Dead Reckoning技术在网络游戏中的应用

介绍了基于物理运动学算法的预测技术以及基于Cubic Splines算法的平滑处理技术,并给出了Dead Reckoning在网络游戏中应用的具体算法以及该算法的相应实例。

增进平滑移动效果的Dead Reckoning算法

网络游戏中一个最常见的问题就是互联网自身存在的延时问题,当新数据包到来的时候,游戏界面上的角色就会产生抖动现象。解决这个问题的最重要的方法就是DeadReckoning算法。本文对传统的DR算法进行了改进,在其平滑机制中引入了“三次参数”方法,并在3D游戏中引入了方向预测方法。改进后的算法在性能和游戏真实性方面取得了较好的效果。

Recurrent neural network for vehicle dead-reckoning

For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.

Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications

Navigation systems play an important role in many vital disciplines. Determining the location of a user relative to its physical environment is an important part of many indoor-based navigation services such as user navigation, enhanced 911 (E911), law enforcement, location-based and marketing services. Indoor navigation applications require a reliable, trustful and continuous navigation solution that overcomes the challenge of Global Navigation Satellite System (GNSS) signal unavailability. To compensate for this issue, other navigation systems such as Inertial Navigation System (INS) are introduced, however, over time there is a significant amount of drift especially in common with low-cost commercial sensors. In this paper, a map aided navigation solution is developed. This research develops an aiding system that utilizes geospatial data to assist the navigation solution by providing virtual boundaries for the navigation trajectories and limits its possibilities only when it is logical to locate the user on a map. The algorithm develops a Pedestrian Dead Reckoning (PDR) based on smart-phone accelerometer and magnetometer sensors to provide the navigation solution. Geospatial model for two indoor environments with a developed map matching algorithm was used to match and project navigation position estimates on the geospatial map. The developed algorithms were field tested in indoor environments and yielded accurate matching results as well as a significant enhancement to positional accuracy. The achieved results demonstrate that the contribution of the developed map aided system enhances the reliability, usability, and accuracy of navigation trajectories in indoor environments.

Design of dead reckoning system for mobile robot

A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.

Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning

To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.

Signal Path Reckoning Localization Method in Multipath Environment

In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.

翻译目的论视角下的对话分析及翻译策略——以A Reckoning为例

从翻译目的论出发,分析了May Sarton的小说A Reckoning中的人物对话,并在翻译目的论和该理论三大原则的指导下,通过深入剖析原文和译文,从目的原则下句式的转换、连贯性原则下语境的融合、忠实性原则下的对话生动性进行了分析,揭示了译文中所使用的主要翻译策略,以期对相关小说的翻译有所启发。

Filtering and Estimation of Vehicular Dead Reckoning System Based on Hopfield Neural Network

The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estimation based on Hopfield network is proposed. Compared with Kalman filter, the algorithm does not require very precise system model and the prior knowledge of noise statistics and does not diverge easily. The simulation results show that the vehicular dead reckoning system based on Hopfield network filtering and estimation has the good position precision, and needn't require the inertial sensors with high precision. Therefore, the algorithm has the good practicability.

基于增强现实技术的电力设备运维检修仿真方法

为解决电力设备运维检修中,运行状态监测不够全面的问题,提出基于增强现实技术的电力设备运维检修仿真方法。基于步行者航位推算技术与GPS技术设计无线定位平台,无线定位检修人员并记录行动轨迹;拍摄电力设备运维检修图像,基于增强现实技术,结合MEEM算法与ORB算法设计增强现实三维注册技术;以3dsMax软件构建电力设备运维检修三维模型。融合定位结果、三维注册结果与模型,实现仿真中的虚实融合、人机交互。测试结果表明,该方法能够实现带电区域检测的仿真以及电力设备识别、运维检修状态检测的仿真,证明该方法拥有良好的仿真性能。

面向室内语音导航的“附近”关系协同定位方法

针对传统室内定位模式单一,且单一“附近”空间关系无法满足定位需求的问题,提出一种融合“附近”空间关系/无线保真(WiFi)/行人航迹推算(PDR)/地磁的室内定位方法:阐述多源融合语音交互室内定位方法框架;并针对室内环境,建立“附近”空间关系概率密度函数;然后步行构建地磁/WiFi指纹库;最后后通过“附近”空间关系约束粒子及指纹匹配空间范围,在PDR推算位置基础上,利用WiFi/地磁/“附近”空间关系更新粒子权重,实现最优位置估计。实验结果表明,基于粒子滤波融合单一“附近”空间关系/WiFi/PDR/地磁为一种低成本可行的定位方法,平均定位精度1.93 m,有90%可能性定位精度可达2.52 m。

行人自主导航技术综述

导航与位置服务是我国经济新增长点和战略性新兴产业。近年来,行人自主导航技术由于可为行人提供高自主、高智能、高可靠和低成本的位置服务而备受关注,成为了导航与位置服务赋能可穿戴设备的关键驱动力。针对惯性、磁场和视觉三大主流行人自主导航方法进行了调研分析,梳理了行人自主导航技术的研究进展,重点阐述了各类技术途径的原理与特点,并指出了行人自主导航技术的未来发展趋势。

一种改进的行人航迹推算算法研究

针对行人在室内定位不准确的问题,提出一种改进的行人航迹推算算法。在步数检测阶段,提出一种基于运动分割的三阈值峰值检测法,实现了行人在不同运动状态下步数的精准检测。通过使用改进的Weinberg模型实现步长估算。并提出一种基于主方向假设的航向角修正算法,实现行人的航向角修正。最后综合步数、步长和航向角信息实现室内行人的航迹推算。实验结果表明,改进的行人航迹推算算法在室内有较好的稳定性,在室内的平均定位误差<5%,较传统PDR算法在平均定位误差上降低了9.53%,提高了行人在室内定位的精度。

基于安卓智能手机的高精度定位系统研发及测试

针对常规外业坐标数据采集方式存在的问题,结合高精度位置服务便捷化应用需求,设计并研发了一套基于安卓智能手机的高精度定位系统,通过融合卫星导航定位基准站差分定位与步行者航位推算算法,并制定多种观测环境差异化测量策略,以获取连续的高精度定位结果。基于华为P40、小米8等四款安卓智能手机,进行了广覆盖(湖南省14个市(州))、多场景(开阔、遮挡)的定位精度测试,用于验证系统的可用性和可靠性。测试结果表明,系统在不同智能手机、不同场景中均能稳定有效运行,华为P40的平面定位精度最优,在开阔环境下,最高可达0.2m左右。系统已全面应用于湖南省农村住房规划建设“三到场”环节。

浅析对智能的误解与新智能的建构

目前,GPT、Sora很多人使用后仍深感其不足和欠缺,究其因,智能终究是一个多因、多果、多维、多元的世界,这意味着智能的发展和应用涉及多个因素、产生多种结果、涉及多个领域和维度,并不是一个管理、一个数理、一个心理、一个生理、一个哲理、一组统计公式、一堆数学方程就能搞定的。若从人机环境系统智能的角度出发会不难发现:智能的形成和进化是由人、机器、环境等多种因素相互作用和影响的结果,这些因素包括技术进步、理论研究、数据积累、算法优化、社会需求等,智能技术的发展需要这些因素相互配合和促进。还有,智能涉及多个维度和层面,智能技术的发展不仅仅限于大规模数据处理和模式识别,还包括推理、学习、决策、创造等多个层面,智能的发展需要综合考虑这些维度和层面,使智能系统能够更好地适应和应对各种复杂任务和环境。

一种穿戴式多IMU的航向对齐方法

针对基于穿戴式多惯性测量单元(IMU)的行人定位与步态测量应用中,多IMU之间的航向对齐通常依赖磁力计、专业人员辅助以及关节假设模型,操作流程复杂度高且精度无法保障的问题,提出一种适用下肢安装多IMU的航向对齐方法:设计适用足部、小腿和大腿IMU的统一的杆臂补偿零速修正算法,利用行人足部周期性触地规律构造的零速度观测值,准确恢复所有IMU的运动轨迹;然后基于轨迹相似性约束,实现多个IMU的航向对齐;最后在室外开阔场景下评估运动轨迹恢复精度和航向对齐精度。实验结果表明,航向对齐精度和各部位IMU的轨迹恢复精度呈正相关;在行走轨迹长度大于15 m时,各IMU的航向对齐精度均优于1°(均方根值)。该方法操作简单,易于实施,能够满足基于多IMU的人体运动分析和行人定位应用需求。

基于人体运动识别约束的室内定位方法

针对传统的行人航迹推算(Pedestrian Dead Reckoning,PDR)方法无法满足多运动状态下的定位问题,提出了一种基于神经网络运动识别辅助室内定位的方法。构建出卷积神经网络(Convolutional Neural Network,CNN)和门控循环单元(Gated Recurrent Unit,GRU)组合的神经网络模型,用于识别人体的运动状态并完成分类。根据运动分类的结果应用到行人航迹推算中,分析和筛选运动参数特征作为算法的阈值约束条件来提高定位精度。在算法中运动步数由合加速度计数据波形检测得到,步长由运动状态的特征自适应调整步长模型。通过实验验证,CNN-GRU模型在自建数据集上的准确率达到99.6%。将识别结果应用到PDR中,在112 m 4种动作的矩形路线中定位误差为1.8 m,误差远低于传统PDR定位的19.9 m。实验结果验证了该方法的可行性。

基于混合H2/H∞滤波信息融合的室内定位算法

在接收信号强度指示(RSSI)指纹定位与行人航位推算(PDR)组合室内定位模型中,针对噪声误差和模型误差对融合定位的影响,提出了一种基于混合H2/H∞滤波的融合定位方法。由于室内信号存在多径传播、衰减、非视距(NLOS)的影响以及传感器精度问题,系统或观测噪声往往不符合卡尔曼滤波(KF)的假设前提,造成滤波稳健性差。H∞滤波不对噪声特性和模型误差做出假设,具有一定抗干扰能力,但由于结果保守,精度略低。混合H2/H∞滤波同时兼顾了二者的特性,在精度和鲁棒性提供一种均衡。通过仿真实验对比这3种滤波的融合定位性能。实验结果表明:混合H2/H∞滤波有效提高了指纹/PDR组合室内定位系统的精度和鲁棒性。

PDR与UWB融合的森林消防员定位系统设计

针对森林消防员在林区环境下由于全球导航卫星系统(GNSS)定位拒止及惯性导航误差累积而难以精确定位的问题,设计了一种融合行人航位推算(PDR)和超宽带(UWB)测距的定位系统。首先,基于消防员个体装备的便携性要求,利用加速度计、磁强计、陀螺仪和气压计设计头戴式定位系统;其次,将指挥员作为基站,利用UWB测得消防员与基站之间的距离;接着,基于扩展卡尔曼滤波(EKF)设计了PDR与UWB融合的定位算法,将测距信息与消防员的运动状态进行融合解算,约束误差发散;最后,利用设计的软硬件定位系统进行了验证实验。结果表明:在真实林区实验中,与无约束PDR相比,采用PDR与UWB融合的定位方法将位置误差减小了60.04%,有效提升了系统的定位精度。

基于UWB与PDR的井下人员融合定位方法

现有超宽带(UWB)与行人航位推算(PDR)融合定位方法大多忽略了非视距(NLOS)环境下的定位误差校正,以简单的阈值划分作为NLOS环境判断依据,而阈值划分在很大程度上与定位场景及场地大小相关。针对上述问题,提出一种考虑NLOS环境的基于UWB与PDR的井下人员融合定位方法。首先,利用UWB技术进行井下人员位置解算,通过三边定位算法得到人员初步位置后,使用最小二乘法对位置进行优化,通过多项式拟合实现NLOS环境下基站和标签之间实际值和测量值之间的拟合,减小NLOS环境下的测距误差,提高定位精度。其次,采用PDR算法对步态进行识别和分析,PDR算法使用惯性导航传感器采集的步态数据,通过步态识别、步长估计和方向估计,实现目标位置的更新;然后,通过卷积神经网络(CNN)-长短期记忆(LSTM)网络分析信道脉冲响应(CIR)特征,实现视距(LOS)/NLOS识别,解决NLOS环境判断存在场景限制的问题;最后,根据LOS/NLOS识别结果确定融合系数,实现UWB和PDR定位结果融合。测试结果表明:多项式拟合后UWB平均测距误差降低0.59 m;LOS/NLOS识别的平均准确率为95.3%,召回率和F1分数均在90%以上,验证了CNNLSTM具有较好的识别效果;融合定位方法的平均误差为0.31 m,较UWB降低1.57 m,较PDR降低1.41 m。