Model-Based Wald Test for Adaptive Range-Spread Target Detection

For the problem of range-spread target detection, many adaptive detectors commonly estimate the covariance matrix of the disturbance by utilizing the training data without target information. However, in the limited-training case, the conventional detectors suffer significant performance degradation. This paper devises and assesses a model-based Wald detector by modeling the disturbance as an autoregressive (AR) process with unknown parameters, which is able to overcome the detection degradation caused by insufficient training data. Meanwhile, the Wald test reduces the computational complexity because the unknown parameters are only estimated by maximum likelihood criterion under hypothesis that the target exists. Remarkably the asymptotic expression for the probability of detection and false alarm shows the detector is asymptotically constant false alarm rate (CFAR) with respect to the disturbance covariance matrix. The performance evaluation, conducted by resorting to simulation data, has confirmed the effectiveness of the current proposal in comparison with the previously proposed detectors.