萤火虫算法智能优化粒子滤波

针对粒子滤波(Particle filter, PF)重采样导致的粒子贫化以及需要大量粒子才能进行状态估计的问题,本文结合粒子滤波的运行机制,对萤火虫算法的寻优方式进行修正,设计了新的萤火虫位置更新公式和荧光亮度计算公式,并在此基础上提出了萤火虫算法智能优化粒子滤波.该方法引入了萤火虫群体的优胜劣汰机制以及萤火虫个体的吸引和移动的行为,使粒子群智能地向高似然区域移动,提高了粒子群的整体质量.实验表明该方法提高了粒子滤波的预测精度,同时大大降低了状态值预测所需的粒子数量.     

基于粒子筛选处理的粒子滤波改进算法

针对粒子滤波中的粒子贫化问题,分析了目前用于增加粒子多样性方法存在的不足,提出了一种新的粒子筛选与处理方法.通过设置筛选区间,保留该区间内的粒子,对区间外的粒子进行移动处理,从而改善粒子分布.仿真结果表明,该方法能够有效缓解粒子贫化问题,提高滤波精度.同时由于有效样本数增加,降低了重采样次数,总体上减少了算法运行时间.     

铜渣贫化的选择性还原过程

借助于金相分析、SEM和EDX等方法,研究炼铜炉渣加炭粉、通惰性气体搅拌选择性还原贫化过程。结果表明,通过选择性贫化过程,渣中残余铜含量可由5％降低到0．35％以下。随气体搅动时间延长,渣中Fe3O4含量降低,二价铁含量增加,有效地降低了渣黏度．促进冰铜滴沉降。在还原过程中,渣中Fe3O4含量逐渐降低,渣中主要存在的相组成为残余磁性氧化铁、(FeO)2·SiO2及FeO·CaO·SiO2的连续固溶体。每1kg铜渣配人17g炭粉既可满足还原反应要求。     

Substantiation of Deep Placer Mining Technology with Pit-Edge Profile Control

Mining technology is presented, which makes it possible to obtain stable profile of the pit edge, diminish its working area by 15-25%, and reduce impoverishment of the seam by 15-20%.     

A Modified Particle Filter for Simultaneous Localization and Mapping

The implementation of a particle filter (PF) for vision-based bearing-only simultaneous localization and mapping (SLAM) of a mobile robot in an unstructured indoor environment is presented in this paper. Variations, using techniques from the genetic algorithm (GA), to standard PF procedures are proposed to alleviate the sample impoverishment problem. A monochrome CCD camera mounted on the robot is used as the measuring device and a measure on the image quality is incorporated into data association and PF update. Since the bearing-only measurement does not contain range information, we add a pseudo range to the measurement during landmark initialization as a hypothesised pair and the non-promising landmark is removed by a map management strategy. Simulation and experimental results from an implementation using real-life data acquired from a Pioneer robot are included to demonstrate the effectiveness of our approach.     

加强矿山“三率”管理之我见

矿山生产的开采回收率、采矿贫化率、选矿回收率，是矿产资源开发利用和矿山企业管理的综合质量指标，文中简要介绍这“三率”的定义和计算方法，及加强“三率”指标管理的一点经验。     

基于粒子群优化的同步定位与地图构建

针对基本FastSLAM算法的样本枯竭、估计精度下降等问题,提出了一种基于多样性启发因子的粒子群优化FastSLAM算法.利用粒子群搜索寻优重新分配粒子,使粒子的表示更加接近于真实的后验概率分布,并且采用粒子集多样性测度作为启发因子,引导粒子优化搜索过程,确保群体多样性水平最优,减轻粒子退化现象,驱动粒子集向后验概率较高的区域运动.对所提出的算法进行了仿真实验,验证了算法的可行性和有效性.仿真结果表明,该算法能够改进样本枯竭问题,并能够获得较高的定位精度、地图构建精度及较好的滤波估计稳定性.     

微分进化粒子滤波

针对传统粒子滤波重采样算法带来的样本贫化问题,提出了一种利用微分进化算法进行重采样的粒子滤波改进方法,新方法通过引入交叉变异操作,保持了粒子的多样性并抑制了粒子退化现象,提高了目标状态的估计与跟踪能力。仿真结果表明,相对于普通粒子滤波,新算法的估计精度提高了一倍,使用较少的粒子数即可完成跟踪任务。     

An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

Particle filter algorithm is widely used for target tracking using video sequences, which is of great importance for intelligent surveillance applications. However, there is still much room for improvement, e.g. the so-called “sample impoverishment”. It is brought by re-sampling which aims to avoid particle degradation, and thus becomes the inherent shortcoming of the particle filter. In order to solve the problem of sample impoverishment, increase the number of meaningful particles and ensure the diversity of the particle set, an evolutionary particle filter with the immune genetic algorithm (IGA) for target tracking is proposed by adding IGA in front of the re-sampling process to increase particle diversity. Particles are regarded as the antibodies of the immune system, and the state of target being tracked is regarded as the external invading antigen. With the crossover and mutation process, the immune system produces a large number of new antibodies (particles), and thus the new particles can better approximate the true state by exploiting new areas. Regulatory mechanisms of antibodies, such as promotion and suppression, ensure the diversity of the particle set. In the proposed algorithm, the particle set optimized by IGA can better express the true state of the target, and the number of meaningful particles can be increased significantly. The effectiveness and robustness of the proposed particle filter are verified by target tracking experiments. Simulation results show that the proposed particle filter is better than the standard one in particle diversity and efficiency. The proposed algorithm can easily be extended to multiple objects tracking problems with occlusions.     

混沌映射采样的粒子滤波器

针对粒子滤波粒子多样性减弱引起的粒子枯竭问题,提出一种新的基于混沌映射采样的粒子滤波改进算法（CMS-PF）．在重要性采样之后,用类似载波的方法将离线生成的混沌序列映射到以较大权重粒子为中心的样本子空间,从而生成一些映射粒子,并结合当前时刻的预测粒子构建候选粒子集,最终依据各粒子自身的权重实现优选．仿真结果表明,该算法能有效提高对非线性系统状态的估计精度．