Robust Kalman Filter with Fading Factor Under State Transition Model Mismatch and Outliers Interference

Peng, Yun; Wu, Panlong; He, Shan; Li, Xingxiu

doi:10.1007/s00034-020-01582-9

Cited by 13 publications

(4 citation statements)

References 24 publications

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“…Equation (15) represents the state estimation, which is the weighted estimation of the state prediction

{\overset{x}{true}}_{k / k - 1}^{'}

at the previous moment with the current measurement

{bold-italicz}_{k}

[21]. When the filter gain increases, the state estimation will be more dependent on the measurement update and ignore the state prediction appropriately [22]. This explains the mechanism of the fading filter from another angle.…”

Section: Dynamic Fading Filter Optimisation Under Fault‐free Conditionsmentioning

confidence: 99%

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Wang

Zhu

et al. 2021

IET Radar Sonar & Navi

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In the application of integrity monitoring of Global Navigation Satellite System (GNSS)/Inertial Navigation System integrated navigation system in complex environments, such as vegetation covered areas, the Kalman filtering (KF) has the disadvantages of low positioning precision, poor performance of fault identification and error bounds. This study proposes an integrity monitoring method based on dynamic fading filter optimisation. According to the real‐time updates of filtering innovation, a fading filtering algorithm is designed, which can adjust the fading factor dynamically. The dynamic fading filtering algorithm is then combined with the fault identification algorithm, which integrates chi‐square and multiple solution separation. An optimisation strategy of setting a fading period to speed up the filter convergence is proposed. The simulation results show that compared with the KF, the dynamic fading filtering reduces the standard deviations of positioning errors by 26%, 39%, and 26%, respectively, in the east, north and up directions. By reducing the filter convergence period, the proposed dynamic fading filtering can enhance the accuracy of the identification results when the subsequent GNSS fault is minor. It can shorten the identification time to about 20% when the subsequent fault is large. The dynamic fading filtering can also limit the state errors below the bounds correctly.

show abstract

“…Equation (15) represents the state estimation, which is the weighted estimation of the state prediction

{\overset{x}{true}}_{k / k - 1}^{'}

at the previous moment with the current measurement

{bold-italicz}_{k}

Section: Dynamic Fading Filter Optimisation Under Fault‐free Conditionsmentioning

confidence: 99%

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Wang

Zhu

et al. 2021

IET Radar Sonar & Navi

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show abstract

“…In practical applications, due to the low cost and instability of the sensor, the measurement is susceptible to outliers, which leads its noise to follow the non-Gaussian distribution. To improve the robustness, some filters based on the variational Bayesian (VB) method are proposed, such as the IMM-VB filter, the Gaussian-Pearson type VII mixture model-based robust VB-KF, and the inverse gamma model and generalized hyperbolic skew Student's t model-based VB-KF [12][13][14]. Although the IMM-VB filter can effectively improve the state estimation accuracy of the multiple-model system under a non-Gaussian measurement noise environment, the computational burden is large, and an accurate non-Gaussian noise model needs to be required.…”

Section: Introductionmentioning

confidence: 99%

Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Huai,

Li,

Yun

et al. 2023

Remote Sensing

Self Cite

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During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter.

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“…By introducing the VB approach, the posterior probability density functions (PDFs) of the state and noise parameters were approximated, which overcame the restrictions of the Gaussian assumption in real applications. In [27], the authors proposed a KF with a fading factor to cope with the state transition model mismatch and non-zero mean value statistical characteristics caused by outliers. The measurement noises were modeled as skew STDs, and the system state vectors were inferred by VB techniques.…”

Section: Introductionmentioning

confidence: 99%

Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking

Chen

Zhou

Gao

2023

JMSE

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A suitable jump Markov system (JMS) filtering approach provides an efficient technique for tracking surface targets. In complex surface target tracking situations, due to the joint influences of lost measurements with an unknown probability and heavy-tailed measurement noise (HTMN), the estimation accuracy of conventional interacting multiple model (IMM) methods may be seriously degraded. Aiming to address the filtering issues in JMSs with HTMNs and random measurement losses, this paper presents an IMM filtering approach with the adaptive estimation of unknown measurement loss probability. In this study, we assumed that the measurement noises obey student’s t-distributions and then proposed Bernoulli random variables (BRVs) to characterize the random measurement loss. Notably, by converting the two likelihood functions from the weighted sum form to exponential multiplication, we established hierarchical Gaussian state space models to directly utilize the variational inference method. The system state vectors, unknown distribution parameters, BRVs, and unknown measurement loss probabilities were estimated simultaneously according to the variational Bayesian inference in the IMM framework. The results of maneuvering target tracking simulations verified that the presented filtering approach demonstrated superior estimation accuracy compared to existing IMM filters.

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Robust Kalman Filter with Fading Factor Under State Transition Model Mismatch and Outliers Interference

Cited by 13 publications

References 24 publications

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking

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