Effect of Sparse Array Geometry on Estimation of Co-array Signal Subspace

This paper considers the effect of sparse array geometry on the co-array signal subspace estimation error for Direction-of-Arrival (DOA) estimation. The second largest singular value of the signal covariance matrix plays an important role in controlling the distance between the true subspace and its estimate. For a special case of two closely-spaced sources impinging on the array, we explicitly compute the second largest singular value of the signal covariance matrix and show that it can be significantly larger for a nested array when compared against a uniform linear array with same number of sensors.

A Tolerant Detection Method for Estimating Robust Signal Subspace with Horizontal Arrays in Uncertain Shallow Ocean Environment

The environmental parameters are usually uncertain in complex shallow ocean environment and restrict the performance of the matching model-like method. Therefore, we need a more tolerant detection method for detecting underwater targets in the uncertain shallow ocean environment. The previous mode-subspace detection method has the characteristics of both high performance and robustness. However, the robust mode-subspace detector is suitable for vertical arrays and its performance is limited by shallow ocean environment. Therefore, we propose the tolerant detection method for estimating the robust signal subspace with horizontal arrays. We estimate the robust signal subspace by bringing uncertain parameters into the observation matrix of a horizontal array. Combined with the robust signal subspace estimation, we propose a subspace detector that tolerates uncertain parameters. The results on simulation in a uncertain shallow ocean environment show that the detector we proposed has a high average detection capability and a certain tolerance for uncertain parameters.

Random Nyström Approach to Clutter Subspace Estimation for Polarimetric Space-Time Adaptive Processing

A random Nyström (R-Nyström) scheme for clutter subspace estimation is proposed in the context of polarimetric space-time adaptive processing (pSTAP). Unlike the standard Nyström scheme making use of only partial columns of the clutter plus noise covariance matrix (CNCM), R-Nyström exploits full CNCM information with a properly designed selection procedure under the newly developed random ridge cross leverage score (RRCLS) criterion. With R-Nyström, sup-ported by the complete CNCM columns, upgraded clutter subspace estimation can be achieved at the expense of an insignificant increase in computational complexity, in contrast to the standard Nyström. The R-Nyström-based pSTAP, termed pR-Nyström, is shown to be superior over the current eigendecomposition-free subspace pSTAP in the signal to clutter plus noise loss and computational complexity. The efficacy of R-Nyström/pR-Nyström is validated by the simulation results.