Performance Evaluation of Unscented Kalman Filter for Gaussian and non-Gaussian Tracking Application

Abstract

State estimation theory is one of the best mathematical approaches to analyze variants in the states of the system or process. The state of the system is defined by a set of variables that provide a complete representation of the internal condition at any given instant of time. Filtering of Random processes is referred to as Estimation, and is a well defined statistical technique. There are two types of state estimation processes, Linear and Nonlinear. Linear estimation of a system can easily be analyzed by using Kalman Filter (KF) but  is optimal only when the model is linear .But  Most of the state estimation problems are nonlinear, thereby limiting the practical applications of the KF and EKF. Unscented Kalman filter and Particle filter are best known for nonlinear estimates. The approach in this paper is to analyze the algorithm for maneuvering target tracking using   bearing only measurements for both Gaussian /Nongaussian distributions where UKF provides better probability of state estimation.  Montecarlo computer simulations are used to analyse the performance .The simulations results showed that UKF provides better performance for Gaussian distributed models compared to the nongaussian models.DOI:http://dx.doi.org/10.11591/ijece.v3i1.326