A novel robust adaptive beamforming algorithm based on a reconstructed covariance matrix is proposed to combat the desired signal look direction error. First, the iterative adaptive approach (IAA) algorithm is performed to acquire the true signal direction and the spatial spectrum simultaneously. Secondly, the IAA spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix. Compared with other reconstruction-based algorithms, this approach is capable of dealing with coherent interferences without any optimisation procedures.Introduction: The traditional adaptive beamforming technique suffers from severe performance degradation when the signal-of-interest (SOI) appears in the training data [1]. Recently, a new robust adaptive beamforming (RAB) algorithm was proposed based on interference-plus-noise covariance matrix (INCM) reconstruction and steering vector (SV) estimation [2]. This approach presents outstanding performance by removing the SOI from the sample covariance matrix. Based on this method, some modified algorithms have been proposed [3,4]. However, for these reconstruction-based algorithms [2-4], the desired signal direction-of-arrival (DOA) must be found first by a low-resolution DOA estimation algorithm. Then, the INCM can be reconstructed by adopting Capon spatial spectrum. Hence, two DOA estimation algorithms are employed during this process. Besides, the accuracy of Capon spatial spectrum degrades severely when coherent interference exists. For the subspace-based DOA estimation methods, their spatial spectrum only represents the orthogonality of different subspaces and should not be used to reconstruct the INCM.In this Letter, we propose a new INCM reconstruction approach based on the iterative adaptive approach (IAA). The IAA can deal with coherent sources, array perturbations and finite-sampling effects. Besides, it has high accuracy of DOA and power estimation [5,6]. Therefore, the processing results of IAA can be used both in SOI DOA estimation and the INCM reconstruction. Simulation results prove that the proposed method is better than other adaptive beamforming algorithms when the coherent interference and SOI DOA mismatch exist.

This letter proposes a signal processing method of passive bistatic radar (PBR) exploiting an uncooperative radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution. Two channels are needed in PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and a simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the uncooperative radar. The procedure of the proposed method is explained in detail, which is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target angle and bistatic range measurement successfully.Index Terms-Passive bistatic radar (PBR), signal reconstruction, uncooperative radar, wideband beamforming. 1545-598X

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A mainlobe interference suppression method is proposed in this paper, which can still work when the signal of interest (SOI) is present in the training data. In this method, the iterative adaptive approach (IAA) is applied to spatial spectrum estimation at first. Then, IAA spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix (INCM). Next, the eigenvector associated with mainlobe interference in INCM is determined, and the eigen-projection matrix can be calculated to suppress the mainlobe interference. Meanwhile, the sidelobe-interference-plus-noise covariance matrix (SINCM) can be reconstructed. Finally, the adaptive weight vector is obtained. One main advantage is that the proposed method can deal with coherent mainlobe interference and sidelobe interferences simultaneously. Simulation results demonstrate the effectiveness and robustness of the proposed method.