Abstract
The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while the cubature particle filter has high computational complexity induced by stochastic sampling. To address these problems, a novel CKF named double-Layer cubature Kalman filter (DLCKF) is proposed. In the proposed DLCKF, the prior distribution is represented by a set of weighted deterministic sampling points, and each deterministic sampling point is updated by the inner CKF. Finally, the update mechanism of the outer CKF is used to obtain the state estimations. Simulation results show that the proposed algorithm has not only high estimation accuracy but also low computational complexity, compared with the state-of-the-art filtering algorithms.
Highlights
Nonlinear estimation has been widely used in many fields, such as information fusion, computer vision, engineering, and economics [1,2,3,4]
Following the above line of thinking, this paper proposes a new double-layer cubature Kalman filter (DLCKF) algorithm which uses the weighted sampling points to represent the posterior density function at the previous moment
The double-Layer cubature Kalman filter (DLCKF) that is based on CKF is proposed in this paper, which uses the cubature points to approximate the prior density function of the state
Summary
Nonlinear estimation has been widely used in many fields, such as information fusion, computer vision, engineering, and economics [1,2,3,4]. As the estimation accuracy of the conventional CKF only reaches three order, reference [19] proposed a high-order cubature Kalman filter in order to augment the estimation accuracy In this filtering method, multiple integral and moment matching are respectively adopted to derive arbitrary-order spherical rule and the radial rule in the frame of points-based Gauss approximation. As the key component of the PF is to approximate importance PDF by selecting particles, the performance of this method is affected by the curse of importance PDF To solve this problem, a cubature particle filter (CPF) [29,30] has been proposed, where the estimation of the CKF is adopted as the proposal distribution.
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