A robust adaptive beamforming method is proposed to cancel coherent, as well as incoherent, interference using an array of arbitrary geometry. In this method, coherent interferences are suppressed by a transformation of received data with the estimates of their arrival angles and then, to reject incoherent interferences, the array output power is minimized subject to the look direction constraint in the transformed signal-plus-interference (TSI) subspace. This TSI subspace-based beamforming results in robustness to errors in the angle estimations. Its performance is theoretically examined. The theoretic results conform to simulation results. It is straightforward to apply the theoretic results to the performance analysis of subspace-based adaptive beamfomers only for incoherent interference cancellation.

Eigenstructure-based beamformers suffer form performance degradation due to pointing errors when the number of the incident signals is incorrectly detected or when the desired signal is much stronger than the interferences. We present a robust beamformer with the self-correction of look direction errors, based on the Newton method. Even though there are errors in the detection of the incident signal number as well as in the presumed look direction, it can achieve optimum performance with no errors.

An adaptive beamforming method for the rejection of coherent interference signals is presented which exploits forward and backward correlations. The proposed method, in which the effective degree of freedom of the beamformer is increased by virtue of its use of both types of correlation, can cancel more coherent interference signals and provide better performance than the existing one that uses the forward correlation only.

This paper presents a computationally efficient subspace-based method for partially adaptive beamforming which is based on the structure of the generalized sidelobe canceller (GSC). Its auxiliary beamformer operates in an estimated interference subspace which is obtained through simple computation. The computational burden of the proposed method in terms of complex multiplication is just on O(η2M) where η and M are the numbers of interferences and the array elements, respectively. Though the subspace obtained is different from the exact interference subspace due to the presence of noise, theoretical analysis shows that the proposed beamfomer virtually attains the optimal performance for strong or sidelobe interference. Simulation results validate its effectiveness including fast convergence, even in the presence of errors in the detected number of directional signals.

The doubly constrained robust Capon beamformer (DCRCB), which employs a spherical uncertainty set of the steering vector together with the constant norm constraint, can provide robustness against arbitrary array imperfections. However, its performance can be greatly degraded when the uncertainty bound of the spherical set is not properly selected. In this paper, combining the DCRCB and the weight-vector-norm-constrained beamformer (WVNCB), we suggest a new robust adaptive beamforming method which allows us to overcome the performance degradation due to improper selection of the uncertainty bound. In WVNCB, its weight vector norm is limited not to be larger than a threshold. Both WVNCB and DCRCB belong to a class of diagonal loading methods. The diagonal loading range of WVNCB, which dose not consider negative loading, is extended to match that of DCRCB which can have a negative loading level as well as a positive one. In contrast to the conventional DCRCB with a fixed uncertainty bound, the bound in the proposed method varies such that the weight vector norm constraint is satisfied. Simulation results show that the proposed beamformer outperforms both DCRCB and WVNCB, being far less sensitive to the uncertainty bound than DCRCB.

This letter presents an adaptive beamformer robust to random steering errors, based on the projection of received signals onto the orthogonal complement of the interference subspace. In the presence of random steering errors, to prevent the suppression of the desired signal, the proposed beamformer effectively finds basis vectors for the estimation of the interference subspace.

Sequential estimation of arrival angles allows us to resolve closely located sources that the standard MUSIC fails to do so. A new sequential estimation method is proposed which utilizes only the signal subspace components of the steering vectors for some estimates of the arrival angles. It is theoretically shown that the asymptotic performance of the proposed method is better than that of the conventional sequential method which exploits both the signal and the noise subspace components. Simulation results show that the former outperforms the latter in correlated sources as well as in uncorrelated sources.

Adaptive arrays with signal blocking have the advantages of fast convergence and robustness to pointing errors as well as of rejecting a coherent interference in addition to incoherent ones. In this paper, we propose a novel method for performance improvement in such arrays with no increase in complexity. The proposed method utilizes all of the array elements to obtain the adaptive output so that its performance is superior to that of the conventional method which does not utilize one of the elements. Their performances are compared analytically and by computer simulation.

To handle coherent signals with unknown arrival angles, an adaptive beamforming method is proposed which can be applied to an arbitrary array. The proposed method efficiently solves a generalized eigenvalue problem to estimate the arrival angles of the desired coherent signal group, by exploiting the Brent method in conjunction with alternating maximization. We discuss the condition for the correct direction estimation without erroneously taking interference direction estimates for the desired ones. Simulation results show that the performance of the proposed beamformer is very similar to that of the beamformer with the exact composite steering vector (CSV).