Efficient extended cubature Kalman filtering for nonlinear target tracking

Abstract

For states estimation problem of continuous–discrete systems, the numerical approximation methods with high order of accuracy are commonly used to build the continuous–discrete filtering algorithms. However, there is a common contradiction between the computational efficiency and the accuracy. In order to improve the efficiency of state estimation in the continuous–discrete filtering method, continuous–discrete extended cubature Kalman filtering based on Adams–Bashforth–Moulton (ABM) numerical approximation is proposed. ABM is the linear multi-step numerical method, which can achieve the fourth-order accuracy for solving the differential state equation, and its ‘predictor–corrector’ mathematic structure is relatively simple. The performances of ABM method are theoretically analysed; the mixed-type filtering method for continuous–discrete nonlinear states estimation is proposed to integrate the best features of extended Kalman filtering and cubature Kalman filtering. More precisely, the time updates are deduced by extended Kalman filtering whereas the measurement updates are conducted by the third-degree spherical-radial cubature rule. The superior performances of proposed method are illustrated in the simulations under the conditions of different step-sizes and sampling periods.

KEYWORDS:

• State estimation
• nonlinear
• Kalman filtering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (grant nos. 61673392, 62073337, and 61703420).

Notes on contributors

Renke He

Renke He was born in 1993. His current research interests include target tracking and signal processing.

Shuxin Chen

Shuxin Chen was born in 1965. He is a Professor in Air Force Engineering University. His research interests are signal processing and communication theory.

Hao Wu

Hao Wu was born in 1988. He is a lecturer at Air Force Engineering University. His research interests are signal processing and bearings-only tracking.

Fengzhe Zhang

Fengzhe Zhang received his M.S. degree in the College of Aerospace Engineering, National University of Defense Technology (NUDT) in 2018. His research interests include videometrics, information processing and target tracking.

Kun Chen

Kun Chen was born in 1989. His research interests are signal processing and navigation technology.