Amirali K. Gostar

Robust Multi-Bernoulli Sensor Selection for Multi-Target Tracking in Sensor Networks

This letter addresses the sensor selection problem for tracking multiple dynamic targets within a sensor network. Since the bandwidth and energy of the sensor network are constrained, it would not be feasible to directly use the entire information of sensor nodes for detection and tracking of the targets and hence the need for sensor selection. Our sensor selection solution is formulated using the multi-Bernoulli random finite set framework. The proposed method selects a minimum subset of sensors which are most likely to provide reliable measurements. The overall scheme is a robust method that works in challenging scenarios where no prior information are available on clutter intensity or sensor detection profile. Simulation results demonstrate successful sensor selection in a challenging case where five targets move in a close vicinity to each other. Comparative results show the superior performance of our method in terms of accuracy of estimating the number of targets and their states.

Multi-Bernoulli sensor-selection for multi-target tracking with unknown clutter and detection profiles

A new sensor-selection solution within a multi-Bernoulli-based multi-target tracking framework is presented. The proposed method is especially designed for the general multi-target tracking case with no prior knowledge of the clutter distribution or the probability of detection, and uses a new task-driven objective function for this purpose. Step-by-step sequential Monte Carlo implementation of the method is presented along with a similar sensor-selection solution formulated using an information-driven objective function (Renyi divergence). The two solutions are compared in a challenging scenario and the results show that while both methods perform similarly in terms of accuracy of cardinality and state estimates, the task-driven sensor-selection method is substantially faster. HighlightsA new sensor-selection solution for multi-target tracking.No need of prior knowledge of clutter distribution.No need of any knowledge of detection profile.Sequential Monte-Carlo implementation is presented.Works substantially faster than traditional methods.

Multi-bernoulli sensor control for multi-target tracking

A new approach to solve the sensor control problem is proposed, formulated based on multi-object Bayes filtering in the partially observable Markov decision process (POMDP) context, where the multi-object states are assumed to be random finite sets with multi-Bernoulli distributions. We introduce a novel cost function that is reliable in real-time environment. In each filtering iteration, after predicting the multi-Bernoulli parameters, estimates for the number and states of the targets are extracted. For each admissible control command, Monte-Carlo samples of measurements corresponding to the estimated target states are generated. Then, for each measurement sample, the CB-MeMBer update is performed and the average cost function is computed. The best command is the one incurring the minimum cost. The simulation results involve a challenging case of detecting and tracking up to 5 manoeuvring targets using a controllable sensor, and show that our method outperforms competing methods both in terms of tracking accuracy (measured in using OSPA metric) and in terms of computational cost.

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 sensors that provide accurate estimation results in a multitarget 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 Multitarget Filters via Minimization of Posterior Dispersion

This paper presents a new sensor management method for multitarget filtering, that is designed based on maximizing a measure of confidence in accuracy of the multitarget state estimate. Confidence of estimation is quantified by optimal subpattern assignment-based dispersion of the multitarget posterior about its statistical mean. Implementation of the algorithm for generic multitarget filters is presented. Simulation studies with labeled multi-Bernoulli filter demonstrate excellent performance in challenging sensor control scenarios.

Bayesian Track-Before-Detect for closely spaced targets

Track-Before-Detect (TBD) is an effective approach to multi-target tracking problems with low signal-to-noise (SNR) ratio. In this paper we propose a novel Labeled Random Finite Set (RFS) solution to the multi-target TBD problem for a generic pixel based measurement model. In particular, we discuss the applicability of the Generalized Labeled Multi-Bernoulli (GLMB) distribution to the TBD problem for low SNR and closely spaced targets. In such case, the commonly used separable targets assumption does not hold and a more sophisticated algorithm is required. The proposed GLMB recursion is effective in the sense that it matches the cardinality distribution and Probability Hypothesis Density (PHD) function of the true joint posterior density. The approach is validated through simulation results in challenging scenarios.

Multi-sensor control for multi-object Bayes filters

Abstract Sensor management in multi-object stochastic systems is a theoretically and computationally challenging problem. This paper presents a new approach to the multi-target multi-sensor control problem within the partially observed Markov decision process (POMDP) framework. We model the multi-object state as a labeled multi-Bernoulli random finite set (RFS), and use the labeled multi-Bernoulli filter in conjunction with minimizing a task-driven control objective function: posterior expected error of cardinality and state (PEECS). A major contribution is a guided search for multi-dimensional optimization in the multi-sensor control command space, using coordinate descent method. In conjunction with the Generalized Covariance Intersection method for multi-sensor fusion, a fast multi-sensor control algorithm is achieved. Numerical studies are presented in several scenarios where numerous controllable (mobile) sensors track multiple moving targets with different levels of observability. The results show that our method works significantly faster than the approach taken by the state of the art methods, with similar tracking errors.

OSPA-based sensor control

This paper presents a new sensor control method for multi-object filtering, that is designed based on maximizing a measure of confidence in state estimation accuracy. Confidence of 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 common statistical filters. Implementation of the algorithm in conjunction with a labeled multi-Bernoulli filter is presented. Simulation studies demonstrate that the proposed method works in a challenging sensor control for multi-target tracking scenario.

Centralized multiple-view sensor fusion using labeled multi-Bernoulli filters

Abstract This paper presents a novel method for track-to-track fusion to integrate multiple-view sensor data in a centralized sensor network. The proposed method overcomes the drawbacks of the commonly used Generalized Covariance Intersection method, which considers constant weights allocated for sensors. We introduce an intuitive approach to automatically tune the weights in the Generalized Covariance Intersection method based on the amount of information carried by the posteriors that are locally computed from measurements acquired at each sensor node. To quantify information content, Cauchy–Schwarz divergence is used. Our solution is particularly formulated for sensor networks where the update step of a Labeled Multi-Bernoulli filter is running locally at each node. We will show that with that type of filter, the weight associated with each sensor node can be separately adapted for each Bernoulli component of the filter. The results of numerical experiments show that our proposed method can successfully integrate information provided by multiple sensors with different fields of view. In such scenarios, our method significantly outperforms the common approach of using Generalized Covariance Intersection method with constant weights, in terms of inclusion of all existing objects and tracking accuracy.

Information theoretic approach to robust multi-Bernoulli sensor control

A novel sensor control solution is presented, formulated within a Multi-Bernoulli-based multi-target tracking framework. The proposed method is especially designed for the general multi-target tracking case, where no prior knowledge of the clutter distribution or the probability of detection profile are available. In an information theoretic approach, our method makes use of Rènyi divergence as the reward function to be maximized for finding the optimal sensor control command at each step. We devise a Monte Carlo sampling method for computation of the reward. Simulation results demonstrate successful performance of the proposed method in a challenging scenario involving five targets maneuvering in a relatively uncertain space with unknown distance-dependent clutter rate and probability of detection.