Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches

Xin Zhao,Hui Zhao,Huan Zhou,Hanqiao Huang,Xiang Yin

doi:10.3390/rs9070657

Abstract

In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems involving model mismatches is described, after that, the necessary and sufficient condition with third order accuracy of the standard UKF is given and proven by using the matrix theory. In the filtering process of N-AUKF, an adaptive matrix gene is introduced to the standard UKF to adjust the covariance matrixes of the state vector and innovation vector in real time, which makes full use of normal innovations. Then, a covariance matching criterion is designed to judge the filtering divergence. On this basis, an adaptive weighted coefficient is applied to restrain the divergence. Compared with the standard UKF and existing adaptive UKF, the proposed UKF algorithm improves the filtering accuracy, rapidity and numerical stability remarkably, moreover, it has a good adaptive capability to deal with sensor faults or model mismatches. The performance and effectiveness of the proposed UKF is verified in a target tracking mission.

Highlights

Target Tracking is an essential technology in both military and civil fields, and it is critical to the success of many tasks, such as target recognition and attack [1,2,3,4,5]
In a target tracking process, the filtering precision will decrease and filtering divergence may appear for the standard and existing adaptive unscented Kalman filter (UKF), if there are sensor faults and systems involving model mismatches
In the new adaptive unscented Kalman filter (N-AUKF), the covariance matrixes of the state vector and innovation vector can be adjusted in real time by introducing an adaptive matrix gene and the optimality with third order accuracy is guaranteed and proven theoretically

Summary

Introduction

Target Tracking is an essential technology in both military and civil fields, and it is critical to the success of many tasks, such as target recognition and attack [1,2,3,4,5]. The main mission of target tracking is to show the value of current state estimation and latter state prediction by using sensor measurements. In this sense, target tracking is essentially a filtering problem. To perform perfect target tracking tasks with valuable state estimation, real-time filtering algorithms with high performance are needed [6]. Traditional filtering algorithms are greatly limited in applications for the complexity of the motion states and maneuvering characteristics for missiles and aircraft, leading to growing difficulties in target tracking. It must be resolved as to how to design an effective, adaptive and stable filtering algorithm for target tracking within complicated environments

Results

Discussion

Conclusion