An Improved Kalman Filter for TOA Localization using Maximum Correntropy Criterion

Qi, Yue; Ji, Mengmeng; Xu, Chunjing; Wan, Jiawang; He, Jie

doi:10.1109/wocc.2019.8770525

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…At the same time, some state estimation problems in more complex environments are also the focus of research, such as estimation with bias compensation, nonlinear filtering based on state constraints, robust constraints, state estimation in complex domain impulsive noise etc. As mentioned in References [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. These issues need to be further studied in the following work.…”

Section: Discussionmentioning

confidence: 95%

Improved Robust High-Degree Cubature Kalman Filter Based on Novel Cubature Formula and Maximum Correntropy Criterion with Application to Surface Target Tracking

Wang

Zhang

Liu

2022

JMSE

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Robust nonlinear filtering is an important method for tracking maneuvering targets in non-Gaussian noise environments. Although there are many robust filters for nonlinear systems, few of them have ideal performance for mixed Gaussian noise and non-Gaussian noise (such as scattering noise) in practical applications. Therefore, a novel cubature formula and maximum correntropy criterion (MCC)-based robust cubature Kalman filter is proposed. First, the fully symmetric cubature criterion and high-order divided difference are used to construct a new fifth-degree cubature formula using fewer symmetric cubature points. Then, a new cost function is obtained by combining the weighted least-squares method and the MCC loss criterion to deal with the abnormal values of non-Gaussian noise, which enhances the robustness; and statistical linearization methods are used to calculate the approximate result of the measurement process. Thus, the final fifth-degree divided difference–maximum correntropy cubature Kalman filter (DD-MCCKF) framework is constructed. A typical surface-maneuvering target-tracking simulation example is used to verify the tracking accuracy and robustness of the proposed filter. Experimental results indicate that the proposed filter has a higher tracking accuracy and better numerical stability than other common nonlinear filters in non-Gaussian noise environments with fewer cubature points used.

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Section: Discussionmentioning

confidence: 95%

Improved Robust High-Degree Cubature Kalman Filter Based on Novel Cubature Formula and Maximum Correntropy Criterion with Application to Surface Target Tracking

Wang

Zhang

Liu

2022

JMSE

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show abstract

“…Correntropy is a local similarity measure that includes all even moments and is robust to outliers [19]. The results show that MCC is not only effective in the non-Gaussian environment but also has a good suppression ability to colored background noise [20,21]. Qi et al applied the Kalman filter based on MCC to TOA localization, which effectively suppressed the Gaussian-colored noise generated by multipath and nonlinear in the system [20].…”

Section: Introductionmentioning

confidence: 99%

“…The results show that MCC is not only effective in the non-Gaussian environment but also has a good suppression ability to colored background noise [20,21]. Qi et al applied the Kalman filter based on MCC to TOA localization, which effectively suppressed the Gaussian-colored noise generated by multipath and nonlinear in the system [20]. Ahmad et al proposed an estimator based on correntropy to achieve efficient parameter estimation under colored and non-Gaussian noise [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Line Enhancer Based on Maximum Correntropy Criterion and Frequency Domain Sparsity for Passive Sonars

Zhang,

An,

et al. 2023

Electronics

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The low-frequency narrowband components (known as lines) in the radiated noise of underwater acoustic targets are an important feature of passive sonar detection. Conventional adaptive line enhancer (ALE) based on the least mean square algorithm has limited performance under colored background noise and low signal-to-noise ratio (SNR). In this paper, by combining the frequency domain sparse model of lines and maximum correntropy criterion (MCC), a β-adaptive l0-MCC-ALE is proposed to solve the above-mentioned problem. The proposed ALE uses a sparse-driven MCC algorithm to update the weight vector in the frequency domain to further suppress the colored background noise. For the problem that the value of parameter β is sensitive to the performance, β is updated adaptively according to the frequency response of ALE in each iteration. Simulation and real data processing results show that the proposed ALE is insensitive to the given parameter β and has excellent performance for line enhancement. Compared with conventional ALE, the SNR of lines can be improved by 7~8 dB.

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On the Use of a Maximum Correntropy Criterion in Kalman Filtering Based Strategies for Robot Localization and Mapping

Reis

Moayyed

Aguiar

2020

Lecture Notes in Electrical Engineering

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An Improved Kalman Filter for TOA Localization using Maximum Correntropy Criterion

Cited by 4 publications

References 21 publications

Improved Robust High-Degree Cubature Kalman Filter Based on Novel Cubature Formula and Maximum Correntropy Criterion with Application to Surface Target Tracking

Improved Robust High-Degree Cubature Kalman Filter Based on Novel Cubature Formula and Maximum Correntropy Criterion with Application to Surface Target Tracking

Adaptive Line Enhancer Based on Maximum Correntropy Criterion and Frequency Domain Sparsity for Passive Sonars

On the Use of a Maximum Correntropy Criterion in Kalman Filtering Based Strategies for Robot Localization and Mapping

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