Joint DOA and DOD estimation of bistatic MIMO radar coherent targets based on smoothing matrix sets optimization
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摘要:
针对在进行相干目标测向时出现的协方差矩阵秩亏现象，双基地多输入多输出(MIMO)雷达探测相干目标会出现精度差、角度分辨率低等问题，提出一种基于分集平滑优化对相干目标波离方向角(DOD)和波达方向角(DOA)进行联合估计的算法。所提算法能通过分集平滑降维协方差矩阵，构造更有效的平滑矩阵集对相干目标进行联合角度估计。将发射阵列和接收阵列进行分集平滑并构建发射子矢量，计算其协方差矩阵；将每个发射子矢量的协方差矩阵进行平滑重构而后进行加权处理得到前向平滑矩阵；对前向平滑矩阵进行共轭翻转等变换后，得到前后向平滑矩阵，再对其进行奇异值(SVD)分解；利用降维多重信号分类(RD-MUSIC)算法对信号进行空间谱估计并实现DOD和DOA自动配对。所提算法有效提高双基地MIMO雷达对相干目标的测向性能，并通过实验证明所提算法的可行性。
Abstract:Due to the rank-deficient phenomenon of the covariance matrix in coherent targets estimation, a algorithm based on diversity smoothing optimization for joint estimation of coherent target wave direction of departure (DOD) angle and direction of arrival (DOA) is proposed to solve the problems of poor accuracy and low angular resolution of coherent target detection by bistatic multiple input multiple output (MIMO) radar. The proposed algorithm can construct a more effective smooth matrix sets for joint angle estimation of coherent targets through diversity smoothing and reducing the covariance matrix. Perform diversity smoothing on the transmit array and receive array, construct transmit sub-vectors, and calculate their covariance matrix. Smooth and reconstruct the covariance matrix of each transmit sub-vector and then perform weighting to obtain a forward smoothing matrix. The forward smoothing matrix is transformed by conjugate flipping and other transformations, and the forward and backward smoothing matrix is obtained, on which the singular value decomposition (SVD) is then performed. The spatial spectrum of the signal is estimated by using the reduced dimension multiple signal classification (RD-MUSIC) algorithm, and the automatic pairing of DOD and DOA is realized. The proposed algorithm effectively improves the direction performance of bistatic MIMO radar for coherent targets, and the feasibility of the proposed algorithm is proved by experiments.
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Key words:
- bistatic radar /
- MIMO radar /
- coherent target /
- diversity smoothing /
- joint angle estimation
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图 3 不同平滑算法角度分辨率比较
Figure 3. Comparison of angular resolution of different smoothing algorithms
图 4 不同平滑算法的归一化空间谱对比
Figure 4. Comparison of angular resolution of different smoothing algorithms
图 5 不同算法的RMSE随SNR和快拍数变化
Figure 5. Variation of RMSE with SNR and snapshot number of different algorithms
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