Adaptive signal processing algorithms: stability and performance
This comprehensive volume explores the analysis of the behavior (stability and performance) of adaptive signal processing (ASP) algorithms. Authors Victor Solo and Xuan Kong discuss general methods of both algorithm construction and algorithm analysis. They introduce ASP through its applications, show how to construct ASP algorithms, and provide a detailed global stability and performance analysis in the presence of time varying parameters in both deterministic and stochastic settings. Adaptive Signal Processing Algorithms: explores deterministic stability analysis by means of averaging methods; covers blind equalization; utilizes simulation throughout to amplify theoretical and practical points; lists additional references for each chapter for those who wish to pursue a specific topic in more depth than is provided. In addition, this is the first book of its type to treat time varying parameters.
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adaptive algorithm Appendix approximation autocovariance averaged system behavior Blind Equalization bounded calculate channel equalization Chapter consider convergence delay estimation deterministic developed differentiation filter discussion eigenvalues eigenvector equalizing filter equilibrium point error signal error system Example finite time averaging FIR filter gives gradient holds Hovering theorem infinite introduce invariant iteration Kalman filter lemma linear Lipschitz condition LMS algorithm loop filter Lyapunov function matrix minimum phase misadjustment mixed time scale Newton's method noise of variance Note obeys a Lipschitz optimal perturbed Lyapunov function positive definite primary signal primary system Proof regularity conditions Repeat Exercise replaced result s=k+ scale LMS sequence setting signal processing single time scale SLLN slowly time varying spectral speed stability region steady steepest descent stochastic suppose Taylor series term trajectory transfer function varying parameters vector white noise zero mean