Application of Improved UPF Algorithm in Nonlinear Non-Gauss System

Hou, Jiaxin; Zhou, Weidong

doi:10.1109/icsp.2018.8652317

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…Therefore, the stability of the algorithm can only be guaranteed by ensuring the stability of the high-order UT strategy. In addition, the equality constraints of equations (26), (27) and (43) are satisfied in the process of solving the adaptive factor, and the global optimization can be satisfied in model mismatch. In view of integration, the UT is also considered as a high dimensional integral method.…”

Section: Discussion On the Stability Of Algorithmmentioning

confidence: 99%

“…The UKF based on the UT is far better than the standard EKF in extensive applications [9], [26]. When compared with the particle filter [27], the UKF has lower computational requirements [28], [29]. The CKF can be regarded as a special form of UKF [15], [16].…”

Section: Other Nonlinear Transformation Methods Mainly Include Thementioning

confidence: 99%

“…where ε k = z k +ẑ k|k−1 . Equation (26) points out that the filtering algorithm has the minimum variance under Gaussian constraints and equation (27) requires that the residuals at different time points be orthogonal to each other. If the performance of the filtering algorithm satisfies the two conditions, it can be considered to have a strong tracking ability and optimal estimation performance.…”

Section: B Optimal Adaptive Factor Based On the Estimated Covariancementioning

confidence: 99%

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A New Adaptive High-Order Unscented Kalman Filter for Improving the Accuracy and Robustness of Target Tracking

Zhou

Hou

2019

IEEE Access

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In target tracking, the tracking process needs to constantly update the data information. For maneuvering target, model mismatch and loss of high-order moment information disrupt the accuracy of the state estimation. In this paper, an adaptive high-order unscented Kalman filter (AHUKF) algorithm is proposed for the case of errors occurring in the capturing of model dynamic behavior using the classical unscented Kalman filter (UKF) algorithm. By introducing the free parameter, the analytical solution of the high-order unscented transformation (UT) was obtained, the basis for choosing free parameters was analyzed, and the stability of the algorithm was discussed. A method for obtaining the optimal adaptive factor based on the prediction residual estimation covariance matrix was proposed, which reduces the influence of the dynamic model error and was applied to the target tracking model. In this paper, the proposed AHUKF is applied to a target tracking problem with state mutation, different sampling intervals, and different turn rates, respectively. Simulation results for target tracking illustrate that the proposed algorithm is more accurate and robust than the UKF, high-order unscented Kalman filter (HUKF) and adaptive unscented Kalman filter (AUKF). INDEX TERMS UKF, optimal adaptive factor, adaptive filtering, orthogonality principle, high-order UT sampling.

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Section: Discussion On the Stability Of Algorithmmentioning

confidence: 99%