55.Signal Processing by John G. Webster (Editor)

By John G. Webster (Editor)

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By John G. Webster (Editor)

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Adaptive Radar Hypothesis (Unknown Covariance) In practice the interference-plus-noise covariance matrix R in Eq. (82) is unknown and must be estimated. Fortunately for the radar problem, the large number of range gates to be tested for the presence of targets usually provides a sufficient number of samples that may be assumed to be identically distributed. It is important that these range gates be free of target reflections, because such targets will be (partially) nulled by the adaptive weight vector.

Applebaum Adaptive arrays, IEEE Trans. , AP-24 (5): 585–598, 1976. 6. R. A. Monzingo T. W. Miller Adaptive Arrays, New York: Wiley, 1980. 7. R. T. Compton, Jr. Adaptive Antennas, Englewood Cliffs, NJ: Prentice-Hall, 1988. 8. S. , Upper Saddle River, NJ: Prentice-Hall, 1996. 9. A. Farina Electronic counter-countermeasures, inM. I. , New York: McGraw-Hill, 1990. 10. A. Farina Antenna-Based ECCM Techniques for Radar Systems, Norwood, MA: Artech House, 1992. 11. L. E. Brennan J. D. Mallett I. S. Reed Adaptive arrays in airborne MTI radar, IEEE Trans.

Robustness to Model Mismatch The detection performance should be robust to modeling errors such as steering vector mismatches to the true target, inclusion of targets in the covariance matrix estimate, and non-Gaussian interference. The estimation loss may be approximated independently of a specific adaptive detector by computing the average SINR obtained from using the sample covariance matrix; this approach is presented in the following subsection. The detection performance of two important adaptive detectors—the generalized likelihood ratio test (GLRT) and the adaptive matched filter (AMF)—is presented in the subsection after.

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