Model-Based Adaptive Detector of Range-Spread Targets with Secondary Data Support

This paper considers the adaptive detection problem of range-spread targets in Gaussian environment. Model the Gaussian disturbance with unknown covariance matrix as an auto-regressive (AR) process with known AR order but unknown parameters. In order to solve this problem, we propose a novel generalised likelihood ratio test (GLRT)-based detector, which estimates the unknown parameters with secondary data support by maximum likelihood estimate (MLE). The simulation experiments are conducted by comparing the proposed detector with the traditional detectors on both synthetic data and real sea clutter data. And simulation results demonstrate the effectiveness of the proposed detector.