Target Capacity Based Power Allocation Scheme in Radar Network

Jinhui DAI; Junkun YAN; Penghui WANG; Hongwei LIU

doi:10.11999/JEIT200873

Volume 43 Issue 9

Sep. 2021

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Jinhui DAI, Junkun YAN, Penghui WANG, Hongwei LIU. Target Capacity Based Power Allocation Scheme in Radar Network[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2688-2694. doi: 10.11999/JEIT200873

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Jinhui DAI, Junkun YAN, Penghui WANG, Hongwei LIU. Target Capacity Based Power Allocation Scheme in Radar Network[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2688-2694. doi: 10.11999/JEIT200873

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Target Capacity Based Power Allocation Scheme in Radar Network

doi: 10.11999/JEIT200873 cstr: 32379.14.JEIT200873

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62071345), The National Science Fund for Distinguished Yong Scholars (61525105), The Fund for Foreign Scholars in University Research and Teaching Programs (111 project, B18039), The Natural Science Foundation of Shaanxi Province (2020JQ-297), The Aeronautical Science Foundation of China (201920081002), The Foundation of National Radar Signal Processing Laboratory (61424010406)

Received Date: 2020-10-12
Rev Recd Date: 2021-01-02

Available Online: 2021-01-07

Publish Date: 2021-09-16

Abstract

Abstract

In view of the fact that low power resource utilization rate exists in radar network, a Target Capacity based Power Allocation (TC-PA) scheme is proposed to increase the number of the targets that satisfy tracking accuracy requirements. Firstly, this scheme formulates the power allocation model of radar network as a non-smooth and non-convex optimization problem. Then the original problem is relaxed into a smooth and non-convex problem through introducing Sigmoid function. Finally, the relaxed non-convex problem is solved by utilizing the Proximal Inexact Augmented Lagrangian Multiplier Method (PI-ALMM). Simulation results show that the PI-ALMM can quickly converge to a stationary point for solving the non-convex optimization problem with linear constraints. Moreover, the proposed TC-PA scheme outperforms the traditional uniform power allocation method and genetic algorithm, in terms of target capacity.
- Radar network,
- Multiple target tracking,
- Resource allocation,
- Non-convex optimization

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References(23)

References

[1]	GRECO M S, GINI F, STINCO P, et al. Cognitive radars: On the road to reality: Progress thus far and possibilities for the future[J]. IEEE Signal Processing Magazine, 2018, 35(4): 112–125. doi: 10.1109/MSP.2018.2822847
[2]	王祥丽, 易伟, 孔令讲. 基于多目标跟踪的相控阵雷达波束和驻留时间联合分配方法[J]. 雷达学报, 2017, 6(6): 602–610. doi: 10.12000/JR17045 WANG Xiangli, YI Wei, and KONG Lingjiang. Joint beam selection and dwell time allocation for multi-target tracking in phased array radar system[J]. Journal of Radars, 2017, 6(6): 602–610. doi: 10.12000/JR17045
[3]	严俊坤, 刘宏伟, 戴奉周, 等. 基于非线性机会约束规划的多基雷达系统稳健功率分配算法[J]. 电子与信息学报, 2014, 36(3): 509–515. doi: 10.3724/SP.J.1146.2013.00656 YAN Junkun, LIU Hongwei, DAI Fengzhou, et al. Nonlinear chance constrained programming based robust power allocation algorithm for multistatic radar systems[J]. Journal of Electronics &Information Technology, 2014, 36(3): 509–515. doi: 10.3724/SP.J.1146.2013.00656
[4]	SHI Chenguang, DING Lintao, WANG Fei, et al. Low probability of intercept-based collaborative power and bandwidth allocation strategy for multi-target tracking in distributed radar network system[J]. IEEE Sensors Journal, 2020, 20(12): 6367–6377. doi: 10.1109/JSEN.2020.2977328
[5]	YAN Junkun, DAI Jinhui, PU Wenqiang, et al. Quality of service constrained-resource allocation scheme for multiple target tracking in radar sensor network[J]. IEEE Systems Journal, 2021, 15(1): 771–779. doi: 10.1109/JSYST.2020.2990409
[6]	YAN Junkun, PU Wenqiang, ZHOU Shenghua, et al. Optimal resource allocation for asynchronous multiple targets tracking in heterogeneous radar networks[J]. IEEE Transactions on Signal Processing, 2020, 68: 4055–4068. doi: 10.1109/TSP.2020.3007313
[7]	XIE Mingchi, YI Wei, KONG Lingjiang, et al. Receive-beam resource allocation for multiple target tracking with distributed MIMO radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(5): 2421–2436. doi: 10.1109/TAES.2018.2818579
[8]	XIE Mingchi, YI Wei, KIRUBARAJAN T, et al. Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks[J]. IEEE Transactions on Signal Processing, 2018, 66(3): 729–743. doi: 10.1109/TSP.2017.2777394
[9]	YI Wei, YUAN Ye, HOSEINNEZHAD H, et al. Resource scheduling for distributed multi-target tracking in netted colocated MIMO radar systems[J]. IEEE Transactions on Signal Processing, 2020, 68: 1602–1617. doi: 10.1109/TSP.2020.2976587
[10]	ZHANG Haowei, LIU Weijian, XIE Junwei, et al. Joint subarray selection and power allocation for cognitive target tracking in large-scale MIMO radar networks[J]. IEEE Systems Journal, 2020, 14(2): 2569–2580. doi: 10.1109/JSYST.2019.2960401
[11]	严俊坤, 纠博, 刘宏伟, 等. 一种针对多目标跟踪的多基雷达系统聚类与功率联合分配算法[J]. 电子与信息学报, 2013, 35(8): 1875–1881. doi: 10.3724/SP.J.1146.2012.01470 YAN Junkun, JIU Bo, LIU Hongwei, et al. Joint cluster and power allocation algorithm for multiple targets tracking in multistatic radar systems[J]. Journal of Electronics &Information Technology, 2013, 35(8): 1875–1881. doi: 10.3724/SP.J.1146.2012.01470
[12]	YAN Junkun, PU Wenqiang, ZHOU Shenghua, et al. Collaborative detection and power allocation framework for target tracking in multiple radar system[J]. Information Fusion, 2020, 55: 173–183. doi: 10.1016/j.inffus.2019.08.010
[13]	秦童, 戴奉周, 刘宏伟, 等. 火控相控阵雷达的时间资源管理算法[J]. 系统工程与电子技术, 2016, 38(3): 545–550. doi: 10.3969/j.issn.1001-506X.2016.03.11 QING Tong, DAI Fengzhou, LIU Hongwei, et al. Time resource management algorithm for the fire control phased-array radar[J]. Systems Engineering and Electronics, 2016, 38(3): 545–550. doi: 10.3969/j.issn.1001-506X.2016.03.11
[14]	YAN Junkun, ZHANG Peng, DAI Jinhui, et al. Target capacity based simultaneous multibeam power allocation scheme for multiple target tracking application[J]. Signal Processing, 2021, 178: 107794. doi: 10.1016/j.sigpro.2020.107794
[15]	GURBUZ A C, MDRAFI R, and CETINER B A. Cognitive radar target detection and tracking with multifunctional reconfigurable antennas[J]. IEEE Aerospace and Electronic Systems Magazine, 2020, 35(6): 64–76. doi: 10.1109/MAES.2020.2990589
[16]	NEITZ O and EIBERT T F. A plane-wave synthesis approach for 3D monostatic RCS prediction from near-field measurements[C]. 2018 15th European Radar Conference, Madrid, Spain, 2018: 99–102. doi: 10.23919/EuRAD.2018.8546622.
[17]	SONG Wanjun and ZHANG Hou. RCS prediction of objects coated by magnetized plasma via scale model with FDTD[J]. IEEE Transactions on Microwave Theory and Techniques, 2017, 65(6): 1939–1945. doi: 10.1109/TMTT.2016.2645567
[18]	TICHAVSKY P, MURAVCHIK C H, and NEHORAI A. Posterior Cramer-Rao bounds for discrete-time nonlinear filtering[J]. IEEE Transactions on Signal Processing, 1998, 46(5): 1386–1396. doi: 10.1109/78.668800
[19]	ILIEV A, KYURKCHIEV N, MARKOV S. On the approximation of the step function by some sigmoid functions[J]. Mathematics and Computers in Simulation, 2017, 133: 223–234. doi: 10.1016/j.matcom.2015.11.005
[20]	GARULLI A and VICINO A. Convex relaxations in circuits, systems, and control[J]. IEEE Circuits and Systems Magazine, 2009, 9(2): 46–56. doi: 10.1109/MCAS.2008.931737
[21]	ZHANG Jiawei and LUO Zhiquan. A proximal alternating direction method of multiplier for linearly constrained nonconvex minimization[J]. SIAM Journal on Optimization, 2020, 30(3): 2272–2302. doi: 10.1137/19M1242276
[22]	ZHANG Jiawei and LUO Zhiquan. A global dual error bound and its application to the analysis of linearly constrained nonconvex optimization[J]. arXiv preprint arXiv: 2006.16440, 2020.
[23]	GREFENSTETTE J J. Optimization of control parameters for genetic algorithms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1986, 16(1): 122–128. doi: 10.1109/TSMC.1986.289288