Radar Target-tracking and Measurement-origin Uncertainty

Abstract

Target tracking refers to the process of estimating the state of a moving object from remote and noisy measurements. In this thesis we consider the Bayesian filtering framework to perform target tracking under nonlinear models, a target moving in continuous time, and measurements that are available in discrete time intervals (known as continuous-discrete). The Bayesian filtering theory establishes the mathematical basis to obtain the posterior probability density function of the state, given the measurement history. This probability density function contains all the information required about the state of the target. It is well documented that there is no exact solution for posterior density under the models mentioned. Hence, the approximation of such density functions have been studied for over four decades. The literature demonstrates that this has led to the development of multiple filters. In target tracking, due to the remote sensing performed, an additional complication emerges. The measurements received are not always from the desired target and could have originated from unknown sources, thus making the tracking more difficult. This problem is known as a measurement origin uncertainty. Additionally to the filters, different methods have been proposed to address the measurement origin uncertainty due to its negative impact, which could cause a false track. Unfortunately, a final solution has yet to be achieved. The first proposal of this thesis is a new approximate Bayesian filter for continuous-discrete systems. The new filter is a higher accuracy version of the cubature Kalman filter. This filter is developed using a fifth-degree spherical radial cubature rule and the Ito-Taylor expansion of order 1.5 for dealing with stochastic differential equations. The second proposal is an improved version of the probabilistic data association method. The proposed method utilizes the maximum likelihood values for selecting the measurements that are used for the data association. In the first experiment, the new filter is tested in a challenging 3-dimensional turn model, demonstrating superiority over other existing filters. In a second and third experiments, the proposed data association method is tested for target tracking in a 2-dimensional scenarios under heavy measurement origin uncertainty conditions. The second and third experiments demonstrate the superiority of the proposed data association method compared to the probabilistic data association.