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 R\`{e}nyi 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

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

Technical Report: Cooperative Multi-Target Localization With Noisy Sensors

This technical report is an extended version of the paper 'Cooperative Multi-Target Localization With Noisy Sensors' accepted to the 2013 IEEE International Conference on Robotics and Automation (ICRA). This paper addresses the task of searching for an unknown number of static targets within a known obstacle map using a team of mobile robots equipped with noisy, limited field-of-view sensors. Such sensors may fail to detect a subset of the visible targets or return false positive detections. These measurement sets are used to localize the targets using the Probability Hypothesis Density, or PHD, filter. Robots communicate with each other on a local peer-to-peer basis and with a server or the cloud via access points, exchanging measurements and poses to update their belief about the targets and plan future actions. The server provides a mechanism to collect and synthesize information from all robots and to share the global, albeit time-delayed, belief state to robots near access points. We design a decentralized control scheme that exploits this communication architecture and the PHD representation of the belief state. Specifically, robots move to maximize mutual information between the target set and measurements, both self-collected and those available by accessing the server, balancing local exploration with sharing knowledge across the team. Furthermore, robots coordinate their actions with other robots exploring the same local region of the environment.Comment: Extended version of paper accepted to 2013 IEEE International Conference on Robotics and Automation (ICRA

Multi-Bernoulli filter based sensor selection with limited sensing range for multi-target tracking

In this paper, we consider a sensor network with limited sensing range and present a sensor selection algorithm for multi-target tracking problem. The proposed algorithm is based on the multi-Bernoulli filtering and a collection of sub-selection problems for individual target. A sub-selection problem for each target is investigated under the framework of partially observed Markov decision process. Each sub-selection problem is solved using a combination of information theoretic method and limited sensing range. Numerical studies validate the effectiveness of our method for multi-target tracking scenario in a sensor network

Regional variance for multi-object filtering

Recent progress in multi-object filtering has led to algorithms that compute the first-order moment of multi-object distributions based on sensor measurements. The number of targets in arbitrarily selected regions can be estimated using the first-order moment. In this work, we introduce explicit formulae for the computation of the second-order statistic on the target number. The proposed concept of regional variance quantifies the level of confidence on target number estimates in arbitrary regions and facilitates information-based decisions. We provide algorithms for its computation for the Probability Hypothesis Density (PHD) and the Cardinalized Probability Hypothesis Density (CPHD) filters. We demonstrate the behaviour of the regional statistics through simulation examples