Time-Varying Autoregressive (TVAR) Models for Multiple Radar Observations

Authors:
Y.I. Abramovich;N.K. Spencer;M.D.E. Turley
Affiliations:
Defence Sci. & Technol. Organ., Adelaide, SA;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2007

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Abstract

We consider the adaptive radar problem where the properties of the (nonstationary) clutter signals can be estimated using multiple observations of radar returns from a number of sufficiently homogeneous range/azimuth resolution cells. We derive a method for approximating an arbitrary Hermitian covariance matrix by a time-varying autoregressive model of order m, TVAR(m), that is based on the Dym-Gohberg band-matrix extension technique which gives the unique TVAR(m) model for any nondegenerate covariance matrix. We demonstrate that the Dym-Gohberg transformation of the sample covariance matrix gives the maximum-likelihood (ML) estimate of the TVAR(m) covariance matrix. We introduce an example of TVAR(m) clutter modeling for high-frequency over-the-horizon radar that demonstrates its practical importance