Kullback–Leibler divergence for interacting multiple model estimation with random matrices

Li, Wenling; Jia, Yingmin

doi:10.1049/iet-spr.2015.0149

Cited by 16 publications

(6 citation statements)

References 31 publications

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“…Four existing filters including VB based Student’s t filter (VBStdF) [30] utilizing only single CT model, IMM filter (IMMF) [1], VB based IMM filter (IMMVBF) estimating the unknown covariance matrix of measurement noises [33], and VB based IMM filter modeling measurement noises as Student’s t -distribution (IMMVBStdF) [31] are compared with the proposed filter. 1000 Monte Carlo (MC) runs are carried out for each case of measurement noises above.…”

Section: Simulation Examplementioning

confidence: 99%

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

Li¹,

Sun²

2019

Sensors

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In maneuvering target tracking applications, the performance of the traditional interacting multiple model (IMM) filter deteriorates seriously under heavy-tailed measurement noises which are induced by outliers. A robust IMM filter utilizing Student’s t-distribution is proposed to handle the heavy-tailed measurement noises in this paper. The measurement noises are treated as Student’s t-distribution, whose degrees of freedom (dof) and scale matrix are assumed to be governed by gamma and inverse Wishart distributions, respectively. The mixing distributions of the target state, dof, and scale matrix are achieved through the interacting strategy of IMM filter. These mixing distributions are used for the initialization of time prediction. The posterior distributions of the target state, dof, and scale matrix conditioned on each mode are obtained by employing variational Bayesian approach. Then, the target state, dof, and scale matrix parameters are jointly estimated. A variational method is also given to estimate the mode probability. The unscented transform is utilized to solve the nonlinear estimation problem. Simulation results show that the proposed filter improves the estimation accuracy of target state and mode probability over existing filters under heavy-tailed measurement noises.

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Section: Simulation Examplementioning

confidence: 99%

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

Li¹,

Sun²

2019

Sensors

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“…Adaptive estimators: the adaptive estimation approach is mainly applied to the estimation of the unknown state and unknown noise parameter, which may considerably change over time in some cases. The adaptive estimators including the filtering tuning [11][12][13][14][15][16][17][18] and multiple model estimator (MM) [16,[19][20][21] are also the state estimation methods for the system uncertainties. There into, the uniqueness of the filtering tuning lies in adopting all kinds of adaptive tuning methods to suppress the system uncertainties, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…There into, the uniqueness of the filtering tuning lies in adopting all kinds of adaptive tuning methods to suppress the system uncertainties, e.g. the noise tuning [11][12][13][14], the parameter tuning [15][16][17], and the switch-mode combination technique [18]. For example, an adaptive UKF (AUKF) was presented to simultaneously online adapt the process and measurement noise co-variances by adopting the main principle of the co-variance matching, which was applied to the ship dynamic positioning system with the model uncertainties of the time-varying noise statistics, the model mismatch, and the slow varying drift forces [11].…”

Section: Introductionmentioning

confidence: 99%

Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

Feng

Deng

et al. 2020

IET signal process.

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In the multisensor target tracking system, the key of the target tracking performance depends on the state estimation accuracy to a great extent. However, the system uncertainties will seriously affect the performance of the state estimation. Up to now, little research focuses on the state estimation for the multi-sensor hybrid target tracking systems with multiple uncertainties including the multiple models, the unknown inputs and the systematic biases. In this study, the minimum error entropy based on the multiple model estimation for the multisensor hybrid uncertain target tracking systems with the multiple system uncertainties is presented. The minimum variance unbiased filter based on the general systematic bias evolution model decoupled with the unknown state is designed to estimate the optimal systematic biases and compensate the system measurements. Taking full advantage of the compensated measurement information in time and space, the multiple model observer based on the minimum error entropy is designed to obtain the optimal state estimation. The simulation results of the target tracking scenario illustrate the effectiveness of the proposed method, and the indoor target tracking and positioning experiment based on the ultrawideband further verifies that the proposed method is satisfying.

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“…the target-birth intensity [12][13][14][15][16][17][18], the target motion model [19] and the unknown target [20,21]) and observations (e.g. the unknown measurement noise statistics [22,23], the detection and clutter rate uncertainty [24,25] and the multi-sensor measurement fusion [26,27]) are of great importance. Modelling close-to-truth is an essential prerequisite for accurate tracking in the RFS-based Bayesian filters.…”

Section: Introductionmentioning

confidence: 99%

CBMeMBer filterwith adaptive target birth intensity

Wang

2018

IET signal process.

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Appropriately modelling target-birth intensity is a significant but challenging issue in multi-target tracking systems. In existing cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filters, a priori knowledge about the locations where targets appear is required to model the target-birth intensity. Since the newborn targets can appear anywhere within the observation region, it is impractical to obtain such prior information. In this study, a novel CBMeMBer filter with adaptive targetbirth intensity is presented, considering the newborn and surviving targets separately. The target-birth function of the target-birth intensity is modelled using current measurements rather than the known birth locations, and the target-birth magnitude is assigned by an allocation function rather than equally assigned. The new CBMeMBer filter can remove the restriction on the requirement of prior birth location information and can adapt well after continuous missing detection occurs. Simulations of the sequential Monte Carlo and Gaussian mixture implementations demonstrate the effectiveness of the proposed filter.

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Kullback–Leibler divergence for interacting multiple model estimation with random matrices

Cited by 16 publications

References 31 publications

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

CBMeMBer filterwith adaptive target birth intensity

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