In this letter, a novel robust adaptive beamforming algorithm is addressed to improve the robustness against steering vector random errors (SVREs), which eliminates the signal of interest (SOI) component from the sample covariance matrix (SCM), based on interference-plus-noise covariance matrix (IPNCM) reconstruction over annulus uncertainty sets. Firstly, several annulus uncertainty sets are used to constrain the steering vectors (SVs) of both interferences and the SOI. Additionally the IPNCM is reconstructed according to its definition by estimating each interference SV over its own annulus uncertainty set via the subspace projection algorithm. Meanwhile, the SOI SV is estimated as the prime eigenvector of the SOI covariance matrix term calculated over its own annulus uncertainty set. Finally, a novel robust beamformer is formulated based on the new IPNCM and the SOI SV, and it outperforms other existing reconstruction-based beamformers when the SVREs exist, especially in low input signal-to-noise ratio (SNR) cases, which is proved through the simulation results.

In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.

Recently, Gan and Luo have proposed a direction-of-arrival estimation method for uncorrelated and coherent signals in the presence of multipath propagation [3]. In their method, uncorrelated and coherent signals are distinguished by rotational invariance techniques and the property of the moduli of eigenvalues. However, due to the limitation of finite number of sensors, the pseudo-inverse matrix derived in this method is an approximate one. When the number of sensors is small, the approximation error is large, which adversely affects the property of the moduli of eigenvalues. Consequently, the method in [3] performs poorly in identifying uncorrelated signals under such circumstance. Moreover, in cases of small number of snapshots and low signal to noise ratio, the performance of their method is poor as well. Therefore, in this letter we first study the approximation in [3] and then propose an improved method that performs better in distinguishing between uncorrelated signals and coherent signals and in the aforementioned two cases. The simulation results demonstrate the effectiveness and efficiency of the proposed method.

We propose a novel sparse representation-based direction-of-arrival (DOA) estimation method. In contrast to those that approximate l0-norm minimization by l1-norm minimization, our method designs a reweighted l1 norm to substitute the l0 norm. The capability of the reweighted l1 norm to bridge the gap between the l0- and l1-norm minimization is then justified. In addition, an array covariance vector without redundancy is utilized to extend the aperture. It is proved that the degree of freedom is increased as such. The simulation results show that the proposed method performs much better than l1-type methods when the signal-to-noise ratio (SNR) is low and when the number of snapshots is small.

Radio signals show characteristics of minute differences, which result from various idiosyncratic hardware properties between different radio emitters. A robust detector based on exponentially weighted distances is proposed to detect the exact reference instants of the burst communication signals. Based on the exact detection of the reference instant, in which the radio emitter finishes the power-up ramp and enters the first symbol of its preamble, the features of the radio fingerprint can be extracted from the transient signal section and the steady-state signal section for radiometric identification. Experiments on real data sets demonstrate that the proposed method not only has a higher accuracy that outperforms correlation-based detection, but also a better robustness against noise. The comparison results of different detectors for radiometric identification indicate that the proposed detector can improve the classification accuracy of radiometric identification.

We address a robust algorithm for the interference-plus-noise covariance matrix reconstruction (RA-INCMR) against random arbitrary steering vector mismatches (RASVMs) of the interferences, which lead to substantial degradation of the original INCMR beamformer performance. Firstly, using the worst-case performance optimization (WCPO) criteria, we model these RASVMs as uncertainty sets and then propose the RA-INCMR to obtain the robust INCM (RINCM) based on the Robust Capon Beamforming (RCB) algorithm. Finally, we substitute the RINCM back into the original WCPO beamformer problem for the sample covariance matrix to formulate the new RA-INCM-WCPO beamformer problem. Simulation results demonstrate that the performance of the proposed beamformer is much better than the original INCMR beamformer when there exist RASVMs, especially at low signal-to-noise ratio (SNR).