Durbin test with enhanced detection performance to mismatched signal

This paper deals with the problem of detecting a signal whose amplitude is a scaling factor in the presence of homogeneous Gaussian noise with unknown covariance matrix.Since no uniformly most powerful test exists for the problem at hand,we devise and assess a detection strategy based on the well-known Durbin test design criteria.The closed-form expressions for the probabilities of false alarm and detection of the Durbin test are derived,which show that it bears a constant false alarm rate property against the noise covariance matrix.At the analysis stage,the performance of the new receiver is assessed,also in comparison with some classical adaptive detectors,both in matched and in mismatched signal cases.The results show that the proposed detector achieves a visible performance improvement in the presence of severe steering vector mismatch,while maintaining an acceptable detection loss for matched signal.

Adaptive detection in the presence of signal mismatch

The problem of adaptive detection in the situation of signal mismatch is considered; that is, the actual signal steering vector is not aligned with the nominal one. Two novel tunable detectors are proposed. They can control the degree to which the mismatched signals are rejected. Remarkably, it is found that they both cover existing famous detectors as their special cases. More importantly, they possess the constant false alarm rate（CFAR）property and achieve enhanced mismatched signal rejection or improved robustness than their natural competitors. Besides, they can provide slightly better matched signals detection performance than the existing detectors.

A tunable adaptive detector for distributed targets when signal mismatch occurs

Aiming at the problem of detecting a distributed target when signal mismatch occurs,this paper proposes a tunable detector parameterized by an adjustable parameter.By adjusting the parameter,the tunable detector can achieve robust or selective detection of mismatched signals.Moreover,the proposed tunable detector,with a proper tunable parameter,can provide higher detection probability compared with existing detectors in the case of no signal mismatch.In addition,the proposed tunable detector possesses the constant false alarm rate property with the unknown noise covariance matrix.

Robust adaptive beamforming algorithm based on Bayesian approach

The performance of adaptive array beamform-ing algorithms substantially degrades in practice because of a slight mismatch between actual and presumed array res-ponses to the desired signal.A novel robust adaptive beam-forming algorithm based on Bayesian approach is therefore proposed.The algorithm responds to the current envi-ronment by estimating the direction of arrival(DOA)of the actual signal from observations.Computational com-plexity of the proposed algorithm can thus be reduced com-pared with other algorithms since the recursive method is used to obtain inverse matrix.In addition,it has strong robustness to the uncertainty of actual signal DOA and makes the mean output array signal-to-interference-plus-noise ratio(SINR)consistently approach the optimum.Simulation results show that the proposed algorithm is bet-ter in performance than conventional adaptive beamform-ing algorithms.