Performance analysis of the approximate dynamic programming algorithm for parameter estimation of superimposed signals

We consider the classical problem of fitting a model composed of multiple superimposed signals to noisy data using the criteria of maximum likelihood (ML) or subspace fitting, jointly termed generalized subspace fitting (GSF). We analyze a previously proposed approximate dynamic programming algorith...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 48; H. 5; S. 1274 - 1286
Hauptverfasser:	Sze Fong Yau, Bresler, Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.05.2000 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Applied sciences Array signal processing Dynamic programming Exact sciences and technology Heuristic algorithms Information, signal and communications theory Lattices Maximum likelihood estimation Multidimensional systems Performance analysis Predictive models Signal and communications theory Signal, noise Studies Telecommunications and information theory Upper bound Waveform Parameter estimation Signal estimation Optimization method Mean square error Perturbation method Model matching Radar Performance analysis System identification Dynamic programming Cramer Rao inequality Estimation error Monte Carlo method Approximate method Non linear regression Sonar Frequency characteristic Experimental result Algorithm performance Signal processing Numerical simulation Maximum likelihood Sensor array Signal to noise ratio
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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