Multitarget Tracking with Spatial Nonmaximum Suppressed Sensor Selection

Abstract

Multitarget tracking is one of the most important applications of sensor networks, yet it is an extremely challenging problem since multisensor multitarget tracking itself is nontrivial and the difficulty is further compounded by sensor management. Recently, random finite set based Bayesian framework has opened doors for multitarget tracking with sensor management, which is modelled in the framework of partially observed Markov decision process (POMDP). However, sensor management posed as a POMDP is in essence a combinatorial optimization problem which is NP-hard and computationally unacceptable. In this paper, we propose a novel sensor selection method for multitarget tracking. We first present the sequential multi-Bernoulli filter as a centralized multisensor fusion scheme for multitarget tracking. In order to perform sensor selection, we define the hypothesis information gain (HIG) of a sensor to measure its information quantity when the sensor is selected alone. Then, we propose spatial nonmaximum suppression approach to select sensors with respect to their locations and HIGs. Two distinguished implementations have been provided using the greedy spatial nonmaximum suppression. Simulation results verify the effectiveness of proposed sensor selection approach for multitarget tracking.

Multitarget tracking in sensor networks via efficient information-theoretic sensor selection

“…Remark that equation ( 24) is the same with the "individual hypothesis information gain" in the study by Ma et al, 15 whereas the underlying rationale of sensor selection is different. In the study by Ma et al, 15 noninformative sensors are eliminated by spatial suppression while redundant sensors are selected for update. On the other hand, the decomposed POMDP-based approach only selects minimal number of sensors for tracking.…”

“…In the study by Ma et al, 15 noninformative sensors are eliminated by spatial suppression while redundant sensors are selected for update. On the other hand, the decomposed POMDP-based approach only selects minimal number of sensors for tracking.…”

“…Then, the observations from each sensor are applied to update target state recursively. The SMC implementation details can be found in the study by Ma et al 15 We refer the readers to the study by Ma et al 17 for other alternative centralized schemes, such as parallel update and random update. Besides, the consensus approach can be applied in a distributed manner.…”

“…Since the nearest neighboring strategy has been widely used in sensor selection, we implement minimum expected distance as another objective function for comparison. Moreover, we also compare the two spatial nonmaximum suppression selection method described in the study by Ma et al 15 with gate " ¼ 500 m, referred as "sum HIG" suppression and "individual HIG" suppression, respectively. It is intuitive for this scenario that to select the proper sensor with respect to a target should choose the nearest sensor that target falls in its FoV.…”

“…10 In the literature, different objective functions were proposed for the POMDP-based sensor management, for example, the Kullback-Leibler discrimination, 9 posterior expected number of targets, 11 the Rényi divergence , 12 and the posterior cardinality. 13 However, no matter what objective function is used, POMDP-based sensor management is essentially a global combinatorial optimization problem that is extremely challenging especially when the sensor network is quite large, unless only one sensor is selected in use as illustrated in the study by Gostar et al 14 A spatial nonmaximum suppression heuristics has been proposed recently to alleviate this issue, 15 whereas it suffers from a tuning parameter of the suppress gate. Ma et al 16 proposed a suboptimal solution of the POMDP-based sensor control which is not suitable for sensor selection problem.…”