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By John G. Webster (Editor)

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136) may be obtained by applying the method of Lagrange multipliers to the problem The augmented function to be optimized may be expressed as Adaptive Degrees of Freedom and Nulling Performance The number of adaptive degrees of freedom, that is, the number N of elements in the adaptive weight vector w = (w1 , w2 , . . , wN )T , sets a fundamental limit on the performance of adaptive processing. Because the dimension of the space of vectors perpendicular to w, where λ and λδ are Lagrange multipliers and an equivalent expression for |wH v|2 has been used.

See also Ref. (54). CONCLUSIONS For effective surveillance against modern targets, radars must operate near their noise-limited performance and cannot be blinded by electromagnetic interference caused by jamming or clutter. Space–time adaptivity provides an effective method for robustly mitigating such interference. Incorporating adaptivity into the radar’s signal processing chain affects all its traditional tasks of receiving signals, examining them for the presence of targets, and estimating the speeds and position of targets.

10). , the choice of a space–time preprocessor matrix T), as well as choice of beamformers and Doppler filters. This design flexibility allows for many different STAP algorithms, each of whose performance is dependent upon specific radar system parameters. Oftentimes, the radar’s entire spatial aperture is used for adaptive degrees of freedom, and temporal adaptivity is introduced using time-varying sub-CPIs. We shall describe one representative algorithm per quadrant; results are shown in Figs. 11 and 12.

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

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