An ultrasonic positioning algorithm based on maximum correntropy criterion extended Kalman filter weighted centroid

Ma, Fuqiang; Liu, FangJie; Zhang, Xiaotong; Wang, Peng; Bai, Hongying; Guo, Hang

doi:10.1007/s11760-018-1272-2

Cited by 23 publications

(16 citation statements)

References 18 publications

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“…The updating value of the corresponding estimation information vector iŝ( | ) (38) In this section, the square root cubature information filter is recalled. It should be noted that SCIF is designed for the Gaussian nonlinear system.…”

Section: (1) Time Updatingmentioning

confidence: 99%

“…Nevertheless, PF has some inherent practical problems, such as complexity of calculation, selection strategy of importance function and so on. In the field of information theoretic learning, maximum correntropy (MC) criterion has been successfully utilized for the non-Gaussian signal processing problems [28][29][30][31][32][33][34][35][36][37][38][43][44][45][46]. Under the MC criterion, several effective filter design methods were also developed for the non-Gaussian systems.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

Feng

Zhou

et al. 2020

IEEE Access

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This paper concerns the nonlinear filter designing methods in the information space of the nonlinear systems with non-Gaussian noises. Firstly, the prediction information vector is obtained by the traditional square root cubature information filtering algorithm. Then, under the maximum correntropy criterion, the prediction information vector is corrected with the contribution information vector obtained by the non-Gaussian measurement. The information filtering gain is obtained by utilizing the state information correntropy matrix and the measurement information correntropy matrix, in which, the state prediction is taken as the state value. In order to improve the advantage of the above nonlinear non-Gaussian information filter in filtering accuracy, with the help of fixed-point theory, an iterative computation method is further developed to update the estimation information vector and the state estimate. The effectiveness of the two proposed nonlinear non-Gaussian filtering methods is illustrated in final four simulation examples. INDEX TERMS Maximum correntropy criterion; information correntropy matrix; square root cubature information filter;

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Section: (1) Time Updatingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

Feng

Zhou

et al. 2020

IEEE Access

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“…K k does not cause much computing complexity with the generalized Gaussian density (GGD). The gain K k need O(m 3 + n 3 + nm 2 + mn 2 + mn + m 2 ) operation. The optimal state value can be expressed as follows:…”

Section: B Generalized Maximum Correntropy Kalman Filtermentioning

confidence: 99%

“…The indoor localization technology is one of the key technologies of internet of things (IoT), and it has been widespread concern by the researchers [1]. For example, many civil and military scenarios require accurate positioning information [3], [4], [43], such as search-and-rescue, looking for lost luggage, personal tracking, logistics tracking, and robot navigation, etc.…”

Section: Introductionmentioning

confidence: 99%

Robust Kalman Filter Algorithm Based on Generalized Correntropy for Ultra-Wideband Ranging in Industrial Environment

He²,

Zhang

2019

IEEE Access

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The performance of time-of-arrival (TOA)-based ranging using ultra-wideband is greatly declined in the industrial environment since the metallic obstacles cause severe non-line-of-sight (NLOS) and result in huge ranging measurement errors. A general challenge of TOA-based ranging and localization in the industrial environment is that the Kalman filter (KF)-based ranging optimization algorithm cannot effectively improve the ranging accuracy because the ranging errors follow a non-Gaussian distribution. In this paper, a generalized maximum correntropy Kalman filter (GMCKF) algorithm which can effectively suppress NLOS errors is proposed. GMCKF uses the generalized maximum correntropy criterion (GMCC) instead of the minimum mean square error as the criterion of KF, and obtain a robust gain function. GMCC can effectively measure the similarity between the state value and the measurement value, which directly reflects the abnormality of measurement errors. Therefore, GMCKF achieves smoothing filtering in both NLOS and line-of-sight conditions. We compare GMCKF with other KF-based algorithms and prove its steady-state performance in field testing. The results show that the ranging optimized by GMCKF is with significantly higher accuracy. Finally, the optimized ranging is used as the input of three general localization algorithms. The localization accuracy of all localization algorithms is also improved.

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“…However, currently, there is no single indoor positioning technology that is able to balance cost, accuracy, performance, robustness, complexity, and limitations [2,3,4]. The general indoor positioning technologies include infrared positioning [3,5], ultrasound positioning [3,6], radio frequency positioning [4], magnetic positioning [7], microelectromechanical systems positioning [8,9], vision-based positioning [10] and audible sound positioning [11,12]. In particular, radio positioning technologies, such as radio frequency identification (RFID), wireless LAN (WLAN), ZigBee, Bluetooth low energy (BLE) and ultrawideband (UWB), have drawn much attention because of the issuance of many wireless radio standards [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Robust Indoor Positioning Method based on Bluetooth Low Energy with Separate Channel Information

Huang

Liu

Sun

et al. 2019

Sensors

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Among the current indoor positioning technologies, Bluetooth low energy (BLE) has gained increasing attention. In particular, the traditional distance estimation derived from aggregate RSS and signal-attenuation models is generally unstable because of the complicated interference in indoor environments. To improve the adaptability and robustness of the BLE positioning system, we propose making full use of the three separate channels of BLE instead of their combination, which has generally been used before. In the first step, three signal-attenuation models are separately established for each BLE advertising channel in the offline phase, and a more stable distance in the online phase can be acquired by assembling measurements from all three channels with the distance decision strategy. Subsequently, a weighted trilateration method with uncertainties related to the distances derived in the first step is proposed to determine the user’s optimal position. The test results demonstrate that our proposed algorithm for determining the distance error achieves a value of less than 2.2 m at 90%, while for the positioning error, it achieves a value of less than 2.4 m at 90%. Compared with the traditional methods, the positioning error of our method is reduced by 33% to 38% for different smartphones and scenarios.

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An ultrasonic positioning algorithm based on maximum correntropy criterion extended Kalman filter weighted centroid

Cited by 23 publications

References 18 publications

Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

Robust Kalman Filter Algorithm Based on Generalized Correntropy for Ultra-Wideband Ranging in Industrial Environment

A Robust Indoor Positioning Method based on Bluetooth Low Energy with Separate Channel Information

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