A New Algorithm for Tracking a Maneuvering Target

Abstract

A new algorithm for tracking a maneuvering target in presence of clutter or false measurements is addressed. Due to the availability of feature or attribute information in measurement vector, a joint probability density function description of the target state and target class is given. Using the joint state-class description the predictive measurement pdf can be proven to be a Gaussian mixture distribution. A Gaussian mixture Kalman filter is used for state estimation, where maneuver detection can also be avoided. In simulation the results with three tracking algorithms are compared, which have shown that proposed method here is more effective.

Maneuvering Target Tracking with ESM Sensor

“…The primary objective of target tracking is to estimate the state trajectories of a target [1][2][3][4]. It is well known that the measurement origin uncertainty and target motion model uncertainty are two major challenges in target tracking.…”