A Novel Prediction Algorithm in Gaussian-Mixture Probability Hypothesis Density Filter for Target Tracking

摘要

This paper proposes a novel prediction algorithm in Gaussian mixture probability hypothesis density filter for target tracking in linear dynamical model. In tracking algorithms, with a possibility of multiple measurements per target, a model for the number of measurements per target is needed. Lately, different implementations have been proposed for such targets. To do a better estimation of performance, this work relaxes the Poisson assumptions of target tracking probability hypothesis density filter in targets and measurement numbers. We offered a gamma Gaussian mixture implementation capable of estimating the measurement rates and the kinematic state of the target. The Variational Bayesian approximation converts the Gamma-Gaussian mixture into the improved Gaussian mixture with its news mean and covariance components. It is compared to its GM-PHD filter counterpart in the simulation study and the results clearly show the best performance of the improved Gaussian Mixture algorithm.