基于SLFNs插值指纹粒子滤波的共享单车跟踪算法
|
摘要
为了提高共享单车跟踪过程中传统指纹检测方法的性能,利用惯性传感器进行数据测量。粒子滤波(PF)方法是一种广泛应用的传感器融合算法,然而初始化和加权过程在共享单车定位系统中存在不足。提出了一种新的PF方案,产生平稳和稳定的局部化知识。利用单隐含层的前馈网络(SLFNs)用于模拟多个概率PF性能的估计与改进,实现了对相似指纹的区分。利用随机一致性抽样(RANSAC)进行算法初始化,以减少收敛时间。实验结果表明:方案的跟踪误差小于1. 2 m,优于选取的对比方法。
In order to improve the performance of traditional fingerprint detection methods in shared bike tracking,inertial sensors are used to measure data. Particle filtering( PF) method is a widely used sensor fusion algorithm. However,initialization and weighting precess are problematic in shared bicycle positioning system. A new PF scheme is proposed to generate steady and stable localization knowledge. A single hidden layer feedforward networks( SLFNs) is used to simulate estimation and improvement of multiple probability PF performance,and to realize the discrimination of similar fingerprints. At the same time,the algorithm is initialized by random consistency sampling( RANSAC) to reduce convergence time. Experimental results show that the tracking error of the proposed scheme is less than 1. 2 m,which is prior to the selected contrast method.
In order to improve the performance of traditional fingerprint detection methods in shared bike tracking,inertial sensors are used to measure data. Particle filtering( PF) method is a widely used sensor fusion algorithm. However,initialization and weighting precess are problematic in shared bicycle positioning system. A new PF scheme is proposed to generate steady and stable localization knowledge. A single hidden layer feedforward networks( SLFNs) is used to simulate estimation and improvement of multiple probability PF performance,and to realize the discrimination of similar fingerprints. At the same time,the algorithm is initialized by random consistency sampling( RANSAC) to reduce convergence time. Experimental results show that the tracking error of the proposed scheme is less than 1. 2 m,which is prior to the selected contrast method.
引文
[1]周帆,江维,李树全,等.基于粒子滤波的移动物体定位和追踪算法[J].软件学报,2013,24(9):2196-2213.
[2]胡振涛,刘先省.基于测量方差时变的改进强跟踪滤波算法[J].传感器与微系统,2005,24(6):65-68.
[3] Yiˇgitler Hüseyin,Jntti Riku,Patwari Neal. On log-normality of RSSI in narrowband receivers under static conditions[J]. IEEE Signal Processing Letters,2017,24(4):367-371.
[4] Xue W X,Qiu W N,Hua X H,et al. Improved Wi Fi RSSI measurement for indoor localization[J]. IEEE Sensors Journal,2017,17(7):2224-2230.
[5] Fang X M,Nan L,Jiang Z H,et al. Fingerprint localisation algorithm for noisy wireless sensor network based on multi-objective evolutionary model[J]. IET Communications,2017,11(8):1297-1304.
[6] Aldoumani Noor,Meydan Turgut,Dillingham Christopher M,et al.Enhanced tracking system based on micro inertial measurements unit to measure sensorimotor responses in pigeons[J]. IEEE Sensors Journal,2016,16(24):8847-8853.
[7] Zhong X,Premkumar A B,Madhukumar A S. Particle filtering for acoustic source tracking in impulsive noise with alpha-stable process[J]. Nor Jornal,2013,13(6):589-600.
[8] Zorzi Mattia. Robust Kalman filtering under model perturbations[J].IEEE Transactions on Automatic Control,2017,62(6):2902-2907.
[9]王法胜,鲁明羽,赵清杰,等.粒子滤波算法[J].计算机学报,2014,37(8):1679-1694.
[10]张翔,郭杭,武和雷.基于虚拟信息库的粒子滤波指纹库定位算法[J].计算机应用与软件,2015(6):224-227.
[11]龙慧,樊晓平,刘少强,等.无线传感器网络动态最近邻协作目标跟踪算法[J].传感器与微系统,2012,31(7):135-139.
[2]胡振涛,刘先省.基于测量方差时变的改进强跟踪滤波算法[J].传感器与微系统,2005,24(6):65-68.
[3] Yiˇgitler Hüseyin,Jntti Riku,Patwari Neal. On log-normality of RSSI in narrowband receivers under static conditions[J]. IEEE Signal Processing Letters,2017,24(4):367-371.
[4] Xue W X,Qiu W N,Hua X H,et al. Improved Wi Fi RSSI measurement for indoor localization[J]. IEEE Sensors Journal,2017,17(7):2224-2230.
[5] Fang X M,Nan L,Jiang Z H,et al. Fingerprint localisation algorithm for noisy wireless sensor network based on multi-objective evolutionary model[J]. IET Communications,2017,11(8):1297-1304.
[6] Aldoumani Noor,Meydan Turgut,Dillingham Christopher M,et al.Enhanced tracking system based on micro inertial measurements unit to measure sensorimotor responses in pigeons[J]. IEEE Sensors Journal,2016,16(24):8847-8853.
[7] Zhong X,Premkumar A B,Madhukumar A S. Particle filtering for acoustic source tracking in impulsive noise with alpha-stable process[J]. Nor Jornal,2013,13(6):589-600.
[8] Zorzi Mattia. Robust Kalman filtering under model perturbations[J].IEEE Transactions on Automatic Control,2017,62(6):2902-2907.
[9]王法胜,鲁明羽,赵清杰,等.粒子滤波算法[J].计算机学报,2014,37(8):1679-1694.
[10]张翔,郭杭,武和雷.基于虚拟信息库的粒子滤波指纹库定位算法[J].计算机应用与软件,2015(6):224-227.
[11]龙慧,樊晓平,刘少强,等.无线传感器网络动态最近邻协作目标跟踪算法[J].传感器与微系统,2012,31(7):135-139.