A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems

Liao, Tianrui; Hirota, Kaoru; Wu, Xiangdong; Shao, Shuai

doi:10.3390/rs14174345

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…Recently, the research on filtering techniques under the maximum correntropy (MC) criterion has become an important orientation for the state estimation of stochastic systems in the presence of non-Gaussian noise [33][34][35][36]. Correntropy is a statistical metric to measure the similarity of two random variables in information theory; unlike the commonly used MMSE criterion, which uses second-order statistics, the MC criterion uses secondorder statistics and higher-order information, thus offering the probability of improving estimation accuracy for systems in the presence of non-Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

Zhang,

Zeng

et al. 2023

Remote Sensing

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In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.

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Section: Introductionmentioning

confidence: 99%

Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

Zhang,

Zeng

et al. 2023

Remote Sensing

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“…Liao et al proposed a dynamic self-tuning MCKF (DSTMCKF), which was used to improve positioning accuracy in wireless sensor networks (WSN). This algorithm employed the observation as a priori information together with the innovation to calculate the noise covariance effectively; then, the new noise covariance estimation could be taken into a standard MCKF procedure [29]. Wang et al introduced the MCC to the extended Kalman filter (MCEKF) under the condition that measurement outliers exist for nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

A Redundant Measurement-Based Maximum Correntropy Extended Kalman Filter for the Noise Covariance Estimation in INS/GNSS Integration

et al. 2023

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The resolution accuracy of the inertial navigation system/global navigation satellite system (INS/GNSS) integrated system would be degraded in challenging areas. This paper proposed a novel algorithm, which combines the second-order mutual difference method with the maximum correntropy criteria extended Kalman filter to address the following problems (1) the GNSS measurement noise estimation cannot be isolated from the state estimation and suffers from the auto-correlated statistic sequences, and (2) the performance of EKF would be degraded under the non-Gaussian condition. In detail, the proposed algorithm determines the possible distribution of the measurement noise by a kernel density function detection, then depending on the detection result, either the difference sequences–based method or an autoregressive correction algorithm’s result is utilized for calculating the noise covariance. Then, the obtained measurement noise covariance is used in MCEKF instead of EKF to enhance filter adaptiveness. Meanwhile, to enhance the numerical stability of the MCEKF, we adopted the Cholesky decomposition to calculate the matrix inverse in the kernel function. The road experiment verified that our proposed method could achieve more accurate navigation resolutions than the compared ones.

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Generalized interacting multiple model Kalman filtering algorithm for maneuvering target tracking under non-Gaussian noises

Wang,

He,

Peng

et al. 2024

ISA Transactions

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A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems

Cited by 7 publications

References 33 publications

Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

A Redundant Measurement-Based Maximum Correntropy Extended Kalman Filter for the Noise Covariance Estimation in INS/GNSS Integration

Generalized interacting multiple model Kalman filtering algorithm for maneuvering target tracking under non-Gaussian noises

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