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• Compressive sensing for SAR image formation. • Block compressive sensing (BCS) for SAR imaging. • Performance evaluation of CS-SAR algorithms by real SAR data. • A novel block compressive sensing algorithm. Compressive Sensing (CS) theory has been used for Synthetic Aperture Radar (SAR) imaging due to the sparsity feature of SAR images. Therefore, some well-known CS algorithms like Orthogonal Matching Pursuit (OMP) and Regularized OMP (ROMP) methods have been employed for SAR image formation with a very small number of samples. On the other hand, it has been shown that the SAR signal is consistent with the definition of block sparsity. Hence, compressive sensing methods employing block structure, known as Block Compressive Sensing (BCS), are presented and used for SAR image formation to achieve more accuracy with a smaller number of samples. In this paper, first, a new BCS-based algorithm, namely, Block Norm Regularized Orthogonal Matching Pursuit (BNROMP), is introduced which can be used in all BCS applications. Then, this novel method is used for SAR image formation to achieve more accuracy and excellent resolution with a small number of samples. The simulation results for the synthesized data, as well as real data, show that by using the novel BNROMP method, we could form SAR images with higher quality, as compared to those for the standard image formation algorithms and other CS-SAR or BCS-SAR methods. [ABSTRACT FROM AUTHOR]

The problem of transmit code design in multiple-input multiple-output (MIMO) radar systems is addressed in this paper. The problem is considered in both colocated and widely separated antenna radars in the presence of signal-dependent interference and target mobility. Due to complexity of exact expressions of the optimal detector in the MIMO radars, information theoretic criteria are employed as design metrics which results in a general form of nonconvex optimization problems. In order to tackle the problem, a novel technique based on the minorization-maximization algorithm is proposed and solutions are presented under practical constraints on the transmit code, namely energy constraint, peak-to-average-power ratio constraint, and similarity constraint. Furthermore, the devised method is extended to be robust against uncertainties of the clutter and interference statistics. Finally, numerical examples are used to show the performance of the proposed technique in different situations. [ABSTRACT FROM PUBLISHER]

Multiple pulse repetition frequency (PRF) radars are used for increasing the maximum unambiguous range and Doppler frequency. Because of the nonuniform transmitted pulses, special techniques are required for spectrum analysis of the received signal in these radars. An algorithm for spectrum analysis of multiple PRF radars is presented here. The algorithm is based on the compressive sensing (CS) theory and uses the sparse characteristic of received signal in frequency domain and universal sampling pattern of multiple PRF pulses for determining the Doppler frequencies of targets. Moreover, a CS-based algorithm for blind estimation of the number of targets is presented. The proposed algorithms work properly in low sampling rates and in the presence of multiple targets and Gaussian clutter. Simulation results are presented for illustrating high performance of the algorithms. [ABSTRACT FROM AUTHOR]