scholarly journals A Novel Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking in an LPI Radar Network

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2193 ◽  
Author(s):  
Ji She ◽  
Fei Wang ◽  
Jianjiang Zhou
Keyword(s):  
Target Tracking ◽  
Power Allocation ◽  
Sensor Selection ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Allocation Algorithm ◽  
Radar Network

scholarly journals Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5944
Author(s):  
Chenyan Xue ◽  
Ling Wang ◽  
Daiyin Zhu
Keyword(s):  
Target Tracking ◽  
Cooperative Game ◽  
Time Allocation ◽  
Dwell Time ◽  
Nash Bargaining ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Allocation Algorithm ◽  
Radar Network ◽  
Target Allocation

To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low interception performance by optimizing the allocation of the dwell time of each radar under the constraints of the given target detection performance, minimizing the total dwell time of radar network. By introducing two variables, dwell time and target allocation indicators, we decompose the dwell time and target allocation into two subproblems. Firstly, combining the Lagrange relaxation algorithm with the Newton iteration method, we derive the iterative formula for the dwell time of each radar. The dwell time allocation of the radars corresponding to each target is obtained. Secondly, we use the fixed Hungarian algorithm to determine the target allocation scheme based on the dwell time allocation results. Simulation results show that the proposed algorithm can effectively reduce the total dwell time of the radar network, and hence, improve the LPI performance.

Power Allocation Algorithm for Target Tracking in Unmodulated Continuous Wave Radar Network

2015 ◽  
Vol 15 (2) ◽  
pp. 1098-1108 ◽  
Author(s):  
Junkun Yan ◽  
Hongwei Liu ◽  
Bo Jiu ◽  
Zheng Bao
Keyword(s):  
Target Tracking ◽  
Power Allocation ◽  
Continuous Wave ◽  
Allocation Algorithm ◽  
Radar Network ◽  
Wave Radar

scholarly journals Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster Based on Fuzzy Logic Reasoning

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1371
Author(s):  
Yuanshi Zhang ◽  
Minghai Pan ◽  
Qinghua Han
Keyword(s):  
Fuzzy Logic ◽  
Power Allocation ◽  
Clustering Algorithm ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Node Selection ◽  
Radar Tracking ◽  
Allocation Algorithm ◽  
Radar Networks ◽  
Logic Reasoning

The unmanned aerial vehicle (UAV) cluster is gradually attracting more attention, which takes advantage over a traditional single manned platform. Because the size of the UAV platform limits the transmitting power of its own radar, how to reduce the transmitting power while meeting the detection accuracy is necessary. Aim at multiple-target tracking (MTT), a joint radar node selection and power allocation algorithm for radar networks is proposed. The algorithm first uses fuzzy logic reasoning (FLR) to obtain the priority of targets to radars, and designs a radar clustering algorithm based on the priority to form several subradar networks. The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target, which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model. Considering the uncertainty of the target RCS in practice, the chance-constraint programming (CCP) is used to balance power resource and tracking accuracy. Through the joint radar node selection and power allocation algorithm, the radar networks can use less power resource to achieve a given tracking performance, which is more suitable for working on drone platforms. Finally, the simulation proves the effectiveness of the algorithm.

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