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Estimation performance is determined by the log likelihood surface of Eq. (91) parametrized using fd , φ, and θ. The maximum likelihood estimator is For actual radar problems when the covariance R is unˆ may be used, as in known, the sample covariance matrix R the AMF detector. If the likelihoods of a target’s angle and Doppler were independent of each other (Fig. 14, left-hand side), then these parameters could be estimated independently using a monopulse-like method. However, space–time adaptivity for clutter nulling yields distorted space–time likelihood surfaces for targets near the interference (Fig.
E. Brennan I. S. Reed Theory of adaptive radar, IEEE Trans. Aerosp. Electron. , AES-9 (2): 237–252, 1973. 5. S. P. 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.
DL provides a tradeoff between nonadaptive steering vectors (δ large) and fully adaptive weight vectors (δ small). DL constrains the length of the adaptive weight vector as in Eq. (142) to counteract the effects of poorly estimated noise eigenvalues. Figure 7 illustrates the clutter eigenvalues found in the PRI-staggered post-Doppler STAP algorithm described in the sub-subsection “Element-Space Post-Doppler STAP” below for the Mountaintop (31) scenario. The clutter subspace dimension is seen to be about 20, which is above the rank of about 16 predicted by Brennan’s rule (Eqs.
55.Signal Processing by John G. Webster (Editor)