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

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3 Author(s)
Abramovich, Y.I. ; Defence Sci. & Technol. Organ., Adelaide, SA ; Spencer, N.K. ; Turley, M.D.E.

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

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Signal Processing, IEEE Transactions on  (Volume:55 ,  Issue: 4 )