Sub-optimal Kalman Filter design for tracking a maneuvering target

Abstract

This paper presents an application of the Kalman filter to the problem of tracking a maneuvering target. A realistic target model is developed and also is proposed the estimation algorithm, called the proposed sub-optimal Kalman filter (PSKF), based on Kalman gain rotation using the coordinate transformation matrix. The new target model improves the bias error in the coordinated turn maneuver compared with Singer model and Berg model. Also the PSKF is compared with the conventional sub-optimal Kalman filter (CSKF), the extended Kalman filter (EKF), and the modified gain extended Kalman filter (MGEKF) using the Monte Carlo simulation for a typical target trajectory. The tracking errors of the four filters are almost similar, while the computational burden of the PSKF is less than that of any other filter such as the CSKF, the EKF, and the MGEKF.