Sensor management using an active sensing approach
Signal Processing
Algorithmic and Architectural Design Methodology for Particle Filters in Hardware
ICCD '05 Proceedings of the 2005 International Conference on Computer Design
Decentralized State Initialization with Delay Compensation for Multi-modal Sensor Networks
Journal of VLSI Signal Processing Systems
Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
Bayesian phase tracking for multiple pulse signals
Signal Processing
PDF target detection and tracking
Signal Processing
Multi-target sensor management using alpha-divergence measures
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Distributed scalable multi-target tracking with a wireless sensor network
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Distributed target tracking using signal strength measurements by a wireless sensor network
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
Online blind speech separation using multiple acoustic speaker tracking and time-frequency masking
Computer Speech and Language
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We present a Bayesian approach to tracking the direction-of-arrival (DOA) of multiple moving targets using a passive sensor array. The prior is a description of the dynamic behavior we expect for the targets which is modeled as constant velocity motion with a Gaussian disturbance acting on the target's heading direction. The likelihood function is arrived at by defining an uninformative prior for both the signals and noise variance and removing these parameters from the problem by marginalization. Advances in sequential Monte Carlo (SMC) techniques, specifically the particle filter algorithm, allow us to model and track the posterior distribution defined by the Bayesian model using a collection of target states that can be viewed as samples from the posterior of interest. We describe two versions of this algorithm and finally present results obtained using synthetic data