A Novel Fifth-Degree Strong Tracking Cubature Kalman Filter for Two-Dimensional Maneuvering Target Tracking

Abstract

A novel fifth-degree strong tracking cubature Kalman filter is put forward to improve the two-dimensional maneuvering target tracking accuracy. First, a new fifth-degree cubature rule, with only one point more than the theoretical lower bound, is used to approximate the intractable nonlinear Gaussian weighted integral in the nonlinear Kalman filtering framework, and a novel fifthdegree cubature Kalman filter is proposed. Then, the suboptimal fading factor is designed for the filter to adjust the filtering gain matrix online and force the residual sequences mutually orthogonal, thus improving the ability of the filter to track the mutation state, and the fifth-degree strong tracking cubature Kalman filter is derived. The suboptimal fading factor is calculated in a new method, which reduces the number of calculations for the cubature points from three times to twice without calculating the Jacobian matrix. The simulation results indicate that the proposed filter has the ability to track the maneuvering target and achieve higher target tracking accuracy and thus verifies the effectiveness of the proposed filter.

Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets

“…Sequentially, it extracted the third-order term information and achieved second-order accuracy. To implement higher tracking accuracy for the maneuvering target tracking, many improved strong tracking cubature Kalman filter (STCKF), such as strong tracking spherical simplex-radial CKF [ 28 ], fifth-degree STCKF [ 29 ], Bayesian-based strong tracking interpolatory CKF [ 30 ], model-based strong tracking square-root CKF [ 31 ], have recently been proposed. In addition, Ma et al [ 32 ] added STF into the sub-filter of IMM to overcome the low tracking accuracy when dealing with maneuvering situations.…”

Application of Improved Simplex Quadrature Cubature Kalman Filter in Nonlinear Dynamic System

“…Nonlinear filtering and estimation based on Bayes framework have been widely applied in many fields, such as target tracking [1][2][3][4][5], navigation [5,6] and positioning [7,8], power system [9][10][11], and pattern recognition [12]. Recent years have seen a surge of interest in solving the nonlinear estimation problems.…”