications

This paper develops optimal procedures for point estimation with Bernoulli filters. These filters are of interest to radar and sonar surveillance because they are designed for stochastic targets that can enter and exit the surveillance region at random instances. Because of this property they are not served by the minimum mean square estimator, which is the most widely used approach to optimal point estimation. Instead of the squared error loss, this paper proposes an application-oriented loss function that is compatible with Bernoulli filters, and it develops two significant practical estimators: the minimum probability of error estimate (which is based on the rule of ideal observer), and the minimum mean operational loss estimate (which models a simple defence scenario)

Multi-Robot Active Information Gathering Using Random Finite Sets

Many tasks in the modern world involve collecting information, such as infrastructure inspection, security and surveillance, environmental monitoring, and search and rescue. All of these tasks involve searching an environment to detect, localize, and track objects of interest, such as damage to roadways, suspicious packages, plant species, or victims of a natural disaster. In any of these tasks the number of objects of interest is often not known at the onset of exploration. Teams of robots can automate these often dull, dirty, or dangerous tasks to decrease costs and improve speed and safety. This dissertation addresses the problem of automating data collection processes, so that a team of mobile sensor platforms is able to explore an environment to determine the number of objects of interest and their locations. In real-world scenarios, robots may fail to detect objects within the field of view, receive false positive measurements to clutter objects, and be unable to disambiguate true objects. This makes data association, i.e., matching individual measurements to targets, difficult. To account for this, we utilize filtering algorithms based on random finite sets to simultaneously estimate the number of objects and their locations within the environment without the need to explicitly consider data association. Using the resulting estimates they receive, robots choose actions that maximize the mutual information between the set of targets and the binary events of receiving no detections. This effectively hedges against uninformative actions and leads to a closed form equation to compute mutual information, allowing the robot team to plan over a long time horizon. The robots either communicate with a central agent, which performs the estimation and control computations, or act in a decentralized manner. Our extensive hardware and simulated experiments validate the unified estimation and control framework, using robots with a wide variety of mobility and sensing capabilities to showcase the broad applicability of the framework

Sensor management for multi-target tracking using random finite sets

Sensor management in multi-target tracking is commonly focused on actively scheduling and managing sensor resources to maximize the visibility of states of a set of maneuvering targets in a surveillance area. This project focuses on two types of sensor management techniques: - controlling a set of mobile sensors (sensor control), and - scheduling the resources of a sensor network (sensor selection).​ In both cases, agile sensors are employed to track an unknown number of targets. We advocate a Random Finite Set (RFS)-based approach for formulation of a sensor control/selection technique for multi-target tracking problem. Sensor control/scheduling offers a multi-target state estimate that is expected to be substantially more accurate than the classical tracking methods without sensor management. Searching for optimal sensor state or command in the relevant space is carried out by a decision-making mechanism based on maximizing the utility of receiving measurements.​ In current solutions of sensor management problem, the information of the clutter rate and uncertainty in sensor Field of View (FoV) are assumed to be known in priori. However, accurate measures of these parameters are usually not available in practical situations. This project presents a new sensor management solution that is designed to work within a RFS-based multi-target tracking framework. Our solution does not require any prior knowledge of the clutter distribution nor the probability of detection profile to achieve similar accuracy. Also, we present a new sensor management method for multi-object filtering via maximizing the state estimation confidence. Confidence of an estimation is quantified by measuring the dispersion of the multi-object posterior about its statistical mean using Optimal Sub-Pattern Assignment (OSPA). The proposed method is generic and the presented algorithm can be used with any statistical filter

Doppler-only target tracking for a multistatic radar exploiting FM band illuminators of opportunity

Includes bibliographical referencesCommensal Radar (CR), defined as a subclass of Passive Radar (PR), is a receive only radar that exploits non-cooperative illuminators of opportunity for target detection, location and subsequent tracking. The objective of this thesis is to evaluate the feasibility of using a Frequency Modulation (FM) Broadcast band CR system as a cost effective solution for Air Traffic Control (ATC). An inherent complication by exploiting FM is the low range resolution due to the low bandwidth of FM radio signals. However, due to typical long integration times associated with CR, the frequency domain resolution is typically very good. As a result, measurements of the target's Doppler shift are highly accurate and could potentially make FM illuminators a viable source for ATC purposes. Accordingly, this thesis aims to obtain a comprehensive understanding of using high resolution Doppler measurements to accurately track the position of a target. This objective have been addressed b by performing a comprehensive mathematical analysis for a Doppler only tracking CR system. The analysis is verified with a tracking simulation, in which the Recursive Gauss Newton Filter (RGNF) is used and lastly, a field experiment was conducted to produce tracking results based on real measurement data. Results demonstrated that Doppler only target tracking from real measurement data is possible, even when the initial target state vector is initialised from real measurement data. A good degree of correlation is achieved between the theoretical, simulated and measured results, hence verifying the theoretical findings of this thesis. Ensuring that the observation matrix is properly conditioned in Doppler only tracking applications is important, as failure to do so results in tracking instability. Factors that influence the conditioning of the observation matrix are; the number of receivers used (assuming the basic observation criteria is met) and the placement of the receivers, keeping in mind the possibility of Doppler correlation in the measurements. The possibility of improving an ill-conditioned observation matrix is also demonstrated. In general, tracking filters, for example the RGNF, typically employ time history information and therefore, a direct comparison to the Cramer Rao Lower Bound (CRLB) is unrealistic and accordingly a new theoretical lower bound, called the Cumulative CRLB was derived that does account for time history measurements. Although the best results for this thesis are achieved by using long integration periods (4 s), the effect of Doppler walk was not compensated for and is an aspect that requires further investigation to potentially further improve on the results obtained in this thesis. As a final conclusion for this thesis; the Doppler only target tracking delivered some encouraging results, however a qualification test in the form of an extensive trial period is next required to motivate Doppler only tracking for ATC purposes