Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussia...Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussian distribution.In some cases,this assumption no longer holds and the performance of the traditional SLAM algorithms declines.In this paper,we present a robust SLAM algorithm based on variational Bayes method by modelling the observation noise as inverse-Wishart distribution with "harmonic mean".Besides,cubature integration is utilized to solve the problem of nonlinear system.The proposed algorithm can effectively solve the problem of filtering divergence for traditional filtering algorithm when suffering the time-variant observation noise,especially for heavy-tai led noise.To validate the algorithm,we compare it with other t raditional filtering algorithms.The results show the effectiveness of the algorithm.展开更多
针对一般模糊规则模型对含有重尾噪声的数据集鲁棒性较差的问题,提出了面向重尾噪声的模糊规则(Rule-based Fuzzy Model for Heavy-tailed Noisy Data,HtRbF)模型.该模型使用了两种新的聚类方法,学生t分布均值聚类算法(Student’s t-dis...针对一般模糊规则模型对含有重尾噪声的数据集鲁棒性较差的问题,提出了面向重尾噪声的模糊规则(Rule-based Fuzzy Model for Heavy-tailed Noisy Data,HtRbF)模型.该模型使用了两种新的聚类方法,学生t分布均值聚类算法(Student’s t-distribution C-Means,StCM)和学生t分布下的背景模糊聚类方法(Student’s t-distribution Context Fuzzy C-Means,StCFCM),并将其应用在初始规则和新规则的生成中,使模型在重尾噪声场景下生成更为准确的规则,有效减少了模型的输出误差,使其更接近真实输出.HtRbF模型具有良好的抗噪能力,通过对数据集添加不同类型的重尾噪声进行系统性实验,实验结果证明了HtRbF模型的有效性.展开更多
Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking(MTT) system,a new Gaussian-Student’s t mixture distribution proba...Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking(MTT) system,a new Gaussian-Student’s t mixture distribution probability hypothesis density(PHD) robust filtering algorithm based on variational Bayesian inference(GST-vbPHD) is proposed.Firstly,since it can accurately describe the heavy-tailed characteristics of noise with outliers,Gaussian-Student’s t mixture distribution is employed to model process noise and measurement noise respectively.Then Bernoulli random variable is introduced to correct the likelihood distribution of the mixture probability,leading hierarchical Gaussian distribution constructed by the Gaussian-Student’s t mixture distribution suitable to model non-stationary noise.Finally,the approximate solutions including target weights,measurement noise covariance and state estimation error covariance are obtained according to variational Bayesian inference approach.The simulation results show that,in the heavy-tailed noise environment,the proposed algorithm leads to strong improvements over the traditional PHD filter and the Student’s t distribution PHD filter.展开更多
为实现正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的时域同步,本文利用ZC(Zadoff-Chu)序列提出了类奈曼-皮尔逊检验加权的l_p(p=1或2)相关(Neyman-Pearson-like test Weighted l_p-Correlation,l_p-NPWC)同步...为实现正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的时域同步,本文利用ZC(Zadoff-Chu)序列提出了类奈曼-皮尔逊检验加权的l_p(p=1或2)相关(Neyman-Pearson-like test Weighted l_p-Correlation,l_p-NPWC)同步算法.分析表明:该算法利用类奈曼-皮尔逊检验能有效抑制多径效应对l_p相关同步的影响,特别地,当取p=1时,它对重尾分布噪声及循环前缀导致的伪峰具有极强的鲁棒性.实验及仿真结果均证明了理论分析的正确性和有效性,并表明本文算法相较于现有算法在各种干扰环境中均具有更高的同步精度和性能.展开更多
基金the National Natural Science Foundation of China(No.61803260)。
文摘Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussian distribution.In some cases,this assumption no longer holds and the performance of the traditional SLAM algorithms declines.In this paper,we present a robust SLAM algorithm based on variational Bayes method by modelling the observation noise as inverse-Wishart distribution with "harmonic mean".Besides,cubature integration is utilized to solve the problem of nonlinear system.The proposed algorithm can effectively solve the problem of filtering divergence for traditional filtering algorithm when suffering the time-variant observation noise,especially for heavy-tai led noise.To validate the algorithm,we compare it with other t raditional filtering algorithms.The results show the effectiveness of the algorithm.
基金Supported by the National Natural Science Foundation of China(No.61976080)the Science and Technology Key Project of Science and Technology Department of Henan Province(No.212102310298)the Innovation and Quality Improvement Project for Graduate Education of Henan University(No.SYL20010101)。
文摘Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking(MTT) system,a new Gaussian-Student’s t mixture distribution probability hypothesis density(PHD) robust filtering algorithm based on variational Bayesian inference(GST-vbPHD) is proposed.Firstly,since it can accurately describe the heavy-tailed characteristics of noise with outliers,Gaussian-Student’s t mixture distribution is employed to model process noise and measurement noise respectively.Then Bernoulli random variable is introduced to correct the likelihood distribution of the mixture probability,leading hierarchical Gaussian distribution constructed by the Gaussian-Student’s t mixture distribution suitable to model non-stationary noise.Finally,the approximate solutions including target weights,measurement noise covariance and state estimation error covariance are obtained according to variational Bayesian inference approach.The simulation results show that,in the heavy-tailed noise environment,the proposed algorithm leads to strong improvements over the traditional PHD filter and the Student’s t distribution PHD filter.
文摘为实现正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的时域同步,本文利用ZC(Zadoff-Chu)序列提出了类奈曼-皮尔逊检验加权的l_p(p=1或2)相关(Neyman-Pearson-like test Weighted l_p-Correlation,l_p-NPWC)同步算法.分析表明:该算法利用类奈曼-皮尔逊检验能有效抑制多径效应对l_p相关同步的影响,特别地,当取p=1时,它对重尾分布噪声及循环前缀导致的伪峰具有极强的鲁棒性.实验及仿真结果均证明了理论分析的正确性和有效性,并表明本文算法相较于现有算法在各种干扰环境中均具有更高的同步精度和性能.
