Meng Huadong

A novel method for evaluating robustness of CFAR radar detectors

A method for evaluating the robustness of constant false alarm rate (CFAR) detectors is presented, which is based on the powerful methodology of influence function (IF) developed in the literature on robust statistics. It can evaluate and compare the robustness of different kinds of CFAR detectors by calculating the first derivative of false alarm probability (FAP) at an underlying distribution, which is named FAP-IF. The FAP-IF of cell averaging (CA) and ordered statistics (OS) CFAR detectors are presented. The computational simulation of radar detection in exponential clutter is also presented to prove the theoretical result.

New methods for estimating the center frequency and bandwidth of clutter

Two methods for estimating the center frequency and bandwidth, separately called centroid method (CM) and energy integration method (EIM), are proposed. A comparison of the complexity and the performance between the two methods and the general ones is presented. It is shown that the two methods are effective for short-time clutter data in practical application.

New CFAR processor independent of original noise distribution

Based on the fact that the result of noncoherent integration of the amplitudes of independent samples of noise is approximately identical to nonzero-mean Gaussian distribution, a Gaussian biparametric constant false alarm rate (GB-CFAR) processor is presented, which is a novel CFAR detector independent of original clutter plus noise distribution. Then the conclusion is presented that its detection performance relates only to the false alarm rate, the number of integration and the mean-to-standard-deviation ratio of original noise. Eliminating the mean of integrated noise amplitude during the detection process is equivalent to taking out partial noise energy, therefore, the detection performance of the GB-CFAR processor exceeds that of the traditional CA-CFAR (cell-averaging CFAR) processor.