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Balakrishnan A.V. — Kalman filtering theory
Balakrishnan A.V. — Kalman filtering theory

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Название: Kalman filtering theory

Автор: Balakrishnan A.V.


This is a textbook intended for a one-quarter (or one-semester, depending on the pace) course at the graduate level in Engineering. The prerequisites are Elementary State-Space theory and Elementary (second-order Gaussian) Stochastic Process theory. As a textbook, it does not not purport to be a compendium of all known work on the subject. Neither is it a "trade book." Rather it attempts a logically sequenced set of topics of proven pedagogical value, emphasizing theory while not devoid of practical utility. The organization is based on experience gained over a period of ten years of class room teaching. It develops those aspects of Kalman Filtering lore which can be given a firm mathematical basis, avoiding the industry syndrome manifest in professional short courses: "Here is the recipe. Use it, it will "work"!"

Язык: en

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1984

Количество страниц: 222

Добавлена в каталог: 11.04.2010

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Предметный указатель
Adaptive Equalizer problem      170
Average, phase      20
Average, time      20
Batch processing      77 153
Bayesian view      13
Bias      36
Conditional expectation      47
Controllability      7
Controllable subspace      7
Correlated signal and noise      184
Cramer — Rao formula      38
Ergodic Gaussian processes      21
Ergodic theory      20
Error propagation equation      91
Error, fit      93
Error, fit, alternate definition of      100
Error, fit, likelihood ratio using      215
Error, mean square      38
Error, signal estimation error covariance      92
Estimate, efficient      41
Estimate, efficient, asymptotically      43
Estimate, ML      43 67 68
Estimate, MUL      58 67 68
Estimate, unbiased      36
Estimate, unbiased, asymptotically      43
Estimation, parameter      35
Factorization      26
Filter      34
Filter, suboptimal      127 129
Finite power      14
Finite power, signals with      14
Frequence domain      133
Frequency, break      135
Frequency, normalized gain      138
Gaussian signals in Gaussian noise      211
Guesstimate      72
Identification, system      149
Ignorance, maximum      67
Information, mutual      58
Innovation sequence      80
Innovation sequence, state      83
Interpolation      171
Likelihood ratio formulas      211
Likelihood, principle of maximum      42 43
Likelihood, unconditional likelihood principle      67
Likelihood, unconditional maximum      57
Markovian process      23
Matrix, controllability      7
Matrix, covariance matrix factorization      59
Matrix, error covariance      87
Matrix, gain      87
Matrix, lower-triangular      61
Matrix, stable      5
Matrix, weighting      3
Non-white noise      193
Observability      9
On-line operation      64
On-line problem      77
On-line version      159
Optimality, asymptotic      105
Orthogonal      60
Orthogonalization, Gram — Schmidt      59 80
Oscillator, linear      27
Pass, backward      171
Pass, band      138
Pass, forward      171
Pass, low      138
Predictor, one-step      91
Second order theory      64
Signal generation model      22
Signal, stochastic      64
Signal, sure      64
Signal-to-noise ratio      135
Smoother estimate      171
Smoother, asymptotic behavior of the      177—178
Spectral density      16
Spectral representation theorem      15
Stability      5
State, controllable      7
State, observable      11
State, unobservable      11
Threshold      136
Time, invariance      4
Time, series      18
Transfer function      6
Transfer function, normalized      139
Weighting pattern      3 6
Weighting pattern, system      4
Wiener — Hopf equation      51 78
z-transform      6
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