Radar detection schemes for joint temporal and spatial correlated clutter using vector ARMA models

Adaptive radar detection and estimation schemes are often based on the independence of the training data used for building estimators and detectors. This paper relaxes this constraint and deals with the non-trivial problem of deriving detection and estimation schemes for joint spatial and temporal correlated radar measurements. In order to estimate these two joint correlation matrices, we propose to use the Vector ARMA (VARMA) methodology. The estimation of the VARMA model parameters are performed with Maximum Likelihood Estimators in Gaussian and non-Gaussian environment. These two joint estimates of the spatial and temporal covariance matrices leads to build Adaptive Radar Detectors, like Adaptive Normalized Matched Filter (ANMF). Their corresponding performance are analyzed through simulated datasets. We show that taking into account the spatial covariance matrix may lead to significant performance improvements compared to classical procedures ignoring the spatial correlation.

[1]  T. Subba Rao,et al.  Spatio-temporal modelling of temperature time series: a comparative study , 2004 .

[2]  Fulvio Gini,et al.  Statistical analysis of measured polarimetric clutter data at different range resolutions , 2006 .

[3]  Xiaochuan Ma,et al.  Adaptive Radar Detection and Range Estimation with Oversampled Data for Partially Homogeneous Environment , 2015, IEEE Signal Processing Letters.

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

[5]  W. Dunsmuir.  A Central Limit Theorem for Parameter Estimation in Stationary Vector Time Series and its Application to Models for a Signal Observed with Noise , 1979 .

[6]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[7]  A. Öztürk,et al.  Non-Gaussian random vector identification using spherically invariant random processes , 1993 .

[8]  G. Reinsel.  Elements of Multivariate Time Series Analysis , 1995 .

[9]  Jean-Philippe Ovarlez,et al.  Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection , 2015 .

[10]  Ruey S. Tsay,et al.  Multivariate Time Series Analysis: With R and Financial Applications , 2013 .

[11]  Hongbin Li,et al.  Parametric GLRT for Multichannel Adaptive Signal Detection , 2007, IEEE Transactions on Signal Processing.

[12]  Augusto Aubry,et al.  Radar detection and range estimation using oversampled data , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[13]  E. Hannan,et al.  Vector linear time series models: corrections and extensions , 1978, Advances in Applied Probability.

[14]  P. Stoica,et al.  Maximum likelihood methods in radar array signal processing , 1998, Proc. IEEE.

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

[16]  David E. Tyler.  A Distribution-Free $M$-Estimator of Multivariate Scatter , 1987 .

[17]  J. K. Ord,et al.  Space-time modelling with an application to regional forecasting , 1975 .

[18]  H. Vincent Poor,et al.  Complex Elliptically Symmetric Distributions: Survey, New Results and Applications , 2012, IEEE Transactions on Signal Processing.

[19]  Romain Couillet,et al.  RMT for whitening space correlation and applications to radar detection , 2015, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[20]  Simon Haykin,et al.  Adaptive radar detection and estimation , 1992 .

[21]  Qingwen Zhang,et al.  Parametric adaptive matched filter for airborne radar applications , 2000, IEEE Trans. Aerosp. Electron. Syst..

[22]  Fulvio Gini,et al.  Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter , 2002, Signal Process..