| Target Tracking with Bayesian Methods
In this thesis we study a Bayesian estimation formulation of the target tracking problem. A Bayesian approach to tracking applications naturally leads to a recursive estimation formulation. The recently invented Particle Filter provides a numerical solution to the non-tractable recursive Bayesian estimation problems. As an alternative, traditional methods such as the Extended Kalman Filter, which is based on a linearized model and an assumption on Gaussian noise, yield approximate solutions. However, in highly nonlinear problems such as in our tracking applications, the EKF tends to be very inaccurate and underestimates the true covariance of the state.
In general the Sequential Monte Carlo Methods are adopted to and tracking applications and compared to traditional approaches. Particularly, the performance of different particle filtering methods are compared. In various target tracking applications, we extend or modify these particle filtering algorithms. Range-only tracking problem is addressed using Bayesian techniques and also the passive ranging application when only angle information is available is discussed.
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