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

Spawn Model Derivations for Multi-object Orbit Determination within a Random Finite Set Framework

Fragmentation events and large scale small-sat deployments are a significant threat; because of the sudden creation of many new objects posing potential risks to existing satellites, and the fact that current surveillance systems require laborious human intervention to identify and catalog these new objects. In the tracking community, the spontaneous appearance of new objects is referred to as birth, whereas spawning refers to the appearance of new objects generated by previously existing ones, such as a fragmentation event or small-sat deployment. In this dissertation, two-well known random finite set (RFS) filters are extended via mathematical derivation, aimed at performing initial orbit determination (IOD) of objects generated by spawning events. A Zero-Inflated Poisson (ZIP) spawn model is presented and a predicted cardinality expression for general spawn model configuration, capable of implementation via Partial Bell Polynomials, is derived for the Cardinalized Probability Hypothesis Density (CPHD) filter using a measure-theoretic approach. Generalized Labeled Multi-Bernoulli (GLMB) filter developments achieve a closed-form solution to the multi-object Bayes recursion capable of jointly estimating a spawned object's state and ancestry. Linear simulations demonstrate fundamental filter developments; the ZIP spawn model is shown to outperform other conventional models with the CPHD filter and multiple generations of spawn object ancestry are accurately estimated with the GLMB filter. Finally, non-linear simulations specific to Space Situational Awareness (SSA) IOD demonstrate the filters' efficacy, which include: fragmentation event and small-sat deployment scenarios, homogeneous and heterogeneous radar network observations, and spawning events that occur in and out of sensor field of view. This research shows that on-line multi-object IOD in the presence of spawning is possible within the RFS paradigm, and establishes a foundation upon which further SSA improvements can be investigated

Poisson Multi-Bernoulli Mixtures for Multiple Object Tracking

Multi-object tracking (MOT) refers to the process of estimating object trajectories of interest based on sequences of noisy sensor measurements obtained from multiple sources. Nowadays, MOT has found applications in numerous areas, including, e.g., air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in automated vehicles. This thesis studies Poisson multi-Bernoulli mixture (PMBM) conjugate priors for MOT. Finite Set Statistics provides an elegant Bayesian formulation of MOT based on random finite sets (RFSs), and a significant trend in RFSs-based MOT is the development of conjugate distributions in Bayesian probability theory, such as the PMBM distributions. Multi-object conjugate priors are of great interest as they provide families of distributions that are suitable to work with when seeking accurate approximations to the true posterior distributions. Many RFS-based MOT approaches are only concerned with multi-object filtering without attempting to estimate object trajectories. An appealing approach to building trajectories is by computing the multi-object densities on sets of trajectories. This leads to the development of many multi-object filters based on sets of trajectories, e.g., the trajectory PMBM filters. In this thesis, [Paper A] and [Paper B] consider the problem of point object tracking where an object generates at most one measurement per time scan. In [Paper A], a multi-scan implementation of trajectory PMBM filters via dual decomposition is presented. In [Paper B], a multi-trajectory particle smoother using backward simulation is presented for computing the multi-object posterior for sets of trajectories using a sequence of multi-object filtering densities and a multi-object dynamic model. [Paper C] and [Paper D] consider the problem of extended object tracking where an object may generate multiple measurements per time scan. In [Paper C], an extended object Poisson multi-Bernoulli (PMB) filter is presented, where the PMBM posterior density after the update step is approximated as a PMB. In [Paper D], a trajectory PMB filter for extended object tracking using belief propagation is presented, where the efficient PMB approximation is enabled by leveraging the PMBM conjugacy and the factor graph formulation

Methods for Online UAV Path Planning for Tracking Multiple Objects

Unmanned aerial vehicles (UAVs) or drones have rapidly evolved to enable carrying various sensors such as thermal sensors for vision or antennas for radio waves. Therefore, drones can be transformative for applications such as surveillance and monitoring because they have the capability to greatly reduce the time and cost associated with traditional tasking methods. Realising this potential necessitates equipping UAVs with the ability to perform missions autonomously. This dissertation considers the problems of online path planning for UAVs for the fundamental task of surveillance comprising of tracking and discovering multiple mobile objects in a scene. Tracking and discovering an unknown and time-varying number of objects is a challenging problem in itself. Objects such as people or wildlife tend to switch between various modes of movements. Measurements received by the UAV’s on-board sensors are often very noisy. In practice, the on-board sensors have a limited field of view (FoV), hence, the UAV needs to move within range of the mobile objects that are scattered throughout a scene. This is extremely challenging because neither the exact number nor locations of the objects of interest are available to the UAV. Planning the path for UAVs to effectively detect and track multi-objects in such environments poses additional challenges. Path planning techniques for tracking a single object are not applicable. Since there are multiple moving objects appearing and disappearing in the region, following only certain objects to localise them accurately implies that a UAV is likely to miss many other objects. Furthermore, online path planning for multi-UAVs remains challenging due to the exponential complexity of multi-agent coordination problems. In this dissertation, we consider the problem of online path planning for UAV-based localisation and tracking of multi-objects. First, we realised a low cost on-board radio receiver system on aUAV and demonstrated the capability of the drone-based platform for autonomously tracking and locating multiple mobile radio-tagged objects in field trials. Second, we devised a track-before-detect filter coupled with an online path planning algorithm for joint detection and tracking of radio-tagged objects to achieve better performance in noisy environments. Third, we developed a multi-objective planning algorithm for multi-agents to track and search multi-objects under the practical constraint of detection range limited on-board sensors (or FoV limited sensors). Our formulation leads to a multi-objective value function that is a monotone submodular set function. Consequently, it allows us to employ a greedy algorithm for effectively controlling multi-agents with a performance guarantee for tracking discovered objects while searching for undiscovered mobile objects under practical constraints of limited FoV sensors. Fourth, we devised a fast distributed tracking algorithm that can effectively track multi-objects for a network of stationary agents with different FoVs. This is the first such solution to this problem. The proposed method can significantly improve capabilities of a network of agents to track a large number of objects moving in and out of the limited FoV of the agents’ sensors compared to existing methods that do not consider the problem of unknown and limited FoV of sensors.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202