Detection, Estimation, and Modulation Theory

1 introduction
　1.1 topical outline
　1.2 possible approaches
　1.3 organization
2 classical detection and estimation theory
　2.1 introduction
　2.2 simple binary hypothesis tests
　　decision criteria.performance:receiver operating characteristic
　2.3 m hypotheses
　2.4 estimation theory
　　random parameters:
bayesestimation.real(nonrandom)parameter estimation.multiple parameter estimation.summary of estimation theory.
　2.5 composite hypotheses
　2.6 the general gaussian problem
　　equal covariance matrices.equal mean vectors.summary.
　2.7 performance bounds and approximations
　2.8 summary
　2.9 problems
　referecnces
3 representations of random processes
.　3.1 introduction
　3.2 deterministic functions:orthogonal representations
　3.3 random process characterization
random processes:conventional characterizations.series representation of sample functions of random processes.gaussian processes.
　3.4 homogeneous integral equations and eigenfunctions
　　rational spectra.bandlimited spectra.nonstationary processes.white noise processes.the optimum linear filler.properties of eigenfunctions and eigenvalues.
　3.5 periodic processes
　3.6 infinite time interval:spectral decomposition
　　spectral decomposition.an application of spectral decomposition:map estimation of a gaussian process.
　3.7 vector random processes
　3.8 summary
　3.9 problems
　referecnces
4 detection of signals-estimation of signal parameters
　4.1 introduction
　　models.format.
　4.2 detection and estimation in white gaussian noise
　　detection of signals in additive white gaussian noise.linear estimation.nonlinear estimation.summary:known signals in white gaussian noise
　4.3 detection and estimation in nonwhite gaussian noise
　　“whitening”approach.a direct derivation using the karhunen-loeve expansion.a direct derivation using the suffcient statistic.detection performance.estimation.solution techniques for integral equations.sensitivity.known linear channels
　4.4 signals with unwanted parameters:the composite hypothesis problem
　　random phase angles.random amplitude and phase.
　4.5 multiple channels
　　formulation.application.
　4.6 multiple parameter estimation
　　additive white gaussian noise channel.extensions.
　4.7 summary and omissions
　　summary.topics omitted.
　4.8 problems
　referecnces
5 estimation of continuous waveforms
　5.1 introduction
　5.2 derivation of estimator equations
　　no-memory modulation systems.modulation systems with memory.
　5.3 a lower bound on the mean-square estimation error
　5.4 multidimensional waveform estimation
　　examples of multidimensional problems.problem formulation.derivation of estimator equations.lower bound on the error matrix.colored noise estimation.
　5.5 nonrandom waveform estimation
　5.6 summary
　5.7 problems
　referecnces
6 linear estimation
　6.1 properties of optimum processors
　6.2 realizable linear filters:stationary 　　processes,infinite past:wiener filters
solution of wiener-hopf equation.errors in optimum systems.unrealizable filters.closed-form error expressions.optimum feedback systems.comments.
　6.3 kalman-bucy filters
　　differential equation representation of linear systems and random process generation.derivation of estimator equations.applications.generalizations.
　6.4 linear modulation:communications context
　　dsb-am:realizable demodulation.dsb-am:demodulation with delay.amplitude modulation:generalized carriers.amplitude modulation:single-sideband suppressed-carrier.
　6.5 the fundamental role of the optimum linear filter
　6.6 comments
　6.7 problems
　referecnces
7 discussion
　7.1 summary
　7.2 preview of part ii
　7.3 unexplored issues
　references
appendix:a typical course outline
glossary
author index
subject index