In this paper, we present a subspace projection and covariance matrix reconstruction (SPCMR) algorithm for adaptive beamforming to improve the robustness against large SV mismatch. The SPCMR algorithm consists of two parts: projection subspaces estimation and interference-plus-noise covariance matrix (INCM) reconstruction. Specifically, we estimate two projection subspaces containing the signal component and obtain the signal SV from their intersection. The first projection subspace is estimated from the constructed signal covariance matrix via the distortionless responses principle. The second one is gotten according to the subspace proximity between the nominal signal SV and the eigenvectors of the sample covariance matrix. Subsequently, the interference SVs are estimated by using the Capon spatial estimator, and each interference power is obtained via the oblique projectors. After that, an accurate INCM is reconstructed, and the SPCMR beamformer is proposed. The simulation results show that the SPCMR algorithm is robust to several model mismatches and outperforms other adaptive algorithms.
Space-time steering vector mismatch of targets usually happens in the space-time adaptive processing (STAP) technique, causing space-time beam distortion. Due to the effects of clutter spectrum broadening and jamming, performance of the conventional STAP method deteriorates dramatically. This paper proposes a novel robust STAP method to improve the performance of clutter suppression and moving target detection. The proposed method employs the alternating projection and clutter cancellation method, to reconstruct the clutter plus jamming plus noise covariance matrix (CJNCM) and re-estimate the target steering vector. Firstly, reconstruction of CJNCM consists of summing the eigenvectors of the projection operator, combining the sample covariance matrix and the integral covariance matrix (ICM) of specific region. Secondly, the re-estimated target steering vector is obtained by estimating the dominant eigenvector of the ICM of the Region of Interest (ROI), which contains the target signal purely because CJNCM is cancelled before estimation. Finally, the robust space-time adaptive filtering weight vector is calculated through the MVDR method with the reconstructed CJNCM and the re-estimated target steering vector. Simulation results indicate that the proposed algorithm shows robust performance against space-time steering vector mismatch, better output signal-to-noise ratio performance and better moving target detection performance than the traditional robust STAP method. INDEX TERMS Space-time adaptive processing, space-time steering vector mismatch, covariance matrix reconstruction, alternating projection, clutter cancellation.
This paper presents a beampattern synthesis scheme based on the single-point array response control with minimum pattern deviation (SPARC-MPD) method. The central concept of SPARC-MPD stems from adaptive array theory, and a closedform expression of the designed weight vector is devised. Comparing to the state-of-the-art methods like the accurate array response control (A 2 RC) approach, the SPARC-MPD method addresses the pattern distortion issue. Moreover, the SPARC-MPD method can realize accurate magnitude response control at one single direction and minimize the pattern deviation at any other direction simultaneously, leading to a faster convergence speed. Simulation results show the effectiveness of the SPARC-MPD method and the efficiency of the pattern synthesis scheme.
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