An IMM-VB Algorithm for Hypersonic Vehicle Tracking with Heavy Tailed Measurement Noise

Peng, Yun; Wu, Panlong; He, Shuping

doi:10.1109/iccais.2018.8570338

Cited by 3 publications

(7 citation statements)

References 16 publications

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“…Ref. [27] proposed an interactive multi-model variational Bayesian (IMM-VB) algorithm to solve the problem of tracking accuracy degradation caused by outlier interference in real systems. Ref.…”

Section: Tracking Methods With Complex Noisementioning

confidence: 99%

A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error

et al. 2022

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Vehicle tracking in the field of intelligent transportation has received extensive attention in recent years. Multi-sensor-based vehicle tracking system is widely used in some critical environments. However, in the actual scenes, the observation error of each sensor is often different and time varying because of the environmental change and the channel difference. Therefore, in this paper, we propose a multi-sensor interacted vehicle-tracking algorithm with time-varying observation error (MI-TVOE). The algorithm establishes a jointed and time-varying observation error model for each sensor to indicate the variation of observation noise. Then, we develop a multi-sensor interacted vehicle-tracking algorithm which can predict the statistical information of a time-varying observation error and fuse the tracking result of each sensor to provide a global estimation. Simulation results show that the proposed MI-TVOE algorithm can significantly improve the tracking performance compared to the single-sensor-based tracking method, the traditional unscented Kalman filter (UKF), the apdative UKF method (AUKF) and the multi-error fused UKF method (MEF-UKF), which will be well applied to the complex tracking scenes and will reduce the computational complexity with time-varying observation error. The experiments in this paper also prove the superiority of the proposed MI-TVOE algorithm in complex environments.

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Section: Tracking Methods With Complex Noisementioning

confidence: 99%

A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error

et al. 2022

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“…The initial weight of each sub-model is 0.5. The process noise follows the Gaussian distribution, while the measurement noise follows the Gaussian mixture distribution [12]. At this time, w i k+1 and v i k+1 (i = 1, 2) are formulated as…”

Section: Case 1 Maneuvering Target Tracking Simulation Experimentsmentioning

confidence: 99%

“…The root mean square error (RMSE) and the time-averaged RMSE (TRMSE) are used as performance evaluating criteria. The IMM [9], IMM-VB [12], and IMM-MCC [21] filters are used to compare with the WMCC-IMM filter. For a fair comparison, the kernel function from the IMM-MCC filter is the same as that from the WMCC-IMM filter, and the kernel bandwidths from the IMM-MCC and WMCC-IMM filters are set as 5.…”

Section: Case 1 Maneuvering Target Tracking Simulation Experimentsmentioning

confidence: 99%

“…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%

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

Huai,

Li,

Yun

et al. 2023

Remote Sensing

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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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“…The variational Bayesian (VB) approach [10][11][12][13][14][15] is an inference method that utilizes a simple distribution to approximate the true posterior distribution of hidden variables, usually assuming that the hidden variables are independent of each other. The VB has been developed for a wide range of models to perform approximate posterior inference at low computational cost in comparison with the sampling methods.…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian Based IMM Robust GPS Navigation Filter

Jwo¹,

Chang²

2022

Computers, Materials &Amp; Continua

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This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the timevarying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.

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An IMM-VB Algorithm for Hypersonic Vehicle Tracking with Heavy Tailed Measurement Noise

Cited by 3 publications

References 16 publications

A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error

A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error

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

Variational Bayesian Based IMM Robust GPS Navigation Filter

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