Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

Zhu, Yun; Wang, Jun; Liang, Shuang

doi:10.3390/s19040980

Cited by 17 publications

(13 citation statements)

References 32 publications

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“…Step 2 : Iterated-corrector multi-sensor GLMB fusion 1) for sensor j ∈ A * k do 2) if j = 1 do 3) Predict the prior δ−GLMB using ( 16)- (22) to obtain the multi-target prediction π…”

Section: P ) From Small To Largementioning

confidence: 99%

“…However, this requires one to assign relative values to each task. Our recent work [22] considers the legacy tracks and measurement-updated tracks separately, to make full use of information involved in the multi-target posterior density. To deal with a multitude of performance criteria in a direct manner simultaneously, some researchers have proposed to use information theoretic measures as objective or reward functions.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Sensor Management for Multitarget Tracking in Passive Sensor Networks via Cauchy-Schwarz Divergence

Zhu¹

2020

Preprint

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This paper presents an efficient sensor management approach for multi-target tracking in passive sensor networks. Compared with active sensor networks, passive sensor networks have larger uncertainty due to the nature of passive sensing. Multi-target tracking in passive sensor networks is challenging because the multi-sensor multi-target fusion problem is difficult and sensor management is necessary to achieve good tradeoffs between tracking accuracy and energy consumption or other costs. To address this problem, we present an efficient information-theoretic approach to manage the sensors for better tracking of the unknown and time-varying number of targets. This is accomplished with two main technical innovations. The first is a tractable information-based multi-sensor selection solution via a partially observed Markov decision process framework. The Cauchy-Schwarz divergence is used as the criterion to select informative sensors sequentially from the candidates. The second is a novel dual-stage fusion strategy based on the iteratedcorrector multi-sensor generalized labeled multi-Bernoulli filter. Since the performance of the iterated-corrector scheme is greatly influenced by the order of sensor updates, the selected sensors are first ranked in order of their abilities to detect targets according to the Cauchy-Schwarz divergence, followed the iteratedcorrector update. The computation costs of ranking the sensors are negligible, since the Cauchy-Schwarz divergence has been computed in the multi-sensor selection procedure. Simulation results validate the effectiveness and efficiency of the proposed approach.

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“…Step 2 : Iterated-corrector multi-sensor GLMB fusion 1) for sensor j ∈ A * k do 2) if j = 1 do 3) Predict the prior δ−GLMB using ( 16)- (22) to obtain the multi-target prediction π…”

Section: P ) From Small To Largementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Sensor Management for Multitarget Tracking in Passive Sensor Networks via Cauchy-Schwarz Divergence

Zhu¹

2020

Preprint

Self Cite

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“…For localizing and searching objects simultaneously, (Dames and Kumar 2015) and (Charrow, Michael, and Kumar 2015) considered a similar scenario, but only for stationary objects. Planning using multi-objective optimization (MOP) has not been explored yet, except for single sensor selection (Zhu, Wang, and Liang 2019) or using the weighted sum method presented in (Charrow, Michael, and Kumar 2015) where the weighting parameters are difficult to define without prior knowledge. In contrast, we focus on optimizing all value functions (i.e., tracking and discovering) simultaneously using MOP.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects

Nguyen

Rezatofighi

et al. 2020

AAAI

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We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking filter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1-1/e) approximation for maximizing the submodular multi-objective function.

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“…The random finite sets (RFS) theory [ 23 ], which represents the targets and the measurements as a finite variable set, is a suitable choice. To solve the point MTT problem, in the early stage, many RFS-based filters have been proposed, such as the Probability Hypothesis Density (PHD) filter [ 24 , 25 , 26 ], the Cardinalized Probability Hypothesis Density (CPHD) filter [ 27 , 28 , 29 , 30 ] and a series of multi-Bernoulli (MB) filters [ 31 , 32 , 33 , 34 ]. In recent years, scholars have proposed many RFS-based filters to solve the extended MTT problem, such as PHD for extended target tracking (ETT-PHD) [ 9 , 35 , 36 , 37 ], ETT-CPHD [ 38 , 39 , 40 ], gamma-Gaussian-inverse Wishart-Poisson multi-Bernoulli mixture (GGIW-PMBM) [ 40 , 41 ], GGIW implementation of the Labelled Multi-Bernoulli (GGIW-LMB) [ 42 , 43 ], and so on [ 22 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

“…The existence of a single target can be modeled by a Bernoulli RFS [ 14 , 31 , 32 , 33 , 34 , 40 , 41 , 42 ]. The cardinality of a Bernoulli RFS

can either be 1 (with probability

) or empty (with probability

), and the Bernoulli density distribution of

can be defined as

…”

Section: Introductionmentioning

confidence: 99%

Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

Xie

2020

Sensors

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The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter.

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Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

Cited by 17 publications

References 32 publications

Efficient Sensor Management for Multitarget Tracking in Passive Sensor Networks via Cauchy-Schwarz Divergence

Efficient Sensor Management for Multitarget Tracking in Passive Sensor Networks via Cauchy-Schwarz Divergence

Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects

Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

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