Constrained Sensor Control for Labeled Multi-Bernoulli Filter Using Cauchy-Schwarz Divergence

Gostar, Amirali Khodadadian; Hoseinnezhad, Reza; Rathnayake, Tharindu; Wang, Xiaoying; Bab–Hadiashar, Alireza

doi:10.1109/lsp.2017.2723924

Cited by 42 publications

(20 citation statements)

References 14 publications

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“…In [14], the Cauchy-Schwarz divergence and generalized labeled multi-Bernoulli filter-based sensor control method is derived. In [15], a labeled multi-Bernoulli filter and Cauchy-Schwarz divergence-based constrained sensor control method is proposed. In [16], the authors control the sensors by using the expected risk reduction between the multi-target predicted and updated densities.…”

Section: Introductionmentioning

confidence: 99%

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Wang

Mei

Peng

et al. 2019

Entropy

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Since the submarine has become the major threat to maritime security, there is an urgent need to find a more efficient method of anti-submarine warfare (ASW). The digital twin theory is one of the most outstanding information technologies, and has been quite popular in recent years. The most influential change produced by digital twin is the ability to enable real-time dynamic interactions between the simulation world and the real world. Digital twin can be regarded as a paradigm by means of which selected online measurements are dynamically assimilated into the simulation world, with the running simulation model guiding the real world adaptively in reverse. By combining digital twin theory and random finite sets (RFSs) closely, a new framework of sensor control in ASW is proposed. Two key algorithms are proposed for supporting the digital twin-based framework. First, the RFS-based data-assimilation algorithm is proposed for online assimilating the sequence of real-time measurements with detection uncertainty, data association uncertainty, noise, and clutters. Second, the computation of the reward function by using the results of the proposed data-assimilation algorithm is introduced to find the optimal control action. The results of three groups of experiments successfully verify the feasibility and effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Wang

Mei

Peng

et al. 2019

Entropy

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“…This method has been proved to be the same principle as the former sensor management method with the same optimal solution obtained. The third one is the sensor management method based on random sets for multi-objective tracking [24]- [27]. The difference between this method and the former two kinds of methods is mainly on the target tracking theory, rather than the optimization principle.…”

Section: Introductionmentioning

confidence: 99%

Distributed Sensor Management Based on Target Losing Probability for Maneuvering Multi-Target Tracking

Shan

Pang

2020

IEEE Access

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This paper proposed a distributed sensor management method in the constraint of target losing probability for maneuvering multi-target tracking. Three main contributions existed in this paper, including adding the emission interception risk to the sensor management objective function, analyzing actual working modes of radars and regarding the target losing probability as one of constraints in sensor management. Two kind of radar working modes were introduced. They were the single target tracking mode and the multiple target tracking mode, leading to different kinds of sensor management methods. Then two sensor management models, containing sensor management in a large defending scope and in a small defending scope, were built. After that, the distributed sensor management process was introduced. Finally, simulations were conducted to show the effectiveness and efficiency of the proposed sensor management methods. By applying the proposed sensor management method, targets couldn't be easily lost and radar could be well protected at the same time. INDEX TERMS Sensor management, target tracking, distributed sensor networks, target losing probability.

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“…To our best knowledge, the sensor control technique has never been used for suppressing the influence of the DBZ. To note, although many RFS control approaches have been extended to the partially observable Markov decision process (POMDP) framework to concurrently solve the multitarget tracking (MTT) and the sensor control problems [40 ] , such as the PHD-POMDP [41], MB-POMDP [42][43][44][45][46][47][48], LMB-POMDP [49][50][51][52][53][54][55][56][57], GLMB-POMDP [58], etc, these algorithms cannot be applicable to the DBZ masking case and are not suitable for multiple maneuvering targets. In response to these problems, this paper proposes an online sensor control algorithm for tracking multiple maneuvering targets hidden in the DBZ.…”

Section: Introductionmentioning

confidence: 99%

MM-PHD filter-based sensor control for tracking multiple maneuvering targets hidden in the DBZ

Wu¹

2020

Preprint

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<a></a><a></a><a>To improve the performance of tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we put forward the idea of using sensor control technique to suppress the DBZ masking problem for the first time, by utilizing the principle that the absolute Doppler of a target with respect to a sensor is affected by the target-to-sensor relative geometry and extending multi model probability hypothesis density (MM-PHD) filter for DBZ masking to the partially observable Markov decision process (POMDP) framework. First, the process flow of sensor control is systematically constructed based on our existing work. Second, in the core sensor controller module, we devise three objective functions (including a new safety indicator ensuring sensor safety, a novel reward rule for the DBZ avoidance, and the Cauchy-Schwarz divergence (CSD) compatible with the multi-maneuvering-target tracking) and a decision-making logic for the selection of control commands. Finally, the feasibility and effectiveness of the proposed control scheme are verified through numerical examples, and it is demonstrated that it is obviously superior to the random control strategy and the earlier work without using the control technology.</a>

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Constrained Sensor Control for Labeled Multi-Bernoulli Filter Using Cauchy-Schwarz Divergence

Cited by 42 publications

References 14 publications

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Distributed Sensor Management Based on Target Losing Probability for Maneuvering Multi-Target Tracking

MM-PHD filter-based sensor control for tracking multiple maneuvering targets hidden in the DBZ

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