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
[ "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" ]
收稿:2021-07-15,
修回:2021-11-01,
纸质出版:2022-11-25
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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.
WANG Hong-yan,ZHOU He.Transceiver Space-Time Resource Allocation Method to Improve the Detection Performance of MIMO-STAP[J].ACTA ELECTRONICA SINICA,2022,50(11):2619-2628.
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. DOI: 10.12263/DZXB.20210927.
WANG Hong-yan,ZHOU He.Transceiver Space-Time Resource Allocation Method to Improve the Detection Performance of MIMO-STAP[J].ACTA ELECTRONICA SINICA,2022,50(11):2619-2628. DOI: 10.12263/DZXB.20210927.
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.
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.
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