This paper presents a new approach for the Capon beamformer to provide robustness against array pointing errors. This robustness is achieved by incorporating an uncertainty constraint with diagonal loading and injected pseudo-interference. A simple performance analysis of this new beamformer is also investigated. Simulation results demonstrate that the power estimator has excellent performance.

This paper describes a new high resolution algorithm for the two-dimensional (2-D) frequency estimation problem, which, in particular, is noise insensitive in view of the fact that in many practical applications the contaminated noise may not be white noise. For this purpose, the approach is set in the context of higher-order statistics (HOS), which has demonstrated to be an effective approach under a colored noise environment. The algorithm begins with the consideration of the fourth-order moments of the available 2-D data. Two auxiliary matrices, constituted by a novel stacking of the diagonal slice of the computed fourth-order moments, are then introduced and through which the two frequency components can be precisely determined, respectively, via matrix factorizations along with the subspace rotational invariance (SRI) technique. Simulation results are also provided to verify the proposed algorithm.

A deep null algorithm for adaptive narrowband beamforming in the presence of array gain errors is proposed. This new algorithm not only preserves the desired signal, but also yields superior performance. Simulations confirm this new approach.

In this paper, we present a new state space-based approach for the two-dimensional (2-D) frequency estimation problem which occurs in various areas of signal processing and communication problems. The proposed method begins with the construction of a state space model associated with the noiseless data which contains a summation of 2-D harmonics. Two auxiliary Hankel-block-Hankel-like matrices are then introduced and from which the two frequency components can be derived via matrix factorizations along with frequency shifting properties. Although the algorithm can render high resolution frequency estimates, it also calls for lots of computations. To alleviate the high computational overhead required, a highly parallelizable implementation of it via the principle subband component (PSC) of some appropriately chosen transforms have been addressed as well. Such a PSC-based transform domain implementation not only reduces the size of data needed to be processed, but it also suppresses the contaminated noise outside the subband of interest. To reduce the computational complexity induced in the transformation process, we also suggest that either the transform of the discrete Fourier transform (DFT) or the Haar wavelet transform (HWT) be employed. As a consequence, such an approach of implementation can achieve substantial computational savings; meanwhile, as demonstrated by the provided simulation results, it still retains roughly the same performance as that of the original algorithm.

In this paper, we propose a set of constraints for adaptive broad-band beamforming in the presence of angular errors. We first present spatial and frequency derivative constraints (SFDC) for the design of the quiescent beamformer response. With the wavelet-based blocking matrices, the proposed generalized sidelobe canceller (GSC) preserves the desired signal, and it is less sensitive to the broad-band noise. To make this beamformer more robust to the directional mismatch, we add a pseudo-interference algorithm in the weight adaptive process. Analysis and simulation results demonstrate that the angular beamwidth is insensitive to the input signal-to-noise ratio (SNR).

In this paper, we examine the effect of random steering errors on the signal-to-interference-plus-noise-ratio (SINR) at the output of the recently addressed wavelet-based generalized sidelobe canceller (GSC). This new beamformer employs a set of P-regular M-band wavelet bases for the design of the blocking matrix of the GSC. We first carry out a general expression of the output SINR of the GSC with multiple interferers present. With this expression, we then examine the analysis of wavelet-based GSC by expressing the SINR in terms of parameters such as the regularity of wavelet filters, the number of bands of wavelet filters, the length of adaptive weights, and the input signal-to-noise ratio (SNR). Some simulation results verify the analytically predicted performance.