Recently, a robust adaptive beamforming (RAB) technique based on interference-plus-noise covariance (INC) matrix reconstruction has been proposed, which utilizes the Capon spectrum estimator integrated over a region separated from the direction of the desired signal. Inspired by the sampling and reconstruction idea, in this paper, a novel method named spatial power spectrum sampling (SPSS) is proposed to reconstruct the INC matrix more efficiently, with the corresponding beamforming algorithm developed, where the covariance matrix taper (CMT) technique is employed to further improve its performance. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Abstract-An effective sparse array extension method for maximizing the number of consecutive lags in the fourth-order difference co-array is proposed, leading to a novel enhanced sparse array structure based on co-prime arrays with significantly increased number of degrees of freedom (DOFs). One method to exploit the increased DOFs based on non-stationary signals is also proposed, with simulation results provided to demonstrate the effectiveness of the proposed structure.

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A wideband off-grid model is proposed to represent dictionary mismatch under the compressive sensing framework exploiting difference co-arrays. A group sparsity based off-grid method is proposed for underdetermined wideband direction of arrival (DOA) estimation which provides improved performance over the existing group sparsity based method with a same search grid. A two-step approach is then proposed which achieves an even better performance with significantly reduced computational complexity.

A critical analysis of the canonical correlation analysis (CCA) approach in blind source separation (BSS) is provided. It is proved that by maximizing the autocorrelation functions of the recovered signals we can separate the source signals successfully. It is further shown that the CCA approach represents the same class of generalized eigenvalue decomposition (GEVD) problems as the matrix pencil method. Finally, online realizations of the CCA approach are discussed with a linear-predictor-based algorithm studied as an example.

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Abstract-Two challenges have been faced in signal processing of ultra-high resolution space-borne synthetic aperture radar (SAR). The first challenge is constructing a precise range model and the second one is to develop an efficient imaging algorithm since traditional algorithms fail to process ultra-high resolution space-borne SAR data effectively. In this paper, a novel high-order imaging algorithm for high resolution space-borne SAR is presented. Firstly, a modified equivalent squint range model (MESRM) is developed by introducing equivalent radar acceleration into the equivalent squint range model, and it is more suitable for high resolution space-borne SAR. The signal model based on the MESRM is also presented. Secondly, a novel high-order imaging algorithm is derived. The insufficient pulse repetition frequency (PRF) problem is solved by an improved sub-aperture method and accurate focusing is achieved through an extended hybrid correlation algorithm. Simulations are performed to validate the presented algorithm.

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