000			03315nam a2200337 a 4500
005			20250919184854.0
008			130819s2012 njua b 001 0 eng
020			_a9780470621707 (hbk.) _cRM397.47
020			_a0470621702 (hbk.)
039		9	_a201312231124 _bzabidah _c201312171215 _dros _y08-19-2013 _zros
040			_aDLC _beng _cDLC _dYDX _dBTCTA _dBDX _dYDXCP _dOCLCO _dBWX _dDLC _dUKM
090			_aQA279.5.H385
090			_aQA279.5 _b.H385
100	1		_aHaug, Anton J., _d1941-
245	1	0	_aBayesian estimation and tracking : _ba practical guide / _cAnton J. Haug.
260			_aHoboken, N.J. : _bWiley, _c2012.
300			_axxvi, 369 p. : _bill. ; _c25 cm.
504			_aIncludes bibliographical references and index.
520			_a'This book presents a practical approach to estimation methods that are designed to provide a clear path to programming all algorithms. Readers are provided with a firm understanding of Bayesian estimation methods and their interrelatedness. Starting with fundamental principles of Bayesian theory, the book shows how each tracking filter is derived from a slight modification to a previous filter. Such a development gives readers a broader understanding of the hierarchy of Bayesian estimation and tracking. Following the discussions about each tracking filter, the filter is put into block diagram form for ease in future recall and reference. The book presents a completely unified approach to Bayesian estimation and tracking, and this is accomplished by showing that the current posterior density for a state vector can be linked to its previous posterior density through the use of Bayes' Law and the Chapman-Kolmogorov integral. Predictive point estimates are then shown to be density-weighted integrals of nonlinear functions. The book also presents a methodology that makes implementation of the estimation methods simple (or, rather, simpler than they have been in the past). Each algorithm is accompanied by a block diagram that illustrates how all parts of the tracking filter are linked in a never-ending chain, from initialization to the loss of track. These filter block diagrams provide a ready picture for implementing the algorithms into programmable code. In addition, four completely worked out case studies give readers examples of implementation, from simulation models that generate noisy observations to worked-out applications for all tracking algorithms. This book also presents the development and application of track performance metrics, including how to generate error ellipses when implementing in real-world applications, how to calculate RMS errors in simulation environments, and how to calculate Cramer-Rao lower bounds for the RMS errors. These are also illustrated in the case study presentations'-- _cProvided by publisher.
650		0	_aBayesian statistical decision theory.
650		0	_aAutomatic tracking _xMathematics.
650		0	_aEstimation theory.
907			_a.b15704397 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQA279.5.H385
914			_avtls003537465
990			_aza
991			_aFakulti Sains dan Teknologi
998			_at _b2013-06-08 _cm _da _feng _gnju _y0 _z.b15704397
999			_c682023 _d682023