Adaptive Time-varying Processing for Stationary Target Detection in Nonstationary Interference
نویسندگان
چکیده
We derive a method of approximating a nonstationary process by a time-varying autoregressive model of order (TVAR( )). This method is based on the Dym–Gohberg band matrix extension technique, and for an -variate arbitrary nondegenerate covariance matrix it gives the unique TVAR( ) model. For adaptive applications, this method requires a sample size that is comparable with the TVAR( ) model order . We derive the maximum-likelihood (ML) estimate for this TVAR( ) model under homogeneous training conditions. The losses associated with the TVAR( ) approximation of a non-TVAR nonstationary process are also evaluated.
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