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Variance bounds for parameter estimation in correlated non-Gaussian clutter

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2 Author(s)
Gini, F. ; Dipt. di Inf., Pisa Univ., Italy ; Greco, M.V.

We derive a lower bound on the error covariance matrix for any unbiased estimator of the parameters of a disturbance modeled as a mixture of spherically invariant random processes (SIRPs). The bound can be numerically computed in closed form in many practical cases where the computation of the true Cramer-Rao lower bound is infeasible. The proposed bound is particularly useful when the disturbance, conditioned to a vector of unwanted random parameters (nuisance parameters) with apriori known probability density function, can be modeled as a Gaussian process. The case of disturbance composed of a mixture of K-distributed clutter, Gaussian clutter and thermal noise belongs to this set and it is regarded as a realistic radar scenario. The performance of some practical estimators are compared to this bound for three study cases

Published in:

Radar Conference, 1997., IEEE National

Date of Conference:

13-15 May 1997