Results 71 to 80 of about 306 (149)
IEEE Access, 2019 The adaptive algorithms have been widely studied in Gaussian environment. However, the impulsive noise and other non-Gaussian noise may largely deteriorate the performance of algorithm in practical applications. To address this problem, in this paper, we
Xiaoding Wang, Jun Han
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Causal Identification Based on Compressive Sensing of Air Pollutants Using Urban Big Data
IEEE Access, 2020 This study addresses the causal identification of air pollutants from surrounding cities affecting Beijing's air quality. A novel compressive sensing causality analysis (CS-Causality) method, which combines Granger causality analysis (GCA) and maximum ...
Mingwei Li +4 more
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Channel Parameter Estimation in the Presence of Phase Noise Based on Maximum Correntropy Criterion
, 2022 Oscillator output generally has phase noise causing the output power spectral density (PSD) to disperse around a Dirac delta function. In this paper, the AWGN channel is considered, where the sent signal accompanying with phase noise is added to the ...
Hodtani, Ghosheh Abed, Alizadeh, Amir
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, 2023 The low-frequency narrowband components (known as lines) in the radiated noise of underwater acoustic targets are an important feature of passive sonar detection.
Xiaoyan Wang +3 more
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Joint Estimation of the DOA and the Number of Sources for Wideband Signals Using Cyclic Correntropy
IEEE Access, 2019 In this paper, a new direction of arrival (DOA) and the number of signals of interest (SOIs) estimation method is proposed for wideband sources in impulsive noise environments.
Fangxiao Jin +3 more
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IEEE Access, 2020 This paper focuses on the problem of distributed adaptive estimation over dynamic multi-task networks, where a set of nodes is required to collectively estimate some parameters of interest from noisy measurements.
Qing Shi +3 more
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Chandrasekhar-based maximum correntropy Kalman filtering with the adaptive kernel size selection
, 2023 This technical note is aimed to derive the Chandrasekhar-type recursion for the maximum correntropy criterion (MCC) Kalman filtering (KF). For the classical KF, the first Chandrasekhar difference equation was proposed at the beginning of 1970s.
Kulikova, Maria
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, 2023 The maximum correntropy criterion (MCC), as an effective method for dealing with anomalous measurement noise, is widely applied in the design of filters.
Dongjie Wu +4 more
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Multi-convex combined filter based on maximum correntropy criterion
Dianzi Jishu Yingyong, 2018 Correntropy based algorithms are widely used in non-Gaussian signal processing, but they also suffer from the conflict between the step size and the misadjustment.
Wu Wenjing +3 more
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, 2023 The maximum correntropy criterion (MCC) has been employed to design outlier-robust adaptive filtering algorithms, among which the recursive MCC (RMCC) algorithm is a typical one.
Qin, Zhen +3 more
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