The nature of statistical learning theory
The nature of statistical learning theory
Support vector regression for surveillance purposes
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Wideband array processing using a two-sided correlationtransformation
IEEE Transactions on Signal Processing
Wideband array signal processing using MCMC methods
IEEE Transactions on Signal Processing
Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Notes on the interpolation of discrete periodic signals using sincfunction related approaches
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Comments on “Sinc interpolation of discrete periodicsignals”
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Reversible jump MCMC for joint detection and estimation of sourcesin colored noise
IEEE Transactions on Signal Processing
Recursive EM and SAGE-inspired algorithms with application to DOA estimation
IEEE Transactions on Signal Processing - Part I
Sinc interpolation of discrete periodic signals
IEEE Transactions on Signal Processing
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In this work, a support vector regression (SVR) based sequential Monte Carlo method is presented to track wideband moving sources using a linear and passive sensor array for a signal model based on buffered data. The SVR method is employed together with a particle filter (PF) method to improve the PF tracker performance when a small sample set is available. SVR is used as a sample producing scheme for the current state vector. To provide a good approximation of the posterior density by means of improving the sample diversity, samples (particles) are drawn from an importance density function whose mean and covariance are calculated by using the pre-estimating state vector and the state vector's previous estimate. Thus, a better posterior density than the classical one can be obtained. Simulation results show that the method proposed in this work performs better than the classical one when a small sample set is available. Moreover, the results also show that a modified signal model that utilizes buffering data is superior to the signal model in Ng et al. [Application of particle filters for tracking moving receivers in wireless communication systems, in: IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Rome, Italy, June 2003, pp. 575-579].