Adaptive Radar Signal Processing by Simon Haykin

By Simon Haykin

This collaborative paintings offers the result of over 20 years of pioneering study by means of Professor Simon Haykin and his colleagues, facing using adaptive radar sign processing to account for the nonstationary nature of our surroundings. those effects have profound implications for defense-related sign processing and distant sensing. References are supplied in every one bankruptcy guiding the reader to the unique examine on which this publication relies.

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This is where adaptivity becomes useful, provided that the radar is designed to adapt its beamforming technique in accordance with the received data. Practical issues (such as cost and physical limitations) put constraints on the number of elements in a sampled aperture and the separation between them. Thomson’s multi-taper method (MTM), described in this chapter, is shown to perform in a robust enough manner for the difficult case of correlated signals and to extract the desired signal information in an optimum way.

2 Angle-of-Arrival Estimation in the Presence of Multipath The Composite Spectrum The use of adaptive weighting as developed above provides superior protection against leakage and bias. Thomson also offers a further refinement to achieve higher resolution by considering each specific frequency point f0 as a free parameter in (f − W ≤ f0 ≤ f + W). 36) k =0 and Sˆ (f0) is the adaptive spectrum developed earlier. This choice of weights imposes the constraint that Sˆ(f0) should have sufficient degrees of freedom for w(f0) to have a reasonable distribution.

11) is found in terms of the eigenfunction expansion of the kernel, which is recognized as the Dirichlet kernel with known eigenfunctions, namely, the prolate spheroidal wavefunctions; these functions are fundamental to the study of time and frequency-limited systems. 11) is carried out in some local interval about f, say ( f − W, f + W), using the prolate spheroidal wavefunctions as a basis. 13) where the Uk (N,W;f ), k = 0, 1, . . , N − 1 are the discrete prolate spheroidal wave1 functions (DPSWF); and W, lying in the interval 0 < W < −, 2 is the local bandwidth, which is normally on the order of 1/N.

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