Parametric Generalized Likelihood Ratio Tests for Distributed Target in Heterogeneous Environment

In High Range-Resolution (HRR) radar, a target extends probably more than one range bins and the parametric adaptive detection is studied in this paper for such distributed target, where the disturbances in both primary and secondary data are represented by a vector autoregressive model with random spatial covariance matrix. Subsequently, the corresponding detectors are derived according to Bayesian one-Step Parametric Generalized Likelihood Ratio Test (B1S-PGLRT) and Bayesian two-Step Parametric GLRT (B2S-PGLRT) decision rules. However, the former leads to no close formulation and the latter has the similar detection architecture with the classic Parametric Adaptive Matched Filter (PAMF), using Maximum A-Posteriori (MAP) estimator instead of Maximum Likelihood Estimator (MLE), of the spatial covariance matrix. Meanwhile, the normalized version for B2S-PGLRT is also given. Finally, the processing steps and its computation issues are analyzed for the Bayesian parametric detectors and their detection performances are evaluated via Monte Carlo simulations. The results show that the parametric matched filter in Bayesian framework is better than GLRT, when the training data are not sufficient.