Abstract
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.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
