改善MIMO-STAP检测性能的收发空时资源配置方法

doi:10.12263/DZXB.20210927

电子学报 ›› 2022, Vol. 50 ›› Issue (11): 2619-2628.DOI: 10.12263/DZXB.20210927

改善MIMO-STAP检测性能的收发空时资源配置方法

王洪雁¹^,², 周贺²

^1.浙江理工大学信息学院，浙江杭州 310018
^2.大连大学信息工程学院，辽宁大连 116622

收稿日期:2021-07-15 修回日期:2021-11-01 出版日期:2022-11-25
- 作者简介:
- 王洪雁男,1979年5月生于河南南阳. 2011年毕业于西安电子科技大学获信号与信息处理专业博士学位,现为浙江理工大学信息学院特聘教授、硕士生导师.主要研究方向为阵列信号处理、机器视觉、深度学习等.E-mail: gglongs@163.com
  周贺男,1995年5月生于河南周口. 现为大连大学信息工程学院控制科学与工程专业硕士研究生，主要研究方向为阵列信号处理.E-mail: 1738561822@qq.com
- 基金资助:
- 国家自然科学基金(61301258);浙江省自然科学基金重点项目(LZ21F010002);中国博士后科学基金资助项目(2016M590218)

Transceiver Space-Time Resource Allocation Method to Improve the Detection Performance of MIMO-STAP

WANG Hong-yan¹^,², ZHOU He²

^1.School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
^2.College of Information Engineering, Dalian University, Dalian, Liaoning 116622, China

Received:2021-07-15 Revised:2021-11-01 Online:2022-11-25 Published:2022-11-19
- Supported by:
- National Natural Science Foundation of China(61301258);Key Program of National Natural Science Foundation of Zhejiang Province(LZ21F010002);Program supported by China Postdoctoral Science Foundation(2016M590218)

摘要/Abstract

摘要：

针对机载多输入多输出(Multiple-In Multiple-Out，MIMO)雷达资源受限，从而导致目标检测性能下降的问题，提出一种改善基于MIMO雷达的空时自适应处理(Space Time Adaptive Processing，STAP)检测性能的收发空时资源配置(Transceiver Space-Time Resource Allocation，TST-RA)方法. 所提方法利用所构建包含待配置收发空时资源的MIMO-STAP模型，基于最大化输出信杂噪比(Signal-Clutter-Noise Ratio，SCNR)准则，构造收发阵元、发射脉冲、基带波形以及接收权值联合设计问题. 为求解所得复杂非线性联合优化问题，基于交替迭代策略将其分解为相互独立的子问题，而后利用如下方法高效求解各子问题：基于逐次凸逼近(Sequential Convex Approximations，SCA)方法选择最优天线脉冲子集，基于半正定规划(SemiDefinite Program，SDP)及随机化方法优化发射波形，基于最小方差无畸变响应(Minimum Variance Distortionless Response，MVDR)准则设计接收权，从而获得联合优化问题的有效求解. 仿真结果验证了所提算法的有效性.

关键词: 多输入多输出雷达, 空时自适应处理, 空时资源配置, 阵元脉冲选择, 逐次凸逼近, 半正定规划

Abstract:

Focusing on the issue that the resource of airborne multiple input multiple output(MIMO) radar is constrained such that the target detection performance is degraded, a transceiver space-time resource allocation(TST-RA) approach is developed in this paper to improve the detection performance of MIMO radar based space time adaptive processing(STAP). By exploiting the constructed MIMO-STAP model containing the transceiver space-time resources to be allocated, with the criterion of maximizing the output signal-clutter-noise ratio(SCNR), a joint design model of transceiver array element, transmitting pulse, baseband waveform and receiving weight is formulated via the developed. In order to solve the resultant complex nonlinear joint optimization issue, it is decomposed into independent sub-problems on the basis of the alternating iteration strategy, and then the following method can be used to efficiently solve each sub-problem: the optimal antenna pulse subset is selected via employing the successive convex approximation(SCA) method, the transmitting waveform can be optimized by exploiting the semi-positive definite programming(SDP) and randomization approach, and the receiving weight is designed on the basis of the criterion of minimum variance distortionless response(MVDR), thus the effective solution of the joint optimization problem can be acquired. Simulation results show the effectiveness of the proposed algorithm.

Key words: multiple-input multiple-out(MIMO) radar, space time adaptive processing(STAP), space-time resource allocation, element and pulse selection, successive convex approximation(SCA), semidefinite program(SDP)

中图分类号:

TN957

王洪雁, 周贺. 改善MIMO-STAP检测性能的收发空时资源配置方法[J]. 电子学报, 2022, 50(11): 2619-2628.

Hong-yan WANG, He ZHOU . Transceiver Space-Time Resource Allocation Method to Improve the Detection Performance of MIMO-STAP[J]. Acta Electronica Sinica, 2022, 50(11): 2619-2628.

图/表 7

图1 机载MIMO雷达模型

图2 所得波形实、虚部分布及波束方向图(a)所得波形实、虚部分布 (b)所得波束方向图

图3 收发空时资源对输出SCNR的影响

图4 收发阵元及脉冲对输出SCNR影响(a)发射阵元及脉冲数对输出SCNR影响　(b)接收阵元数对输出SCNR影响

图5 TST-RA对输出SCNR及改善因子的影响(a)SCNR随SNR变化曲线 (b)SCNR随CNR变化曲线 (c)各算法改善因子比较

图6 最优收发阵元脉冲位置分布

图7 输出SCNR随迭代次数变化曲线

参考文献 30

1	LIH, ZHOUS, XIEP, et al. Waveform optimization for multiple beam communications under radar constant modulus constraints[C]//2021 IEEE Wireless Communications and Networking Conference(WCNC). Nanjing: IEEE, 2021: 1‐6.
2	WENC, PENGJ, ZHOUY, et al. Enhanced three-dimensional joint domain localized STAP for airborne FDA-MIMO radar under dense false-target jamming scenario[J]. IEEE Sensors Journal, 2018, 18(10): 4154‐4166.
3	HAMZAS A, AMINM G. Sparse array design utilizing matrix completion[C]//2019 53rd Asilomar Conference on Signals, Systems, and Computers. Pacific Grove: IEEE, 2019: 1207‐1211.
4	HAMZAS A, ZHAIW, WANGX, et al. Sparse array transceiver design for enhanced adaptive beamforming in MIMO radar[C]//ICASSP 2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto: IEEE, 2021: 4410‐4414.
5	JOSHIS, BOYDS. Sensor selection via convex optimization[J]. IEEE Transactions on Signal Processing, 2009, 57(2): 451‐462.
6	TOHIDIE, COUTINOM, CHEPURIS P, et al. Sparse antenna and pulse placement for colocated MIMO radar[J]. IEEE Transactions on Signal Processing, 2019, 67(3): 579‐593.
7	WANGX, ABOUTANIOSE, AMINM. Reduced-rank STAP for slow-moving target detection by antenna-pulse selection[J]. IEEE Signal Processing Letter, 2015, 22(8): 1156‐1160.
8	WANGX, ABOUTANIOSE, AMINM. Slow radar target detection in heterogeneous clutter using thinned space-time adaptive processing[J]. IET Radar, Sonar & Navigation, 2016, 10(4): 726‐734.
9	TANGB, TANGJ. Joint design of transmit waveforms and receive filters for MIMO radar space-time adaptive processing[J]. IEEE Transactions on Signal Processing, 2016, 64(18): 4707‐4722.
10	AUBRYA, MAIOA D, NAGHSHM M. Optimizing radar waveform and doppler filter bank via generalized fractional programming[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1387‐1399.
11	LIJ, LIAOG, HUANGY, et al. MIMO-STAP based cognitive design of transmitted waveforms and receive filters for clutter suppression[C]//2018 IEEE Radar Conference (RadarConf18). Oklahoma: IEEE, 2018: 1439‐1444.
12	WANGY, LIUH, LUOZ. Iterative design of MIMO radar transmit waveforms and receive filter bank[C]//2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas: IEEE, 2010: 2770‐2773.
13	WANGH, PEIB, WANGZ, et al. Robust waveform optimization for MIMO radar to improve the worst-case detection performance[C]//2014 IEEE Radar Conference. Cincinnati: IEEE, 2014: 1098‐1101.
14	MAIOA D, NICOLAS D, HuangY, et al. Code design for radar STAP via optimization theory[J]. IEEE Transactions on Signal Processing, 2010, 58(2): 679‐694.
15	CHENGZ, HEZ, ZHANGS, et al. Constant modulus waveform design for MIMO radar transmit beampattern[J]. IEEE Transactions on Signal Processing, 2017, 65(18): 4912‐4923.
16	CUIG, YUX, CAROTENUTOV, et al. Space-time transmit code and receive filter design for collocated MIMO radar[J]. IEEE Transactions on Signal Processing, 2017, 65(5): 1116‐1129.
17	STOICAP, SELENY. Cyclic minimizers, majorization techniques, and the expectation-maximization algorithm: a refresher[J]. IEEE Signal Processing Magazine, 2004, 21(1): 112‐114.
18	CAPONJ. High resolution frequency-wavenumber spectrum analysis[J]. IEEE Proceeding, 1969, 57(8): 1408‐1418.
19	WANGH. Robust Waveform optimization for MIMO-OFDM-based STAP in the presence of environmental uncertainty[J]. Circuits Syst Signal Processing:2019, 38(3): 1301‐1317.
20	STOICAP, LIJ, ZHUX, et al. On using a priori knowledge in space-time adaptive processing[J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2598‐2602.
21	TANGB, TANGJ, PENGY, et al. Estimation of transition range bin in clutter edge for space time adaptive processing[C]//Proceedings of 2011 IEEE CIE International Conference on Radar. Chengdu: IEEE, 2011, 688‐691.
22	WANGX, WANGH, PEIB. Robust waveform design for MIMO-OFDM-STAP with imperfect clutter prior knowledge[C]//2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). Chennai: IEEE, 2016: 1849‐1852.
23	张贤达. 矩阵分析与应用[M]. 北京: 清华大学出版社, 2011: 271‐278.
	ZHANGX D. Matrix Analysis and Application[M]. Beijing: Tsinghua University Press, 2011: 271‐278. (in Chinese)
24	LIW, ZHANGH, LIANGX. A sequential convex approximation algorithm for portfolio optimization model[J]. Journal of Dalian University of Technology, 2017, 57(3): 321‐326.
25	ADVE, RAVIRAJS. Design of multiple near-orthogonal spectrally-compliant waveforms via alternating successive convex approximations and projections[J]. IET Radar, Sonar & Navigation, 2019, 13(5): 781‐788.
26	AUBRYA, MAIOA D, PIEZZOM, et al. Cognitive design of the transmitted phase code and receive filter in reverberating environment[C]//2012 International Waveform Diversity & Design Conference(WDD). Kauai: IEEE, 2012: 085‐090.
27	IMANIS, NAYEBIM M, GHORASHIS A. Transmit signal design in collocated MIMO radar without covariance matrix optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(5): 2178‐2186.
28	WANGH, PEIB, BAIY. Robust waveform design for MIMO-STAP with imperfect clutter prior knowledge[C]//2014 IEEE International Conference on Signal Processing, Communications and Computing(ICSPCC). Guilin: IEEE, 2014: 578‐581.
29	CUIG, LIH, RANGASWAMYM. Waveform design for MIMO radar with constant modulus and similarity constraints[C]//2014 IEEE Radar Conference. Cincinnati, OH: IEEE, 2014: 0354‐0359.
30	CONTINOM, CHEPURIS, LEUSG. Near-optimal greedy sensor selection for MVDR beamforming with modular budget constraint[C]//2017 25th European Signal Processing Conference(EUSIPCO). Kos: IEEE, 2017: 1981‐1985.

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改善MIMO-STAP检测性能的收发空时资源配置方法

Transceiver Space-Time Resource Allocation Method to Improve the Detection Performance of MIMO-STAP

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