Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Mathematics of Data Fusion
Wireless Communications
Coding for Wireless Channels (Information Technology: Transmission, Processing and Storage)
Coding for Wireless Channels (Information Technology: Transmission, Processing and Storage)
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Multitarget miss distance via optimal assignment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
IEEE Transactions on Information Theory
On multivariate Rayleigh and exponential distributions
IEEE Transactions on Information Theory
Multiuser Detection in a Dynamic Environment– Part I: User Identification and Data Detection
IEEE Transactions on Information Theory
A multi-dimensional trust evaluation model for large-scale P2P computing
Journal of Parallel and Distributed Computing
| Hi-index | 754.84 |
The problem of jointly estimating the number, the identities, anti the data of active users in a time-varying multiuser environment was examined in a companion paper (IEEE Trans. Information Theory, vol. 53, no. 9, September 2007), at whose core was the lise of the theory of finite random sets on countable spaces. Here we extend that theory to encompass the more general problem of estimating unknown continuous parameters of the active-user signals. This problem is solved here by applying the theory of random finite sets constructed on hybrid spaces. We do so deriving Bayesian recursions that describe the evolution with time of a Posteriori densities of the unknown parameters and data. Unlike in the above cited paper, wherein one could evaluate the exact multiuser set posterior density, here the continuous-parameter Bayesian recursions do not admit closed-form expressions. To circumvent this difficulty, we develop numerical approximations for the receiviers that are based on Sequential Monte Carlo (SMC) methods ("Particle filtering"). Simulation results, referring to a code-divisin multiple-access (CDMA) system, are presented to illustrate the theory.