On adaptive censored CFAR detection

In this dissertation, new adaptive thresholding techniques for use in nonhomogeneous background environments are proposed and analyzed. It is shown that these new schemes under many conditions perform better than the methods described in the literature in terms of achieving lower probabilities of false alarm and higher probabilities of detection.; First we analyze the greatest-of (GO) and smallest-of constant false alarm rate (SO-CFAR) detectors in time diversity transmission. Time diversity transmission is employed to combat deep fades and the loss of the signal. We then present a comparison of the detection performance and the false alarm regulation of the CA, GO and SO-CFAR detectors.; Then we propose and analyze the Automatic Censored Cell Averaging CFAR detector, ACCA-CFAR, which determines whether the test cell is in the clutter or the clear region and selects only those samples that are identically distributed with the noise in the test cell to form the detection threshold. In the presence of two clutter power transitions in the reference window, the ACCA-CFAR detector is shown to achieve robust false alarm regulation performance while none of the detectors in the literature performs well.; For multiple target situations we propose and analyze the Adaptive Spiky Interference Rejection detector, ASIR-CFAR, which determines and censors the interfering targets by performing cell-by-cell tests, without a priori knowledge about the number of interfering targets.; For multiple target situations in nonhomogeneous clutter the Data Discriminator detector, DD-CFAR, is proposed and analyzed. The DD-CFAR detector performs two passes over the data. In the first pass, the algorithm censors any possible interfering target returns that may be present in the reference cells of the test cell. In the second pass the algorithm determines whether the test cell is in the clutter or the clear region and selects only those samples that are identically distributed with the noise in the test cell to form the detection threshold.; Finally we propose and analyze, the Residual Cell Averaging CFAR detector, RCA-CFAR, an adaptive thresholding procedure for Rayleigh envelope distributed signal and noise where noise power residues instead of noise power estimates are processed. (Abstract shortened by UMI.)...