一种稳健的互质空间步进频雷达参数估计方法OA

The coprime spatial stepped-frequency radar effectively enhances both the angular and range resolution by constructing a virtual aperture and synthesizing an equivalent large bandwidth.However,conventional methods that vectorize the sample covariance matrix to obtain virtual array data will distort the inherent structure of the received sig-nal across the transmit-receive-snapshot dimensions,leading to information loss.Although the method based on parallel factor decomposition(PARAFAC)preserve the three-dimensional structure of the signal,its decomposition errors are amplified during the subsequent coprime array virtualization,which negatively impacting the parameter estimation per-formance.To address this issue,this paper proposes a robust parameter estimation method based on Hankel matrix re-construction.Firstly,a third-order tensor model of the received signal is constructed,and the snapshot-dimensional low-rank approximation is introduced to reduce the computational complexity.Then,to mitigate the amplification of ten-sor decomposition errors,the estimated virtual steering vectors are reconstructed into Hankel matrices and refined via truncated singular value decomposition(TSVD),effectively correcting estimation bias.Simulation results demonstrate that,under low signal-to-noise ratio and small snapshot conditions,the proposed method outperforms 2D-SSMUSIC,PARAFAC,and HOSVD-RDMUSIC algorithms in terms of estimation accuracy and robustness,while significantly re-ducing the computational complexity.