Detection of range-distributed targets in compound Gaussian clutter without secondary data

Abstract

This paper consider the problem of detecting range-distributed targets using high resolution radar(HRR) in compound-Gaussian clutter without secondary data. To overcome the lack of training data, we first assume that clutter returns can be clustered into groups of cells sharing the same value of the noise power. Then an adaptive modified generalized likelihood ratio test (A-GLRT) detector is proposed by replacing the unknown parameters with their maximum likelihood estimations (MLEs). The proposed A-GLRT detector do not need secondary data and ensures constant false alarm rate (CFAR) property with respect to the unknown statistics of the clutter. Performances of this proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar modified generalized likelihood ratio test (MGLRT) detector.