Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors

This paper presents a sensor-control method for choosing the best next state of the sensor(s), that provide(s) accurate estimation results in a multi-target tracking application. The proposed solution is formulated for a multi-Bernoulli filter and works via minimization of a new estimation error-based cost function. Simulation results demonstrate that the proposed method can outperform the state-of-the-art methods in terms of computation time and robustness to clutter while delivering similar accuracy

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

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

Multi-object tracking in video using labeled random finite sets

The safety of industrial mobile platforms (such as fork lifts and boom lifts) is of major concern in the world today as industry embraces the concepts of Industry 4.0. The existing safety methods are predominantly based on Radio Frequency Identification (RFID) technology and therefore can only determine the distance at which a pedestrian who is wearing an RFID tag is standing. Other methods use expensive laser scanners to map the surrounding and warn the driver accordingly. The aim of this research project is to improve the safety of industrial mobile platforms, by detecting and tracking pedestrians in the path of the mobile platform, using readily available cheap camera modules. In order to achieve this aim, this research focuses on multi-object tracking which is one of the most ubiquitously addressed problems in the field of \textit{Computer Vision}. Algorithms that can track targets under severe conditions, such as varying number of objects, occlusion, illumination changes and abrupt movements of the objects are investigated in this research project. Furthermore, a substantial focus is given to improving the accuracy and, performance and to handling misdetections and false alarms. In order to formulate these algorithms, the recently introduced concept of Random Finite Sets (RFS) is used as the underlying mathematical framework. The algorithms formulated to meet the above criteria were tested on standard visual tracking datasets as well as on a dataset which was created by our research group, for performance and accuracy using standard performance and accuracy metrics that are widely used in the computer vision literature. These results were compared with numerous state-of-the-art methods and are shown to outperform or perform favourably in terms of the metrics mentioned above