相控阵雷达长时跟踪波束调度与波形优化策略

刘一鸣; 盛文; 胡冰; 张磊

doi:10.7527/S1000-6893.2019.23519

航空学报 >

2020 , Vol. 41 >Issue 3: 323519 - 323519

DOI: https://doi.org/10.7527/S1000-6893.2019.23519

电子电气工程与控制

相控阵雷达长时跟踪波束调度与波形优化策略

刘一鸣 ,
盛文 ,
胡冰 ,
张磊

展开

空军预警学院防空预警装备系, 武汉 430019

收稿日期: 2019-09-23

修回日期: 2019-12-13

网络出版日期: 2019-12-12

基金资助

军内预研基金

收起

Long time tracking beam scheduling and waveform optimization strategy for phased array radar

LIU Yiming ,
SHENG Wen ,
HU Bing ,
ZHANG Lei

Expand

Air Defense Early Warning Equipment Department, Air Force Early Warning Academy, Wuhan 430019, China

Received date: 2019-09-23

Revised date: 2019-12-13

Online published: 2019-12-12

Supported by

Military Advanced Research Fund

Fold

摘要

针对相控阵雷达多目标跟踪波束调度和波形参数优化控制的问题，本文提出了一种基于马尔可夫决策过程（MDP）的相控阵雷达跟踪波束调度与波形参数优化策略，该方法以无迹卡尔曼滤波（UKF）算法为基础来估计目标的状态。首先将本文的序列决策问题建模为马尔可夫决策过程，定义了资源的效费比和长期回报率，然后与当前实际跟踪误差综合考虑作为MDP的回报函数，进而给出了调度的优化模型，最后将长时决策问题转化为动态规划算法结构进行求解，并且提出了一种并行混合遗传粒子群优化算法来求解各决策时刻的最优策略。仿真结果表明了长时策略的先进性以及寻优算法的优越性，与传统的短时策略相比，跟踪精度可提高11.17%。

关键词： 相控阵雷达; 波束调度; 波形参数优化; 马尔可夫决策过程; 无迹卡尔曼滤波(UKF); 长期回报率; 混合遗传粒子群优化

本文引用格式

刘一鸣 , 盛文 , 胡冰 , 张磊 . 相控阵雷达长时跟踪波束调度与波形优化策略[J]. 航空学报, 2020 , 41(3) : 323519 -323519 . DOI: 10.7527/S1000-6893.2019.23519

Abstract

Aiming at the problem of multi-target tracking beam scheduling and waveform parameter optimization control of phased array radar, a strategy of tracking beam scheduling and waveform parameter optimization based on Markov Decision Process (MDP) is proposed. The Unscented Kalman Filter (UKF) algorithm is used to estimate the state of the target. Firstly, the sequence decision problem of this paper is modeled as a Markov decision process, and the cost-effectiveness ratio and the long-term return rate of the resource are defined. Then, the current actual tracking error is intigrated as the reward function of MDP, and the optimization model of joint scheduling is given. Finally, the long-term decision problem is transformed into a dynamic programming algorithm structure, and a parallel hybrid genetic particle swarm optimization algorithm is proposed to solve the optimal strategy at each decision time. The simulation result shows the advanced nature of the strategy and the superiority of the optimization algorithm. Compared with the traditional "short-term" strategy, the tracking accuracy can be improved by 11.17%.

Key words： phased array radar; beam scheduling; waveform parameter optimization; Markov decision process; Unscented Kalman Filter (UKF); long-term return rate; hybrid genetic particle swarm optimization

参考文献

[1] MCLNTYRE G A, HINTZ K J. Sensor measurement scheduling an enhanced dynamic preemptive algorithm[J]. Optical Engineering, 1998, 37(2):517-523.
[2] 刘先省,申石磊,潘泉, 等. 基于信息熵的一种传感器管理算法[J]. 电子学报, 2000(9):39-41. LIU X X, SHEN S L, PAN Q, et al. A sensor management algorithm based on information entropy[J]. Journal of Electronic Science, 2000(9):39-41 (in Chinese).
[3] KALANDROS M, PAO L Y. Covariance control for multi-sensor systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(4):1138-1157.
[4] 周文辉,胡卫东,余安喜, 等. 基于协方差控制的集中式传感器分配算法研究[J]. 电子学报, 2003, 31(12A):2158-2162. ZHOU W H, HU W D, YU A X, et al. Research on centralized sensor allocation algorithm based on covariance control[J]. Journal of Electronic Science, 2003, 31(12A):2158-2162 (in Chinese).
[5] HERNANDEZ M L, KIRUBARAJAN T, BRASHALOM Y. Multi-sensor resource deployment using posterior Cramer-Rao bounds[J]. IEEE Transactions on Aerospace Electronic Systems, 2004, 40(2):399-416.
[6] 胡卫东, 郁文贤, 卢建斌, 等.相控阵雷达资源管理的理论与方法[M]. 北京:国防工业出版社, 2010:142-185. HU W D, YU W X, LU J B, et al. Theory and method of phased array radar resource management[M]. Beijing:National Defense Industry Press, 2010:142-185 (in Chinese).
[7] 卢建斌, 胡卫东, 郁文贤. 基于协方差控制的相控阵雷达资源管理算法[J]. 电子学报, 2007, 35(3):402-408. LU J B, HU W D, YU W X. Phased array radar resource management algorithm based on covariance control[J]. Journal of Electronic Science, 2007, 35(3):402-408 (in Chinese).
[8] ZHANG H W, XIE J W, SHI J P, et al. Joint beam and waveform selection for the MIMO radar target tracking[J]. Signal Processing, 2018, 156:31-40.
[9] 卢建斌,肖慧,席泽敏, 等. 相控阵雷达波束波形联合自适应调度算法[J]. 系统工程与电子技术, 2011, 33(1):84-88. LU J B, XIAO H, XI Z M, et al. Phased array radar beam and waveform combined adaptive scheduling algorithm[J]. Systems Engineering and Electronic Technology, 2011, 33(1):84-88 (in Chinese).
[10] CHENG T, LI S Y, ZHANG J. Adaptive resource management in multiple targets tracking for co-located multiple input multiple output radar[J]. IET Radar Sonar Navigation, 2018, 12(9):1038-1045.
[11] 张贞凯, 汪飞, 周建江, 等. 多目标跟踪中自适应时间资源调度[J]. 航空学报, 2011, 32(3):522-530. ZHANG Z K, WANG F, ZHOU J J, et al. Adaptive time resource scheduling in multi objective tracking[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(3):522-530 (in Chinese).
[12] 喻晨龙, 李凡, 谭贤四, 等. 基于TAS模式的多目标跟踪波束调度策略[J]. 系统工程与电子技术, 2017, 39(7):1445-1450. YU C L, LI F, TAN X S, et al. Multi-target tracking beam scheduling strategy based on TAS mode[J]. Systems Engineering and Electronic Technology, 2017, 39(7):1445-1450 (in Chinese).
[13] YAN J K, LIU H W, JIU B, et al. Simultaneous multibeam resource allocation scheme for multiple target tracking[J]. IEEE Transactions on Signal Processing, 2015, 63(12):3110-3122.
[14] YAN J K, LIU H W, PU W Q, et al. Joint beam selection and power allocation for multiple target tracking in netted colocated MIMO radar system[J]. IEEE Transactions on Signal Processing, 2016, 64(24):6417-6427.
[15] XIE M C, YI W, KINBARAJAN 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.
[16] 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. WANG X L, YI W, KONG L J. Phased array radar beam and dwell time joint allocation method based on multi-target tracking[J]. Journal of Radar, 2017, 6(6):602-610 (in Chinese).
[17] WANG X L, YI W, XIE M C, et al. Time management for target tracking based on the predicted Bayesian Cramer-Rao lower bound in phase array radar system[C]//20th International Conference on Information Fusion. Piscataway, NJ:IEEE Press, 2017:1-5.
[18] XIAO J M, LIU W R, HE Y, et al. Energy-efficient sensor scheduling scheme for target tracking in wireless sensor networks[C]//26th Chinese Control and Decision Conference. Piscataway, NJ:IEEE Press, 2014:1869-1874.
[19] ZHOU L, TANG H, LI H Z, et al. Dynamic power management strategies for a sensor node optimised by reinforcement learning[J]. International Journal of Computational Science and Engineering, 2016, 13(1):24-36.
[20] LI Y Z, ZHANG F, QUEVEDO D E, et al. Power control of an energy harvesting sensor for remote state estimation[J]. IEEE Transactions on Automatic Control, 2017, 62(1):277-290.
[21] ZHANG F, JING T, HUO Y, et al. Joint spectrum access and transmission power management for energy harvesting cognitive radio sensor networks[J]. International Journal of Sensor Networks, 2018, 27(2):103-115.
[22] 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. QIN T, DAI F Z, LIU H W. Time resource management algorithm for fire control phased array radar[J]. Systems Engineering and Electronic Technology, 2016, 38(3):545-550 (in Chinese).
[23] ZHANG Z K, TIAN Y B. A novel resource scheduling method of netted radars based on Markov decision process during target tracking in clutter[J]. EURASIP Journal on Advances in Signal Processing, 2016, 16:1-9.
[24] CAYIR O, CANDAN C. Performance improvement of time-balance radar schedulers through decision policies[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(4):1679-1691.
[25] HAUG A J. Bayesian estimation and tracking a practical guide[M]. Hoboken:John Wiley & Sons, Inc., 2012:145-165.
[26] LIU Z G, WANG J K, XUE Y B. PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks[J]. Future Generation Computer Systems, 2013, 29(7):1751-1757.
[27] 张浩为, 谢军伟, 张昭建, 等. 基于混合遗传-粒子群算法的相控阵雷达调度方法[J]. 系统工程与电子技术, 2017, 39(9):1985-1991. ZHANG H W, XIE J W, ZHANG Z J. Phased array radar scheduling method based on hybrid genetic-particle swarm optimization[J]. Systems Engineering and Electronic Technology, 2017, 39(9):1985-1991 (in Chinese).
[28] JIANG H Q, ZHANG Y R, XU H Y. Optimal allocation of cooperative jamming resource based on hybrid quantum-behaved particle swarm optimisation and genetic algorithm[J]. IET Radar, Sonar & Navigation, 2017, 11(1):185-192.
[29] ZHU M X, LI J C, CHANG D G, et al. Optimization of antenna array deployment for partial discharge localization in substations by hybrid particle swarm optimization and genetic algorithm method[J]. Energies, 2018, 11(7):1-18.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献