An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

A Distributed Particle Filter Applied in Single Object Tracking

Visual object-tracking is a fundamental task applied in many applications of computer vision. Particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability and flexibility on both linear and non-linear environments, various particle filter-based trackers have been proposed in the literature. However, the conventional approach cannot handle very large videos efficiently in the current data intensive information age. In this work, a parallelized particle filter is provided in a distributed framework provided by the Hadoop/Map-Reduce infrastructure to tackle object-tracking tasks. The experiments indicate that the proposed algorithm has a better convergence and accuracy as compared to the traditional particle filter. The computational power and the scalability of the proposed particle filter in single object tracking have been enhanced as well.

基于平方根UPF的电力系统鲁棒预测状态估计

针对辅助预测状态估计器在迭代计算中会出现状态预测误差协方差矩阵不正定,导致估计精度差甚至发散的问题,提出了基于平方根UPF的电力系统鲁棒辅助预测状态估计。该方法采用两种数学方法:矩阵Cholesky分解因子更新和矩阵QR分解,引入平方根技术动态更新状态预测误差协方差矩阵以保持状态预测误差协方差矩阵的正定性。运用MATLAB进行仿真模拟测试,结果表明:IEEE 30节点系统非高斯噪声测试中,平方根UPF电压相角的均方根误差平均值为UPF相应测试值的0.09%,平方根UPF电压幅值的均方根误差平均值为UPF相应测试值的0.14%;IEEE 57节点系统非高斯噪声测试中,平方根UPF电压相角的均方根误差平均值为UPF相应测试值的0.67%,平方根UPF电压幅值的均方根误差平均值为UPF相应测试值的0.57%。所提出的平方根UPF对解决辅助预测状态估计中状态预测误差协方差矩阵不正定的问题具有很好的效果,具有更高估计精度和鲁棒性。

基于AGPF的目标定位精度改善方法

针对传统遗传算法粒子滤波容易因遗传操作参数恒定不变而陷入局部最优的问题,在遗传算法粒子滤波中引入自适应方法,提出自适应遗传算法粒子滤波。根据粒子适应度的大小,动态调节遗传操作的交叉、突变概率,从而在尽可能多地保留优势粒子的同时更有效地产生新的优势粒子,跳出局部最优。将自适应遗传算法粒子滤波应用于动态目标定位模型,并将其与遗传算法粒子滤波的性能进行比较。结果表明,自适应方法的引入可以增加算法有效粒子数,有效解决算法早熟问题,改善滤波精度,对于提高动态目标定位精度是有效的。

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor Positioning

In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

平坦地形条件下改进RPF的TAN方法

针对地形辅助导航系统中递归地形匹配方法在平坦地形条件下位置估计鲁棒性差的问题,提出了一种基于集合卡尔曼滤波和正则化粒子滤波(RPF)的地形匹配方法。首先分别以航行器的水平位置分量和多波束声纳的高程测量值作为地形匹配系统的状态量和观测量,然后采用基于投影的方案补偿航行器姿态变化导致的测深误差,最后利用集合卡尔曼滤波器更新RPF中的条件建议分布以实现递归地形匹配。通过船载湖试数据评估了改进RPF在不同初始匹配位置误差条件下的地形匹配跟踪性能,结果表明:所提地形匹配滤波器能始终保持有界的定位误差,位置跟踪精度和置信区间估计性能较高,在10 m分辨率的先验数字地形图中地形匹配误差均值小于2个网格。

基于LSTM-UPF混合驱动方法的燃料电池寿命预测

燃料电池的寿命预测是燃料电池健康管理的重要组成部分,可为燃料电池的运行和维护提供指导性意见。为提高寿命预测的工况适应性并保证预测精度,本工作结合长短期记忆神经网络(long short-term memory neural network,LSTM)和无迹粒子滤波(unscented particle filter,UPF)两种算法的优势,提出了一种LSTMUPF混合驱动方法进行稳态和准动态工况下燃料电池的寿命预测。该方法首先优化训练预测模型的实验数据并采用离散小波变换(discrete wavelet transform,DWT)技术将其分解为高频部分和低频部分,使用LSTM算法对这两部分分别进行预测实现对燃料电池长期老化趋势的预测,并使用修正因子对趋势预测结果进行漂移修正,然后利用得到的燃料电池长期老化趋势,根据UPF算法对燃料电池的剩余使用寿命(remaining useful life,RUL)进行估计。采用预测寿命终点、预测寿命误差、置信区间宽度、RUL预测误差等评价指标对不同寿命预测方法进行对比分析,结果表明,LSTM-UPF混合预测方法对燃料电池稳态工况和准动态工况的RUL预测误差分别为4.1%和3.4%,比基于模型的PF和UPF方法具有更精确的RUL预测结果与高质量的预测置信区间,工况适应性良好。本研究有助于提高多工况下的燃料电池寿命预测精度和置信度。

Vehicle recognition and tracking based on simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm

Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value.

An Improved Particle Filter Map Matching Algorithm for Personal Inertial Positioning

The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algorithm for the inertial positioning of personnel. The historical moment position constraint and feasible region constraint of particles were introduced in this paper. A resampling method based on multi-stage backtracking of particles was proposed. Therefore, the effectiveness of newly generated particles could be guaranteed. The utilization rate of map information could be improved, thus enhancing the accuracy of personnel localization. The walking experiment results showed that, compared with the traditional PDR algorithm, the proposed method had higher localization accuracy and better repeatability of the localization trajectory for multi-turn paths. Under the total travel of 480 meters, the deviation of the starting end point was less than 2 meters, which was about 0.4% of the total travel.

EPF和PF在水下三维纯方位目标跟踪中的应用

针对水下目标跟踪问题,以静止双观测站三维纯方位跟踪系统为研究对象,介绍粒子滤波(Particle Filter,PF)和扩展卡尔曼粒子滤波(Extended Kalman Filter,EPF)的基本思想和算法实现步骤,根据建立的目标运动模型,在目标运动速度不同、粒子数目不同的情况下,将EPF、PF在双观测站三维纯方位目标跟踪系统中进行仿真分析,并结合UKF、EKF算法进行对比,结果表明,EPF算法相较于其他算法有更好的跟踪效果,并且不需要选取过多的粒子数目就可以达到较好的跟踪效果,但跟踪时间长、实时性较差。

TE-PF及其在轴承寿命预测中的应用

针对构建科学的预测模型以及估计合适的模型参数是极大限制粒子滤波(particle filter,简称PF)方法计算效率与稳定性的瓶颈问题,提出了一种基于轨迹强化粒子滤波(trajectory enhanced particle filter,简称TE-PF)的滚动轴承剩余使用寿命(remaining useful life,简称RUL)预测方法。从退化速率跟踪和退化轨迹强化的角度出发,构建了一种面向PF方法的通用预测模型,利用历史样本以及粒子生成样本的退化趋势信息,有效指导通用预测模型的参数估计,最终获取多信息融合的轨迹增强预测模型。实验结果表明,相较于已有方法,TE-PF方法具有更高的计算效率与更强的趋势预测稳定性,观测样本累积情形下能够获取置信区间内较高的预测精度。

基于Chan-PF的TDOA井下人员定位算法研究

煤矿井下的复杂情况,导致在NLOS环境下,人员定位精度大幅下降,甚至得到的数据无法使用。针对此问题,文章提出一种Chan-PF融合定位算法。首先,使用Chan算法对到达时间差(TDOA)得到的测量值进行处理,获得最优解;其次,采用粒子滤波对最优解再次进行处理,最终粒子重采样后的中心位置即为目标节点的精确位置。仿真结果表明,该算法能有效地抵抗煤矿井下NLOS环境中的干扰,提高定位精度,得到较为精确的定位坐标。

高效低背压CDPF催化剂技术研究

背压和PN过滤效率是DPF载体和CDPF催化剂的关键性能指标,结合空白载体孔径和涂层负载两个方面开展研究,结果显示:孔径是影响DPF/CDPF背压和PN排放的关键,孔径越小,背压越大,PN过滤效率也越高。调低DPF载体孔径可提高PN过滤效率,但背压增率难以控制;通过涂层涂覆可有效调控孔径分布,实现向小孔径方向偏移,在提高PN过滤效率的同时可有效控制背压增率。基于此,协同载体技术和涂层涂覆技术,优选出中值孔径为11.5μm的DPF载体,涂覆粒径D 90为3.5μm、负载量为15 g/L的涂层,涂覆后背压增率为8.88%,PN排放为9.34×10^(10)个/(kW·h),在满足国六排放法规的前提下,可同时兼顾高效的PN过滤效率和较低背压增率。

基于多策略人工蜂鸟优化PF的SLAM研究

针对粒子滤波算法(PF)重采样导致粒子贫乏及需增加粒子数以提高估计精度的问题,提出一种基于多策略人工蜂鸟算法优化的粒子重组粒子滤波算法。首先,引入中垂线算法提高人工蜂鸟算法收敛速度,通过其智能觅食机制,使得最优粒子引导粒子集向高似然区域移动,以此提高估计精度;其次,实时计算最优粒子附近的粒子密度,当密度大于设置的区域搜索阈值时引入Levy飞行策略以扩大搜索空间,当其大于最大密度值时,自适应调整迭代次数;最后,重采样阶段将筛选后保留的粒子与剩余粒子重新组合成新的粒子,以此增加粒子多样性。通过仿真实验检验改进算法在SLAM中的性能,结果表明该算法较其他3种算法相比,其位姿与路标估计精度更高且鲁棒性更佳。

Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security.Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

基于LSTM-CAPF框架的岸桥起升减速箱轴承寿命预测方法

岸桥起升减速箱轴承的健康状况对港口生产安全具有重要意义.针对岸桥变工况的工作条件,提出一种起升减速箱轴承的剩余使用寿命(RUL)预测框架.首先,对工作载荷进行离散化,并确定工况边界.然后,利用长短时记忆(LSTM)网络模型预测载荷和相应的运行工况.其次,以维纳过程为基础,建立了考虑不同工况下退化率和跳变系数的状态退化函数.最后,利用工况激活粒子滤波(CAPF)方法预测轴承退化状态和RUL.采用NetCMAS系统采集的上海某港口起升减速箱轴承全寿命数据验证了所提出的预测框架.与其他3种预测模式比较表明,所提出的框架能够在变工况条件下获得更准确的退化状态和RUL预测.

基于改进UPF的低空小型飞行器跟踪方法

在低空小型飞行器的单站纯角度目标定位问题中,受限于气象条件、操作水平等因素,对观测目标的测量精度有限。为提高目标跟踪性能、降低硬件要求,该文提出一种适用于三维目标的纯方位目标跟踪算法。针对UPF算法实时性较低的问题,采用超球面分布的SSUT代替UPF中的UT,与UPF算法相比,SSUPF在保证定位精度的同时用时更少。为提高对三维目标跟踪精度,该文采用一种三阶段滤波算法,分别对目标方位角、俯仰角及距离进行滤波。实验表明该方法可降低方位角与俯仰角之间的耦合误差,提高对目标状态的估计精度,同时可大大降低目标跟踪所需的计算时间。

基于Camshift和Particle Filter的小目标跟踪算法

Particle Filter算法有较好的跟踪鲁棒性,但实时性差;Camshift算法计算速度快,但它属于半自动跟踪,所以都无法有效避免复杂背景的干扰。为了解决上述问题,提出了基于Camshift和Particle Filter的融合算法。该算法首先利用Particle Filter来自动搜索小目标的初始位置,接着采用Camshift跟踪小目标,然后通过度量因子自适应切换Camshift和Particle Filter来跟踪短时丢失的目标。利用复杂背景下的飞行小目标图像序列,与序贯相似性检测算法(SSDA)、Camshift和Particle Filter做对比实验。结果表明算法不仅能实现小目标的全自动跟踪,而且还降低了跟踪效果受目标形变和部分遮挡的影响,对小目标跟踪具有较高的鲁棒性和实时性。

Face tracking algorithm based on particle filter with mean shift importance sampling

The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation. In order to overcome this shortcoming, a new face tracking algorithm based on particle filter with mean shift importance sampling is proposed. First, the coarse location of the face target is attained by the efficient mean shift tracker, and then the result is used to construct the proposal distribution for particle propagation. Because the particles obtained with this method can cluster around the true state region, particle efficiency is improved greatly. The experimental results show that the performance of the proposed algorithm is better than that of the standard condensation tracking algorithm.