An ABORT-Like Detector With Improved Mismatched Signals Rejection Capabilities

In this paper, we present a GLRT-based adaptive detection algorithm for extended targets with improved rejection capabilities of mismatched signals. We assume that a set of secondary data is available and that noise returns in primary and secondary data share the same statistical characterization. To increase the selectivity of the detector, similarly to the ABORT formulation, we modify the hypothesis testing problem at hand introducing fictitious signals under the null hypothesis. Such unwanted signals are supposed to be orthogonal to the nominal steering vector in the whitened observation space. The performance assessment, carried out by Monte Carlo simulation, shows that the proposed dectector ensures better rejection capabilities of mismatched signals than existing ones, at the price of a certain loss in terms of detection of matched signals.

[1]  Daniel R. Fuhrmann,et al.  A CFAR adaptive matched filter detector , 1992 .

[2]  A. Maio,et al.  CFAR detection of distributed targets in non-Gaussian disturbance , 2002 .

[3]  H. V. Trees.  Detection, Estimation, And Modulation Theory , 2001 .

[4]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[5]  Christ D. Richmond,et al.  Performance of a class of adaptive detection algorithms in nonhomogeneous environments , 2000, IEEE Trans. Signal Process..

[6]  D. Luenberger,et al.  Estimation of structured covariance matrices , 1982, Proceedings of the IEEE.

[7]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[8]  Charles M. Rader,et al.  Adaptive beamformer orthogonal rejection test , 2001, IEEE Trans. Signal Process..

[9]  Giuseppe Ricci,et al.  GLRT-based adaptive detection algorithms for range-spread targets , 2001, IEEE Trans. Signal Process..

[10]  Michael A. Zatman,et al.  A computationally efficient two-step implementation of the GLRT , 2000, IEEE Trans. Signal Process..

[11]  A. Farina,et al.  Vector subspace detection in compound-Gaussian clutter. Part I: survey and new results , 2002 .

[12]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[13]  G. Ricci,et al.  Adaptive Detection and Interference Rejection of Multiple Point-Like Radar Targets , 2006, IEEE Transactions on Signal Processing.

[14]  E. J. Kelly.  An Adaptive Detection Algorithm , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[15]  E J Kelly,et al.  Adaptive Detection and Parameter Estimation for Multidimensional Signal Models , 1989 .

[16]  Louis L. Scharf,et al.  The CFAR adaptive subspace detector is a scale-invariant GLRT , 1999, IEEE Trans. Signal Process..

[17]  A. Farina,et al.  Spatial adaptive subspace detection in OTH radar , 2003 .

[18]  E. Conte,et al.  Adaptive matched filter detection in spherically invariant noise , 1996, IEEE Signal Processing Letters.

[19]  B. A. D. H. Brandwood.  A complex gradient operator and its applica-tion in adaptive array theory , 1983 .

[20]  Giuseppe Ricci,et al.  Recursive estimation of the covariance matrix of a compound-Gaussian process and its application to adaptive CFAR detection , 2002, IEEE Trans. Signal Process..

[21]  Louis L. Scharf,et al.  Adaptive subspace detectors , 2001, IEEE Trans. Signal Process..