Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises, 4th Edition

Index

Adaptive Kalman filter 216–225

Allan variance 326, 354–356

Andrews, A.P. 207, 212

Autocorrelation function 65–68

from experimental data 90–95

Autocovariance function 65

Average 15–16

Bandlimited systems 110–111

Bandlimited white noise 76–77

Bar-Shalom, Y. 217

Bayes rule 11

continuous 25

Bayesian filter, general 237–242

Bayesian filter, flow diagram 242

Bierman, G.J. 194, 196

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

Complementary filter 290–298

error-state filter 291, 294–296

go-free Monte Carlo simulation example 298–303

intuitive filter 292–293

total-state filter 291, 296–298

Wiener two-input problem 290

Conditional probability 7–12

Continuous Kalman filter 371–378

error covariance equation 374

estimate equation 374

Kalman gain 373

Riccati equation 375–378

Continuous random variables 13–14

Continuous-time model 121–122

Convergence in probability 46

Convergence in the mean 45

Correlated process and measurement noise 226–228

Correlation 26–28

Correlation coefficient 27

Covariance 26–28

Covariance stationary 68

Craps 8, 47

Cribbage 47

Crosscorrelation function 68–70

Davenport, Jr., W.B. 46

Decentralized ...