Document Type


Date of Award

Fall 1-31-1993

Degree Name

Doctor of Philosophy in Electrical Engineering - (Ph.D.)


Electrical and Computer Engineering

First Advisor

Joseph Frank

Second Advisor

Yun Q. Shi

Third Advisor

Alexander Haimovich

Fourth Advisor

Stelios D. Himonas

Fifth Advisor

Priyantha Liyanage Perera


In an automatic radar detection system the received signal in every range resolution cell is compared with a threshold to test for the presence of a target. A Neyman-Pearson type test is used which maximizes the probability of detection for a fixed probability of false alarm. For the simple case where the noise is homogeneous a fixed threshold is chosen to achieve the designed constant false alarm rate (CFAR). In the more realistic case the noise background is non-stationary due to clutter and interference. In this situation, the threshold used for testing a particular cell is usually set adaptively using data from nearby resolution cells. A number of such adaptive schemes have been proposed and these are reviewed and the analysis of some of them extended 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, 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. In addition, the results of the Censored Cell Averaging CFAR detector, CCA-CFAR, are extended for multiple pulse transmission and compared with those of the proposed detector.

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 wheather [sic] 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. An analysis of the processing time required by the proposed detector is also presented, and compared with the processing time required by other detectors.

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. The fact that the noise residues become partially correlated to the same degree, if the adjacent samples are identically distributed, enable us to identify non-homogeneities in the clutter power distribution, by simply observing the consistency in the degree of correlation.



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