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Adaptive CFAR detection for clutter-edge heterogeneity using Bayesian inference

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3 Author(s)
Biao Chen ; Dept. of Electr. & Comput. Syst., Syracuse Univ., NY, USA ; Varshney, P.K. ; Michels, J.H.

Radar constant false alarm rate (CFAR) detection is addressed in this correspondence. Motivated by the frequently encountered problem of clutter-edge heterogeneity, we model the secondary data as a probability mixture and impose a hierarchical model for the inference problem. A two-stage CFAR detector structure is proposed. Empirical Bayesian inference is adopted in the first stage for training data selection followed by a CFAR processor using the identified homogeneous training set for target detection. One of the advantages of the proposed algorithm is its inherent adaptivity; i.e., the threshold setting is much less sensitive to the nonstationary environment compared with other standard CFAR procedures.

Published in:

Aerospace and Electronic Systems, IEEE Transactions on  (Volume:39 ,  Issue: 4 )