This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this pa...This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image.The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics,and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation,and non-uniform mutation operator is used to capture all kinds of mutation in this paper.The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering（EKF）,PF,regularized partide filtering（RPF）,and genetic algorithm（GA）-PF.展开更多
Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unsc...Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unscented KF(UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF(BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature(being stuck into local optimum) and increase the diversity of particles, Lévy flight is designed to increase the randomness of particle's movement. Finally,the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.展开更多
针对纯方位目标跟踪(Bearing-Only Tracking,BOT)系统强非线性特点,提出一种新的解决方案:采用平方根中心差分卡尔曼滤波器(Square-RootCDKF,SRCDKF)产生粒子滤波提议分布,融入最新的观测数据影响;增加改进措施以提高滤波性能,如采用系...针对纯方位目标跟踪(Bearing-Only Tracking,BOT)系统强非线性特点,提出一种新的解决方案:采用平方根中心差分卡尔曼滤波器(Square-RootCDKF,SRCDKF)产生粒子滤波提议分布,融入最新的观测数据影响;增加改进措施以提高滤波性能,如采用系统重抽样算法减少方差、应用马尔可夫链模特卡罗(Markovchain Monte Carlo,MCMC)方法消除粒子贫乏等。仿真表明该算法是有效的,针对当前BOT系统,比传统EKF、PF算法可靠性更好,跟踪精度更高。展开更多
针对农业温室复杂环境中的超宽带(Ultra wide band,UWB)定位精度受非视距(Non line of sight,NLOS)效应和多路径影响的问题,本文提出了一种融合Chan-Taylor与改进沙猫群优化粒子滤波(Chan-Taylor and improved sand cat swarm intellige...针对农业温室复杂环境中的超宽带(Ultra wide band,UWB)定位精度受非视距(Non line of sight,NLOS)效应和多路径影响的问题,本文提出了一种融合Chan-Taylor与改进沙猫群优化粒子滤波(Chan-Taylor and improved sand cat swarm intelligence optimization particle filter,CT+ISCSO-PF)定位算法。首先,利用Chan-Taylor算法实现对目标初始位置的快速估算,为粒子滤波提供准确初值;随后,引入ISCSO(Improved sand cat swarm optimization particle filter)引导粒子向高似然区域移动,通过三角游走策略提升全局搜索能力,结合Levy飞行机制增强局部收敛效率,从而有效抑制粒子退化问题。本文模拟了3种不同噪声水平的环境。仿真结果表明,CT+ISCSO-PF算法在3种环境下,相比于传统的粒子滤波(Particle filter,PF)、Chan-Taylor与粒子滤波(Chan-Taylor and particle filter,CT+PF)、Chan-Taylor与沙猫群优化粒子滤波(Chan-Taylor and sand cat swarm intelligence optimization particle filter,CT+SCSO-PF)、Chan-Taylor与灰狼优化粒子滤波(Chan-Taylor and grey wolf optimizer particle filter,CT+GWO-PF)均表现出明显优势。进一步以农用履带车辆为载体开展温室环境定位试验,结果显示:在LOS场景下,该算法较PF、CT+PF、CT+SCSO-PF和CT+GWO-PF的均方根误差分别降低27.9%、17.8%、7.8%和10.2%;在NLOS场景下,均方根误差降幅分别达21.4%、15.6%、7.6%和5.2%。展开更多
基金supported by the National Natural Science Foundation of China(61302145)
文摘This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image.The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics,and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation,and non-uniform mutation operator is used to capture all kinds of mutation in this paper.The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering（EKF）,PF,regularized partide filtering（RPF）,and genetic algorithm（GA）-PF.
文摘Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unscented KF(UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF(BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature(being stuck into local optimum) and increase the diversity of particles, Lévy flight is designed to increase the randomness of particle's movement. Finally,the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.
文摘针对纯方位目标跟踪(Bearing-Only Tracking,BOT)系统强非线性特点,提出一种新的解决方案:采用平方根中心差分卡尔曼滤波器(Square-RootCDKF,SRCDKF)产生粒子滤波提议分布,融入最新的观测数据影响;增加改进措施以提高滤波性能,如采用系统重抽样算法减少方差、应用马尔可夫链模特卡罗(Markovchain Monte Carlo,MCMC)方法消除粒子贫乏等。仿真表明该算法是有效的,针对当前BOT系统,比传统EKF、PF算法可靠性更好,跟踪精度更高。
文摘为提高井下超宽带定位的精度,提出一种基于改进卡尔曼滤波(improved Kalman filter, IKF)与萤火虫优化粒子滤波(FA-PF)的目标跟踪定位算法。首先利用IKF对标签与基站之间的测距值进行降噪处理;然后利用萤火虫算法改进粒子滤波的重采样过程,使算法在迭代过程中保持粒子的多样性,提高滤波的精度;最后使用FA-PF算法估计目标位置。仿真结果表明,相比传统卡尔曼滤波,IKF抑制非视距(non line of sight, NLOS)噪声的能力有所提高;在测量值相同的情况下,FA-PF算法具有更高的定位精度,定位误差相比粒子滤波算法和卡尔曼滤波算法分别降低了43.98%和32.50%。
