Decomposed POMDP Optimization-Based Sensor Management for Multi-Target Tracking in Passive Multi-Sensor Systems

Zhu, Yun; Liang, Shuang; Gong, Maoguo; Yan, Junkun

doi:10.1109/jsen.2021.3139365

Cited by 32 publications

(11 citation statements)

References 56 publications

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“…In this section, the performance of the proposed efficient MOO with improved IC (EMO-IIC) method is demonstrated using two challenging multi-sensor MTT scenarios. There is one transmitter and ten receivers located in the surveillance area, and the structure of the multi-sensor system is the same as that in [40,43,45]. The sampling interval is fixed to T s = 10 s. The detection probability of a receiver j located at r j for a target with position p is modeled as follows: where r j − p denotes the distance between receiver j and the target, h = 1e − 4 , and R 0 = 5000 m. The detection probability decreases rapidly as r j − p increases.…”

Section: Resultsmentioning

confidence: 99%

“…If the detection ability of the last sensor is low, the overall performance of the filter degrades. A solution to this problem is to rank the sensors according to their ability to detect the target and then implement the IC fusion based on the ranking result [43]. In this paper, sensors are ranked according to ED obtained in (27), as follows…”

Section: Methodsmentioning

confidence: 99%

“…At each time step, we assume that P sensors are selected from the complete set. Motivated by the decomposed POMDP approach proposed in [43], the MOO problem is decomposed into a set of simple MOO subproblems for individual sensors to avoid the global combinatorial optimization. Instead of searching for the global optimal solution, sensors are selected sequentially from candidates.…”

Section: Efficient Multi-objective Optimizationmentioning

confidence: 99%

“…Another approach was developed in [42] for multi-sensor control but cannot be applied to solve the sensor selection problem. Recently, we proposed a decomposed POMDP optimization approach based on the CS divergence for efficient multi-sensor selection [43]. The approach can effectively reduce the computation burden and achieved satisfactory performance.…”

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confidence: 99%

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An efficient multi-objective optimization approach for sensor management via multi-Bernoulli filtering

Zhu

Liang

Yang

et al. 2022

EURASIP J. Adv. Signal Process.

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Intelligent sensor management is generally required for efficient and accurate data processing when the multi-sensor system is used for multi-target tracking (MTT). However, this is theoretically and computationally challenging. To deal with this problem, we propose a novel sensor management approach based on efficient multi-objective optimization for MTT under the framework of partially observed Markov decision process. The multi-Bernoulli filter is used in conjunction with two objective functions. To simplify the multi-objective optimization problem, we use the Euclidean distance (ED) between the feasible and utopian solution vectors as a measure of the objectives and then sequentially select sensors from the candidates. For the selected sensors, we rank them according to the obtained ED measure and implement the iterated-corrector fusion scheme after the ranking. Numerical studies demonstrate the effectiveness and efficiency of our approach in multi-sensor MTT scenarios.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Efficient Multi-objective Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

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An efficient multi-objective optimization approach for sensor management via multi-Bernoulli filtering

Zhu

Liang

Yang

et al. 2022

EURASIP J. Adv. Signal Process.

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“…Since the number of Doppler sensors is generally large, a subset of sensors can be selected in line 2, to ensure that the communication and real-time constraints are met. The sensor management problem is beyond the scope of this work; please refer to [61][62][63] for more information. The selected sensors in line 3 supply their measurement set Z (A k ) k and lines 4-12 implement the iterated-corrector scheme.…”

Section: Detailed Implementation Stepsmentioning

confidence: 99%

Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements

Zhu

Mallick²,

Liang

et al. 2022

Remote Sensing

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The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and produce the target trajectory in any tracking algorithm. Within the framework of random finite sets, we propose a novel constrained admissible region (CAR) based birth model that instantiates the birth distribution using Doppler-only measurements. By combining physics-based constraints in the unobservable subspace of the state space, the CAR based birth model can effectively reduce the ambiguity of the initial state. The CAR based birth model combines physics-based constraints in the unobservable subspace of the state space to reduce the ambiguity of the initial state. We implement the CAR based birth model with the generalized labeled multi-Bernoulli tracking filter to demonstrate the effectiveness of our proposed algorithm in Doppler-only tracking. The performance of the proposed approach is tested in two simulation scenarios in terms of the optimal subpattern assignment (OSPA) error, OSPA(2) error, and computing efficiency. The simulation results demonstrate the superiority of the proposed approach. Compared to the approach taken by the state-of-the-art methods, the proposed approach can at most reduce the OSPA error by 58.77%, reduce the OSPA(2) error by 43.51%, and increase the computing efficiency by 9.56 times in the first scenario. In the second scenario, the OSPA error is reduced by 62.80%, the OSPA(2) error is reduced by 43.65%, and the computing efficiency is increased by 2.61 times at most.

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Multi-sensor distributed fusion based on cross-location for passive tracking

Xu,

Guo,

Kuang

et al. 2024

SIViP

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Decomposed POMDP Optimization-Based Sensor Management for Multi-Target Tracking in Passive Multi-Sensor Systems

Cited by 32 publications

References 56 publications

An efficient multi-objective optimization approach for sensor management via multi-Bernoulli filtering

An efficient multi-objective optimization approach for sensor management via multi-Bernoulli filtering

Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements

Multi-sensor distributed fusion based on cross-location for passive tracking

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