Tracking and data association
Bayesian Multiple Target Tracking
Bayesian Multiple Target Tracking
Data association and target identification using range profile
Signal Processing
Gaussian sum particle filtering
IEEE Transactions on Signal Processing
A Bayesian Approach to Multiple Target Detection and Tracking
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Tracking in a cluttered environment with probabilistic data association
Automatica (Journal of IFAC)
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This paper presents a new approach to the real time single and multiple target detection and tracking problems with measurement input data. The new approach addresses the measurement uncertainty-of-origin issue by capturing all measurement input data information in the Bayesian conditional probability density function (PDF), used in the recursive propagation of the posterior target detection and tracking information PDF over time via Bayesian and Markov PDF updates. The application of Bayes' formula over time resolves measurement association ambiguities. Under linear, Gaussian assumptions, the posterior PDF is a repeating Gaussian mixture. This leads to computational simplifications and efficiencies in implementation. Simulation results demonstrate operation and performance.