Nonmyopic sensor scheduling for target tracking with emission control

Qiao, Chunming; Shan, Ganlin; Yan, Liping; Duan, Xinbin

doi:10.1002/acs.2987

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…where t i is the continuous working time, n i is the number of targets tracked by sensor i at the same time, t 0 is a constant which can be set according to working parameters of radar interceptor (Qiao et al, 2019). According to the silence principle, the intercepted probability factor becomes zero when the sensor stops tracking the target (Xu et al, 2019a).…”

Section: System Observation and Measurement Equationmentioning

confidence: 99%

“…But, the computational complexity of NMSM is higher. The commonly used solving algorithms for NMSM are dynamic programming algorithm (Li et al, 2009), decision tree optimization algorithm (Qiao et al, 2019) and modern intelligent optimization algorithm (Pang and Shan, 2019;Solaiman, 2016). The intelligent optimization algorithm, such as genetic algorithm, particle swarm optimization algorithm, has high solving efficiency and will not be limited by the calculated condition, which has been used in the sensor management field.…”

Section: Introductionmentioning

confidence: 99%

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Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

Zhang

Shan

2021

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Purpose Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system. Design/methodology/approach First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly. Findings Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance. Originality/value In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

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Section: System Observation and Measurement Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

Zhang

Shan

2021

Self Cite

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“…In order to illustrate the advancement of the branchand-bound-based greedy search algorithm (BB-GS), we compare it with three existing algorithms, namely uniform cost search (UCS), greedy search (GS) and branch-andbound-based standard cost search (BB-UCS) [29]. Then, the percentage of nodes opened and the maximum number of nodes stored are selected as evaluation indexes, which represent the performance of the algorithm in search time and memory consumption, respectively [29]. Fig.…”

Section: Comparison Of Search Algorithmsmentioning

confidence: 99%

A Risk-Based Sensor Management Method for Target Detection in the Presence of Suppressive Jamming

Zhang

Shan²

2020

Teh. vjesn.

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In this paper, a risk-based sensor management method for target detection in the presence of suppressive jamming is proposed, in which available sensors are dynamically scheduled to control the risk in the execution of target detection task. Firstly, the sensor detection models in the absence/ presence of jamming are established, and the calculation method of target detection risk is presented based on the target detection probability. Secondly, the sensor radiation model is established, and the calculation method of radiation risk is given by using hidden Markov filter. Then, a non-myopic objective function is constructed to minimize the sum of detection risk and radiation risk. Furthermore, in order to obtain the optimal solution of objective function quickly, a decision tree search algorithm combining with branch and bound theory and greedy search is proposed. Finally, simulations are conducted, and the results show that the proposed algorithm and sensor management method are effective and advanced compared with the existing algorithms and methods.

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“…An objective function should be established firstly and after that, the optimal sensor management schemes should be calculated. The calculated sensor management scheme is carried out to get observations, which will be used to estimate motion states of targets [1]- [4].…”

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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Nonmyopic sensor scheduling for target tracking with emission control

Cited by 8 publications

References 24 publications

Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

A Risk-Based Sensor Management Method for Target Detection in the Presence of Suppressive Jamming

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

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