A Novel Noise Jamming Detection Algorithm for Radar Applications

In this letter, we devise and assess a new algorithm to detect a platform equipped with self-screening jamming systems. The latter illuminates the victim radar by means of noise-like signals leading to an increase of the constant false alarm rate threshold (at the detection stage) and a reduction of radar sensitivity. The problem is formulated in terms of a binary hypothesis test exploiting the rank-one modification of the interference covariance matrix introduced by the jammer and the generalized likelihood ratio test is derived. Performance analysis is conducted on simulated data and is aimed at highlighting the effectiveness of this new approach.

[1]  David L Adamy,et al.  Ew 101: A First Course in Electronic Warfare , 2001 .

[2]  Stephen E. Fienberg,et al.  Testing Statistical Hypotheses , 2005 .

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

[4]  A. Farina,et al.  Knowledge-Aided Bayesian Radar Detectors & Their Application to Live Data , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[5]  E. Lehmann Testing Statistical Hypotheses , 1960 .

[6]  A. Farina,et al.  Calculation of blanking probability for the sidelobe blanking for two interference statistical models , 1998, IEEE Signal Processing Letters.

[7]  Yuri I. Abramovich,et al.  GLRT-Based Detection-Estimation for Undersampled Training Conditions , 2008, IEEE Transactions on Signal Processing.

[8]  Hongbin Li,et al.  Parametric adaptive signal detection for hyperspectral imaging , 2006, IEEE Transactions on Signal Processing.

[9]  Augusto Aubry,et al.  Advanced SLB Architectures with Invariant Receivers , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Danilo Orlando,et al.  Adaptive Detection of Point-Like Targets in Spectrally Symmetric Interference , 2016, IEEE Transactions on Signal Processing.

[11]  Alfonso Farina,et al.  Antenna-Based Signal Processing Techniques for Radar Systems , 1992 .

[12]  Carmine Clemente,et al.  Invariant Rules for Multipolarization SAR Change Detection , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Olivier Besson,et al.  On the Expected Likelihood Approach for Assessment of Regularization Covariance Matrix , 2015, IEEE Signal Processing Letters.

[14]  I. S. Reed,et al.  A new CFAR sidelobe canceler algorithm for radar , 1990 .

[15]  A. Farina Single Sidelobe Canceller: Theory and Evaluation , 1977, IEEE Transactions on Aerospace and Electronic Systems.

[16]  R. Muirhead Aspects of Multivariate Statistical Theory , 1982, Wiley Series in Probability and Statistics.

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

[18]  Augusto Aubry,et al.  Exploiting multiple a priori spectral models for adaptive radar detection , 2014 .

[19]  Hongbin Li,et al.  Knowledge-Aided Parametric Tests for Multichannel Adaptive Signal Detection , 2011, IEEE Transactions on Signal Processing.

[20]  L. Mirsky On the Trace of Matrix Products , 1959 .

[21]  William L. Melvin,et al.  Principles of Modern Radar: Advanced techniques , 2012 .

[22]  F. Gini,et al.  Suboptimum approach to adaptive coherent radar detection in compound-Gaussian clutter , 1999 .

[23]  Ami Wiesel,et al.  Invariance Theory for Adaptive Detection in Interference With Group Symmetric Covariance Matrix , 2016, IEEE Transactions on Signal Processing.

[24]  Ami Wiesel,et al.  Time Varying Autoregressive Moving Average Models for Covariance Estimation , 2013, IEEE Transactions on Signal Processing.

[25]  A. Farina,et al.  Performance analysis of the sidelobe blanking system for two fluctuating jammer models , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Augusto Aubry,et al.  Maximum Likelihood Estimation of a Structured Covariance Matrix With a Condition Number Constraint , 2012, IEEE Transactions on Signal Processing.

[27]  William L. Melvin,et al.  Space-time adaptive radar performance in heterogeneous clutter , 2000, IEEE Trans. Aerosp. Electron. Syst..

[28]  Antonio De Maio,et al.  Sidelobe Blanking with Generalized Swerling-Chi Fluctuation Models , 2013, IEEE Transactions on Aerospace and Electronic Systems.