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RISS 인기검색어
- 검색키워드
- 저자 : Fuliang Yin (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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Peak-to-Average Power Ratio Reduction Using N-tuple Selective Mapping Method for MC-CDMA
Sajjad Ali,Zhe Chen,Fuliang Yin 한국전자통신연구원 2015 ETRI Journal Vol.37 No.2
The multi-carrier transmission signal in Multi-Carrier Code Division Multiple Access (MC-CDMA) has a high peak-to-average power ratio (PAPR), which results in nonlinear distortion and deteriorative system performance. An n-tuple selective mapping method is proposed to reduce the PAPR, in this paper. This method generates 2n sequences of an original data sequence by adding n-tuple of n PAPR control bits to it followed by an interleaver and error-control code (ECC) to reduce its PAPR. The convolutional, Golay, and Hamming codes are used as ECCs in the proposed scheme. The proposed method uses different numbers of the n PAPR control bits to accomplish a noteworthy PAPR reduction and also avoids the need for a side-information transmission. The simulation results authenticate the effectiveness of the proposed method.
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An Eye Location based Head Posture Recognition Method and Its Application in Mouse Operation
( Zhe Chen ),( Bingbing Yang ),( Fuliang Yin ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.3
An eye location based head posture recognition method is proposed in this paper. First, face is detected using skin color method, and eyebrow and eye areas are located based on gray gradient in face. Next, pupil circles are determined using edge detection circle method. Finally, head postures are recognized based on eye location information. The proposed method has high recognition precision and is robust for facial expressions and different head postures, and can be used in mouse operation. The experimental results reveal the validity of proposed method.
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Robot Tracking in SLAM with Masreliez-Martin Unscented Kalman Filter
Ming Tang,Zhe Chen,Fuliang Yin 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.9
The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, a robot tracking algorithm in SLAM with Masreliez-Martin unscented Kalman filter (MMUKF) is proposed. A robot dynamic model based on SLAM characteristics is first used as state equation to model the robotic movement, and the measurement equations are deduced by linearizing the motion model. Next, the covariance of process noise is estimated with an adaptive factor to improve tracking performance in the MMUKF. Finally, the MMUKF is employed to estimate the positions of robot and landmarks. The proposed algorithm can complete robot tracking with good accuracy, and obtain reliable state estimation in SLAM. Simulation results reveal the validity of the proposed Algorithm.
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An Improved Adaptive Unscented FastSLAM with Genetic Resampling
Ming Tang,Zhe Chen,Fuliang Yin 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.4
The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, an improved adaptive unscented FastSLAM with genetic resampling is proposed. Specifically, the adaptive unscented Kalman filter (IAUKF) algorithm is improved as importance sampling of particle filter, where the adaptive factor is used to improve the tracking ability of system and the Huber cost function is constructed to decrease the measurement covariance. Next, the process noise and the measurement noise are assessed by a time varying estimator. Moreover, the resampling in particle filter is carried out by an improved genetic algorithm (GA). Finally, the improved adaptive unscented FastSLAM (IAUFastSLAM) is proposed to complete robot tracking. The proposed algorithm has good tracking performance and obtains reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.
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SLAM with Improved Schmidt Orthogonal Unscented Kalman Filter
Ming Tang,Zhe Chen,Fuliang Yin 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.4
Simultaneous localization and mapping (SLAM) is a momentous topic for robot navigation to explore uncharted environment. To enhance the accuracy and efficiency, an improved Schmidt orthogonal unscented Kalman filter (ISOUKF) based SLAM algorithm is proposed in this paper. First, based on the Schmidt orthogonal transform (SOT) sampling, a modified unscented Kalman filter (UKF) algorithm is presented. Then, an adaptive fading factor is derived using the strong tracking algorithm, and it is introduced into the prediction covariance to improve tracking ability and accuracy. Next, the Schmidt orthogonal unscented Kalman filter is improved with square root filter to raise the efficiency of SLAM algorithm. Finally, the ISOUKF algorithm is proposed to complete the robot tracking in SLAM. The proposed algorithm provides a high precision robot tracking for SLAM and decreases the computational cost to some extent. Experiment results verify the superiority of the proposed algorithm.
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An Improved UFastSLAM With Generalized Correntropy Loss and Adaptive Genetic Resampling
Ming Tang,Zhe Chen,Fuliang Yin 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.3
The simultaneous localization and mapping (SLAM) is a research hotspot in robot navigation. In thispaper, an improved UFastSLAM with generalized correntropy loss and adaptive genetic resampling is proposed. Specifically, the unscented Kalman filter algorithm with generalized correntropy loss is improved as the importancesampling in particle filter. Then, an adaptive genetic algorithm is employed to complete the resampling of particlefilter. Finally, the improved UFastSLAM with generalized correntropy loss is presented to complete robot tracking. The proposed algorithm can complete robot tracking with high accuracy performance, and obtain reliable stateestimation under the non-Gaussian measurement noise in SLAM. Simulation and experiment results exhibit theavailability of the proposed SLAM algorithm.
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