Transmit Power Allocation Method of Frequency Diverse Array-Multi Input and Multi Output Radar Based on Reinforcement Learning

DING Zihang; XIE Junwei; QI Cheng

doi:10.11999/JEIT211555

Volume 45 Issue 2

Feb. 2023

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DING Zihang, XIE Junwei, QI Cheng. Transmit Power Allocation Method of Frequency Diverse Array-Multi Input and Multi Output Radar Based on Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2023, 45(2): 550-557. doi: 10.11999/JEIT211555

Citation:

DING Zihang, XIE Junwei, QI Cheng. Transmit Power Allocation Method of Frequency Diverse Array-Multi Input and Multi Output Radar Based on Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2023, 45(2): 550-557. doi: 10.11999/JEIT211555

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Transmit Power Allocation Method of Frequency Diverse Array-Multi Input and Multi Output Radar Based on Reinforcement Learning

doi: 10.11999/JEIT211555

Air and Missile Defense College, Aire Force Engineering University, Xi’an 710051, China

Received Date: 2021-12-22
Accepted Date: 2022-03-03
Rev Recd Date: 2022-02-24

Available Online: 2022-03-07

Publish Date: 2023-02-07

Abstract

Abstract

In recent years, the electromagnetic environment has been becoming increasingly complex and changeable, and new jamming methods emerge one after another, which brings great challenges and threats to the radar system. In this paper, the spectrum interference model is introduced and a transmit power allocation optimization method based on Reinforcement Learning (RL) under the dynamic game framework of Frequency Diverse Array Multi Input and Multi Output (FDA-MIMO) radar and the spectrum interference is proposed, so that the radar system can obtain the maximum output Signal-to-Interference plus Noise Ratio (SINR). Firstly, the mathematical model of FDA-MIMO radar is established, and on this basis, the spectrum interference model is constructed. Secondly, there is a Stackelberg game relationship between radar and jammer. Taking radar as the leader and jammer as the follower, the transmit power allocation optimization model under the framework of dynamic game is established. Using the Deep Deterministic Policy Gradient (DDPG) algorithm and power constraints, a reward function is designed to allocate the radar transmit power in real time to obtain the maximum output SINR. Finally, the simulation results show that under the framework of the game between radar and interference, the proposed optimization algorithm can effectively optimize the radar transmit power and make the radar have better anti-jamming performance.
- Frequency Diverse Array (FDA),
- Reinforcement Learning (RL),
- Game theory,
- Power allocation

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References(20)

References

[1]	ANTONIK P, WICKS M C, GRIFFITHS H D, et al. Frequency diverse array radars[C]. The 2006 IEEE Conference on Radar, Verona, Italy, 2006: 215–217.
[2]	WANG Wenqing. Overview of frequency diverse array in radar and navigation applications[J]. IET Radar, Sonar & Navigation, 2016, 10(6): 1001–1012. doi: 10.1049/iet-rsn.2015.0464
[3]	WANG Wenqing and SHAO Huaizong. Range-angle localization of targets by a double-pulse frequency diverse array radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(1): 106–114. doi: 10.1109/JSTSP.2013.2285528
[4]	DING Zihang, XIE Junwei, WANG Bo, et al. Robust adaptive null broadening method based on FDA-MIMO radar[J]. IEEE Access, 2020, 8: 177976–177983. doi: 10.1109/ACCESS.2020.3025602
[5]	XU Jingwei, LIAO Guisheng, ZHU Shengqi, et al. Joint range and angle estimation using MIMO radar with frequency diverse array[J]. IEEE Transactions on Signal Processing, 2015, 63(13): 3396–3410. doi: 10.1109/TSP.2015.2422680
[6]	WANG Bo, XIE Junwei, ZHANG Jing, et al. Dot-shaped beamforming analysis of subarray-based sin-FDA[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(10): 1429–1444. doi: 10.1631/FITEE.1800722
[7]	XIONG Jie, WANG Wenqing, SHAO Huaizong, et al. Frequency diverse array transmit beampattern optimization with genetic algorithm[J]. IEEE Antennas Wireless Propagation Letters, 2016, 16: 469–472. doi: 10.1109/LAWP.2016.2584078
[8]	SAMMARTINO P F, BAKER C J, and GRIGGITHS H D. Frequency diverse MIMO techniques for radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 201–222. doi: 10.1109/TAES.2013.6404099
[9]	XU Jingwei, LIAO Guisheng, ZHU Shengqi, et al. Deceptive jamming suppression with frequency diverse MIMO radar[J]. Signal Processing, 2015, 113: 9–17. doi: 10.1016/j.sigpro.2015.01.014
[10]	LAN Lan, XU Jingwei, LIAO Guisheng, et al. Suppression of mainbeam deceptive jammer with FDA-MIMO radar[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 11584–11598. doi: 10.1109/TVT.2020.3014689
[11]	LAN Lan, LIAO Guisheng, XU Jingwei, et al. Transceive beamforming with accurate nulling in FDA-MIMO radar for imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(6): 4145–4159. doi: 10.1109/TGRS.2019.2961324
[12]	XU Jingwei, ZHU Shengqi, and LIAO Guisheng. Space-time-range adaptive processing for airborne radar systems[J]. IEEE Sensors Journal, 2015, 15(3): 1602–1610. doi: 10.1109/JSEN.2014.2364594
[13]	XU Jingwei, LIAO Guisheng, HUANG Lei, et al. Robust adaptive beamforming for fast-moving target detection with FDA-STAP radar[J]. IEEE Transactions on Signal Processing, 2017, 65(4): 973–984. doi: 10.1109/TSP.2016.2628340
[14]	赫彬, 苏洪涛. 认知雷达抗干扰中的博弈论分析综述[J]. 电子与信息学报, 2021, 43(5): 1199–1211. doi: 10.11999/JEIT200843 HE Bin and SU Hongtao. A review of game theory analysis in cognitive radar anti-jamming[J]. Journal of Electronics &Information Technology, 2021, 43(5): 1199–1211. doi: 10.11999/JEIT200843
[15]	吴家乐, 时晨光, 周建江. 博弈论在雷达系统中的应用研究综述[J]. 飞航导弹, 2021(9): 59–66. WU Jiale, SHI Chenguang, and ZHOU Jianjiang. A review of game theory application in radar system[J]. Aerodynamic Missile Journal, 2021(9): 59–66.
[16]	SONG Xiufeng, WILLETT P, ZHOU Shengli, et al. The MIMO radar and jammer games[J]. IEEE Transactions on Signal Process, 2012, 6(2): 687–699. doi: 10.1109/TSP.2011.2169251
[17]	DELIGIANNIS A, PANOUI A, LAMBOTHARAN S, et al. Game-theoretic power allocation and the NASH equilibrium analysis for a multistatic MIMO radar network[J]. IEEE Transactions on Signal Processing, 2017, 65(24): 6397–6408. doi: 10.1109/TSP.2017.2755591
[18]	GODRICH H, PETROPULU A P, and POOR H V. Power allocation strategies for target localization in distributed multiple-radar architectures[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3226–3240. doi: 10.1109/TSP.2011.2144976
[19]	DING Zihang and XIE Junwei. Joint transmit and receive beamforming for cognitive FDA-MIMO radar with moving target[J]. IEEE Sensors Journal, 2021, 21(18): 20878–20885. doi: 10.1109/JSEN.2021.3100332
[20]	LUO Zhiquan, MA W K, SO A M C, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20–34. doi: 10.1109/MSP.2010.936019