Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ...Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.展开更多
考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新...考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。展开更多
针对航空制造业中,当容差分配问题中含有装配成功率等随机约束时,常用的数值算法往往难以处理。为提高产品制造精度,提出了混合蒙特卡洛(Hybrid Monte Carlo,HMC)算法,即把动态蒙特卡洛(Dynamic Monte Carlo,DMC)算法和静态蒙特卡罗(SMC...针对航空制造业中,当容差分配问题中含有装配成功率等随机约束时,常用的数值算法往往难以处理。为提高产品制造精度,提出了混合蒙特卡洛(Hybrid Monte Carlo,HMC)算法,即把动态蒙特卡洛(Dynamic Monte Carlo,DMC)算法和静态蒙特卡罗(SMC)算法结合起来,将DMC用于容差分配的优化仿真运算,把SMC用来处理装配成功率约束。通过仿真验证了该方案的可行性,混合蒙特卡洛法既合理地处理了随机约束,证明装配准确度计算和容差分配的一致性。结果说明求解这类问题是最佳算法。展开更多
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.
文摘考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。
文摘针对航空制造业中,当容差分配问题中含有装配成功率等随机约束时,常用的数值算法往往难以处理。为提高产品制造精度,提出了混合蒙特卡洛(Hybrid Monte Carlo,HMC)算法,即把动态蒙特卡洛(Dynamic Monte Carlo,DMC)算法和静态蒙特卡罗(SMC)算法结合起来,将DMC用于容差分配的优化仿真运算,把SMC用来处理装配成功率约束。通过仿真验证了该方案的可行性,混合蒙特卡洛法既合理地处理了随机约束,证明装配准确度计算和容差分配的一致性。结果说明求解这类问题是最佳算法。
