An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking

Abstract

In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM).

IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

“…Therefore, we choose 5thSSRCKF as the filtering algorithm in the IMM framework, and propose interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) algorithm for maneuvering target tracking of nonlinear system. Simulation results show that the IMM5thSSRCKF exhibits better performance than IMMUKF, IMMCKF and IMM5thCKF [26] in terms of accuracy and switching response.…”

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

“…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 tracking process, therefore, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic is proposed in this paper, it uses the maximum likelihood function obtained by the improved A5CKF algorithm in the parallel filtering, updates the probability through the FL algorithm, and finally obtains the result through the output data fusion. Finally, by setting the same simulation model analysis, compared with IMMCKF [14], IMMA5CKF, and IMM5CKF [18], FLIMMA5CKF has better tracking effect and robustness, and the hysteresis is also improved. The rest of the sections are arranged as follows.…”

“…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.…”

“…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 tracking process, therefore, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic is proposed in this paper, it uses the maximum likelihood function obtained by the improved A5CKF algorithm in the parallel filtering, updates the probability through the FL algorithm, and finally obtains the result through the output data fusion. Finally, by setting the same simulation model analysis, compared with IMMCKF [14], IMMA5CKF, and IMM5CKF [18], FLIMMA5CKF has better tracking effect and robustness, and the hysteresis is also improved.…”

A Hybrid Model Algorithm for Hypersonic Glide Vehicle Maneuver Tracking Based on the Aerodynamic Model

“…The CKF [8] is proposed by Ienkaran Arasaratnam based on the Cubature rules with rigorous mathematical derivation. CKF is more accurate than the extended Kalman filter (EKF) [9] in a nonlinear filter and more accurate than the unscented Kalman filter (UKF) [10] in a high-degree nonlinear system filter [11]. Furthermore, the system of tracking an HGV is a high-degree nonlinear system.…”