February 2012
Intermediate to advanced
400 pages
11h 15m
English
book
Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises, 4th Edition
by Robert Grover Brown, Patrick Y. C. Hwang
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Index
Adaptive Kalman filter 216–225
Autocorrelation function 65–68
Autocovariance function 65
Bar-Shalom, Y. 217
Bayes rule 11
continuous 25
Bayesian filter, general 237–242
Bayesian filter, flow diagram 242
Binary Offset Code (BOC) code 88–89
Bivariate normal random variable 24
Black, H.S. 95
Blair, W.D. 217
Bona, B.E. 151
Bozic, S.M. 205
Brownian motion process 83–86, 148–149
Bucy, R.S. 371
Butterworth filter 133
Caputi, M.J. 217
Central limit theorem 31
Centralized Kalman filter 231
Characteristic function 14, 17–18, 101
Chi-square probability density 55
Cholesky factorization 129
Communication link ranging and timing 345–348
error-state filter 291, 294–296
go-free Monte Carlo simulation example 298–303
total-state filter 291, 296–298
Wiener two-input problem 290
Continuous Kalman filter 371–378
error covariance equation 374
estimate equation 374
Kalman gain 373
Continuous random variables 13–14
Convergence in probability 46
Convergence in the mean 45
Correlated process and measurement noise 226–228
Correlation coefficient 27
Covariance stationary 68
Cribbage 47
Crosscorrelation function 68–70
Davenport, Jr., W.B. 46
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.