The particle filter（PF） algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle...The particle filter（PF） algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit（GPU） parallel computing the platform, the “channel-level” and “particlelevel” parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.展开更多
采用离散元素法(discrete element method, DEM)进行颗粒系统运动仿真时,其模拟计算量大、计算效率低下,所采用的传统中央处理器(central processing unit, CPU)并行计算模型难以实现较大规模模拟。文章提出了一种基于图形处理单元(grap...采用离散元素法(discrete element method, DEM)进行颗粒系统运动仿真时,其模拟计算量大、计算效率低下,所采用的传统中央处理器(central processing unit, CPU)并行计算模型难以实现较大规模模拟。文章提出了一种基于图形处理单元(graphics processing unit, GPU)和统一计算设备架构(compute unified device architecture, CUDA)的并行计算方法;以球磨机的介质运动仿真为例,利用DEM方法结合CUDA并行计算模型,充分利用GPU众核多线程的计算优势,同时将颗粒属性信息存入GPU的常量存储器,减少信息读取的时滞,将筒体和衬板视为圆柱面和平面,简化了筒体与颗粒的接触判断,实现每个线程处理1个颗粒的相关计算,大幅提高计算速度;对颗粒堆积、筒体内2种尺寸颗粒运动进行仿真,并与基于CPU并行计算的结果进行对比。研究结果表明:在同等价格的硬件条件下,该文的方法可以实现10倍以上的加速比;对于含有复杂几何模型的仿真,如多尺寸颗粒和带衬板筒体的仿真,加速比会减少,但仍然可以实现数倍的加速。展开更多
基金supported by the National High-tech R&D Program of China(2015AA70560452015AA8017032P)the National Natural Science Foundation of China(61401504)
文摘The particle filter（PF） algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit（GPU） parallel computing the platform, the “channel-level” and “particlelevel” parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.
文摘采用离散元素法(discrete element method, DEM)进行颗粒系统运动仿真时,其模拟计算量大、计算效率低下,所采用的传统中央处理器(central processing unit, CPU)并行计算模型难以实现较大规模模拟。文章提出了一种基于图形处理单元(graphics processing unit, GPU)和统一计算设备架构(compute unified device architecture, CUDA)的并行计算方法;以球磨机的介质运动仿真为例,利用DEM方法结合CUDA并行计算模型,充分利用GPU众核多线程的计算优势,同时将颗粒属性信息存入GPU的常量存储器,减少信息读取的时滞,将筒体和衬板视为圆柱面和平面,简化了筒体与颗粒的接触判断,实现每个线程处理1个颗粒的相关计算,大幅提高计算速度;对颗粒堆积、筒体内2种尺寸颗粒运动进行仿真,并与基于CPU并行计算的结果进行对比。研究结果表明:在同等价格的硬件条件下,该文的方法可以实现10倍以上的加速比;对于含有复杂几何模型的仿真,如多尺寸颗粒和带衬板筒体的仿真,加速比会减少,但仍然可以实现数倍的加速。
