A track-before-detect labelled multi-Bernoulli particle filter with label switching

This paper presents a multitarget tracking particle filter (PF) for general track-before-detect measurement models. The PF is presented in the random finite set framework and uses a labelled multi-Bernoulli approximation. We also present a label switching improvement algorithm based on Markov chain Monte Carlo that is expected to increase filter performance if targets get in close proximity for a sufficiently long time. The PF is tested in two challenging numerical examples.Comment: Accepted for publication in IEEE Transactions on Aerospace and Electronic System

Random finite set filters for superpositional sensors

The multi–object filtering problem is a generalization of the well–known single– object filtering problem. In essence, multi–object filtering is concerned with the joint estimation of the unknown and time–varying number of objects and the state of each of these objects. The filtering problem becomes particular challenging when the number of objects cannot be inferred from the collected observations and when no association between an observation and an object is possible. A rather new and promising approach to multi–object filtering is based on the principles of finite set statistics (FISST). FISST is a methodology, originally proposed by R. Mahler, that allows the formulation of the multi–object filtering problem in a mathematical rigorous way. One of the main building blocks of this methodology are random finite sets (RFSs), which are essentially finite set (FS) – valued random variables (RVs). Hence, a RFS is a RV which is not only random in the values of each element but also random in the number of elements of the FS. Under the premise that the observations are generated by detection–type sensors, many practical and efficient multi–object filters have been proposed. In general, detection–type sensors are assumed to generate observations that either originate from a single object or are false alarms. While this is a reasonable assumption in many multi–object filtering scenarios, this is not always the case. Central to this thesis is another type of sensors, the superposition (SPS)–type sensors. Those types of sensors are assumed to generate only one single observation that encapsulates the information about all the objects in the monitored area. More specifically, a single SPS observation is comprised out of the additive contribution of all the observations which would be generated by each object individually. In this thesis multi–object filters for SPS–type sensors are derived in a formal mathematical manner using the methodology of FISST. The first key contribution is a formulation of a SPS sensor model that, alongside errors like sensor noise, accounts for the fact that an object might not be visible to a sensor due to being outside of the sensor’s restricted field of view (FOV) or because it is occluded by obstacles. The second key contribution is the derivation of multi–object Bayes filter for SPS sensors that incorporates the aforementioned SPS sensor model. The third key contribution is the formulation of a filter variant that incorporates a multi–object multi–Bernoulli distribution as underlying multi–object state distribution, thus providing a multi–object multi–Bernoulli (MeMBer) filter variant for SPS–type sensors. As the stated variant turns out not to be conjugate, two approximations to the exact solution are given. The fourth key contribution is the derivation of computationally tractable implementations of the SPS MeMBer filters

Moving target detection in multi-static GNSS-based passive radar based on multi-Bernoulli filter

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accuratel

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management

Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach

The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters