Covariance matrix estimation for CFAR Detection in correlated heavy tailed clutter
Signal Processing - Signal processing with heavy-tailed models
A maximum entropy framework for space-time adaptive processing
Signal Processing - Special section: New trends and findings in antenna array processing for radar
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In the majority of adaptive radar detection algorithms, the covariance matrix for the clutter-plus-noise is estimated using samples taken from range cells surrounding the test cell. In a nonhomogeneous environment, this can lead to a mismatch between the mean of the estimated covariance matrix and the true covariance matrix for the test cell. Further, an inaccurate target steering vector may also be employed. Closed-form expressions are provided, which give the performance for such cases when any of a set of popular space-time adaptive processing (STAP) algorithms are used. The expressions are exact for some interesting cases. For some other cases, it is demonstrated that the expressions provide good approximations to the exact performance. To simplify the analysis, the samples from the surrounding range cells are assumed to be independent and identically distributed, and these samples are assumed to be independent from the sample taken from the test cell. A small number of important parameters describe which types of mismatches are important and which are not. Monte Carlo simulations, which closely match the predictions of our equations, are included