Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking

Abstract

Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the "snake-like" maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage-Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).Symmetry 2019, 11, 767 2 of 16 third-order spherical-radial cubature rule was proposed by Haykin et al. [13,14]; it is different from UKF, CKF has a strict mathematical formula to prove, it has been shown that CKF has better performance than UKF when the dimension of the system is greater than three [15]. In order to pursue better performance of CKF, 5CKF was proposed in the literature [16][17][18], and the simulation shows that 5CKF performance is better than CKF. For pursuing a better estimation performance of 5CKF filtering algorithm, combined with the improvement of Sage-Husa noise estimation in the literature [19], and the combination with the corresponding filtering algorithm in the literature [20][21][22][23], the adaptive five-degree cubature Kalman algorithm based on error covariance estimation is proposed, and it is used as a filtering algorithm under the IMM framework.In the IMM algorithm, the transition probability between models is performed by the Markov transition matrix. When the target is tracked at a certain time, it works by a specific model, and its probability is approximately 1, other approximations are 0. However, this method requires a certain amount of time, and there is hysteresis, resulting in a decrease in tracking effect. For solving the problem of slow convergence caused by hysteresis, a fuzzy logic (FL) is proposed [24], and combine the FL algorithm with the IMM algorithm, and the corresponding fuzzy rules are formulated, when the model is transformed, the FL algorithm is used to judge whether the probability of the model is 1 or 0 to accelerate the update of the probability, this will speed up the convergence rate, the related references also proves the effectiveness of the combined algorithm [20,25].Although the IMMCKF algorithm and the IMM5CKF algorithm have already achieved good results [14,18], they still cannot effectively solve the problem of low filtering precision and slow convergence in the tr...

Design of Pseudo-Command Restricted Controller for Tailless Unmanned Aerial Vehicles Based on Attainable Moment Set

“…Large maneuverable penetration flights often involve three types of maneuvers: spiral, jump, and snake-shaped maneuvers [4]. Snake-shaped maneuvers are not only suitable for TUAVs to carry out, but also effectively create challenges in hostile radar tracking and interception guidance [5,6]. Therefore, this article selected snake-shaped maneuvers as the simulation task scenario for flight controller design.…”