Optimal signal processing under uncertainty [E-Book] / author: Edward R. Dougherty
Dougherty, Edward R., (author)
Bellingham, Washington : SPIE, 2018
1 online resource (308 pages)
englisch
9781510619302
10.1117/3.2317891
SPIE Press monograph ; PM287
Full Text
Table of Contents:
  • Preface
  • Acknowledgments
  • 1. Random functions: 1.1. Moments; 1.2. Calculus; 1.3. Three fundamental processes; 1.4. Stationarity; 1.5. Linear systems
  • 2. Canonical expansions: 2.1. Fourier representation and projections; 2.2. Constructing canonical expansions; 2.3. Orthonormal coordinate functions; 2.4. Derivation from a covariance expansion; 2.5. Integral canonical expansions; 2.6. Expansions of WS stationary processes
  • 3. Optimal filtering: 3.1. Optimal mean-square-error filters; 3.2. Optimal finite-observation linear filters; 3.3. Optimal linear filters for random vectors; 3.4. Recursive linear filters; 3.5. Optimal infinite-observation linear filters; 3.6. Optimal filtering via canonical expansions; 3.7. Optimal morphological bandpass filters; 3.8. General schema for optimal design
  • 4. Optimal robust filtering: 4.1. Intrinsically Bayesian robust filters; 4.2. Optimal Bayesian filters; 4.3. Model-constrained Bayesian robust filters; 4.4. Robustness via integral canonical expansions; 4.5. Minimax robust filters; 4.6. IBR Kalman filtering; 4.7. IBR Kalman-Bucy filtering
  • 5. Optimal experimental design: 5.1. Mean objective cost of uncertainty; 5.2. Experimental design for IBR linear filtering; 5.3. IBR Karhunen-Loève compression; 5.4. Markovian regulatory networks; 5.5. Complexity reduction; 5.6. Sequential experimental design; 5.7. Design with inexact measurements; 5.8. General MOCU-based experimental design
  • 6. Optimal classification: 6.1. Bayes classifier; 6.2. Optimal Bayesian classifier; 6.3. Classification rules; 6.4. OBC in the discrete and Gaussian models; 6.5. Consistency; 6.6. Optimal sampling via experimental design; 6.7. Prior construction; 6.8. Epistemology
  • 7. Optimal clustering: 7.1. Clustering; 7.2. Bayes clusterer; 7.3. Separable point processes; 7.4. Intrinsically Bayesian robust clusterer
  • References
  • Index