基于LoRa信号的行走距离和方向估计

行走追踪在真实场景中具有广泛应用,可以用于安防监控、老人看护、室内导航等场景.近年来,基于无线射频信号的非接触式行走追踪受到了研究人员的广泛关注,包括基于Wi-Fi信号、RFID信号等的行走追踪系统.然而,现有的行走追踪系统依然面临感知范围小、感知设备部署稠密等问题.在本文中,我们首次将用于物联网低功耗、远距离通信的LoRa技术应用到非接触式的大范围行走追踪中,显著地增加了行走追踪系统的感知距离.特别地,通过利用LoRa网关上配置的多天线,利用两根天线上接收信号的比,可以有效地消除噪声干扰以及收发不同步带来的误差,从而进一步提升了感知范围,然后利用计算切线向量相位变化的方法准确计算原始信号中动态分量的相位变化来在估计行走距离和方向.基于此,本文提出基于LoRa的非接触感知系统,可以在一段自然连续的行走活动中准确地判断人的动静状态并切割出行走片段,进而计算出行走的距离和方向,实现人的行走追踪.实验验证了系统计算行走距离和方向的准确性和实时性,其中距离计算的平均误差为3.8%,准确判断行走方向所需时间为0.7 s.     

基于信道状态信息相位差的人员入侵检测方法

目前基于Wi-Fi的人员入侵检测方法缺少对相位信息的利用,从而限制了入侵检测的适用范围和感知距离.讨论了信道状态信息相位差的可行性,采用轻量级的指标来感知无线环境的状态变化.利用递归量化分析法对静止情况下的子载波进行挑选,采用离散小波变换提取呼吸信号,进一步判断当前环境是否有人入侵.不同实验场景的结果表明,运动和静止人员的检测精度都可以达到90％.     

基于高阶累积量的信道估计及数字调制识别方法①

无线信道的多径干扰使得接收信号发生畸变,其高阶累积量特征不再趋近于其理论值,给调制识别带来了很大困难。针对多径信道中的调制识别问题,提出了一种基于高阶累积量的信道估计及调制识别算法。首先提取接收信号的高阶累积量进行信道参数估计,根据估计结果对识别参数进行修正,使其重新趋近于理论值,从而完成调制识别。该算法仅根据接收信号序列进行运算,所需先验信息少,能对大部分调制方式进行识别。仿真结果证明了算法的有效性。     

基于视频的人体行走参数测量方法及应用

针对现有的人体行走参数测量方法复杂度高、效率低等问题,提出了一种基于视频的人体行走 参数测量方法。利用监督学习的方法对视频中的运动目标进行姿态估计,逐帧识别骨骼关节点。然后根据头部 和脚部特征点,结合场景标定获取的像素距离与实际距离的转换关系,实现行走身高测量;根据关节特征点, 利用余弦公式计算关节活动度;根据脚部特征点,提出了一种结合前后极点帧差和像素差判断行走步长和步速 的方法。最后提出了一种基于 Unity3D 的虚拟人随动控制方法,能够在虚拟场景中进行运动仿真,便于实时监 控和分析视频中的人体异常行为并做出预警。实验表明该方法具有操作简单、准确度高和实时性强等优点。     

基于CSI的日常行为识别方法

针对现有依靠穿戴设备、雷达和视频图像人体行为感知的方法对环境要求高,成本高,且不利于保护隐私等问题,提出一种基于信道状态信息无设备且低成本的日常行为识别方法.通过商用WiFi设备采集原始CSI数据,在无需信号进行去噪处理的情况下通过提取原始CSI最大程度能提高识别精度的三阶累积量特征,应用基于互信息的特征选择算法(MI...     

基于九轴传感器的行人室内定位方法

针对由步数、步长精度和航向角偏移带来的定位误差,提出一种基于九轴传感器的行人室内定位方法.利用一种改进的波峰检测法实现精准计步,利用传感器姿态的变化情况识别出原地踏步;在启发式随机漂移消除法(HDE)算法的基础上,提出一种基于主方向的航向修正方法对航向角进行修正;利用一种非线性步长估计模型对行走时的步长进行估算.实验结果表明,该方法切实有效地降低了步态检测的误差,优化了由陀螺漂移而产生的航向角发散问题,提高了用户在步长多样化时的估计精度.     

WiSFall:一种非接触式的浴室跌倒监测模型

封闭浴室场景中的跌倒行为危害性大，救助及时性差，对其高效便捷的监测研究有重要意义.针对现有基于Wi-Fi信号感知的跌倒算法中存在的特征提取不足、识别精度有限问题，本文提出了一种基于深度学习的非接触式浴室跌倒监测模型WiSFall.首先，WiSFall将一维时序CSI数据流重构为二维的频率能量图形式，使得感知数据的特征容纳能力得以增强；其次，对重构后的感知数据进行巴特沃斯滤波，以去除环境噪声；最后，将滤波后的感知数据以频率能量图形式输入到构建好的深度学习模型中，通过对感知特征的有效提取和分类实现Wi-Fi环境下高精度的浴室跌倒监测.实验结果表明，WiSFall在居家浴室环境下对跌倒行为监测的准确率达到99.63%,相比同类模型表现更好，且具有较强泛化能力.     

基于随机行走的自适应规模估计方法

在大规模动态网络环境中,节点需要系统规模信息以支持高效可靠的数据分发,针对该问题,提出一种基于随机行走的自适应规模估计方法——RASE。采用反熵聚集方法,在系统规模变化时发起随机行走以增强对动态环境的自适应性。结果证明,与已有方法相比,RASE方法在节点频繁波动的网络环境中仍然能准确快速地提供规模估计信息。     

基于自学习中枢模式发生器的仿人机器人适应性行走控制

为了克服传统中枢模式发生器(Central pattern generator, CPG)关节空间控制方法的复杂性和局限性, 本文基于自学习中枢模式发生器模型, 提出了一套在线调制和融合多传感器信息的仿人机器人环境自适应行走控制方法.算法难点在于如何在机器人的工作空间将自学习CPG用于工作空间轨迹生成, 并使CPG参数直接和步态模式相关联.本文提出了利用自学习CPG来学习和实时生成机器人质心轨迹和脚掌轨迹的方法, 在线调节机器人步长、抬腿高度和步行速度等关键参数.参考生物反射行为, 利用传感反馈信息激发CPG以产生具有环境适应性的工作空间轨迹, 提升行走质量. 控制系统的参数通过优化算法来进一步改善行走性能.相比于传统的CPG关节空间法, 本文所采用的自学习CPG工作空间法不仅极大简化了CPG网络结构而且提高了仿人机器人行走的适应性.最后, 通过仿人机器人坡面适应性行走的仿真和实验, 验证了所提出控制策略的可行性和有效性.     

基于生物力学的虚拟人运动控制及行走仿真

为使虚拟人运动控制产生的运动行为更加接近真实人体运动,构建了一个基于生物力学的虚拟人运动控制系统.该控制系统主要包含3层:高层策略控制层,用于监控环境变化和虚拟人自身状态,并依据给定的控制策略向中间层送出控制信号;中间脊椎反射层,可接收来自高层的控制信号和感知到的虚拟人自身状态及环境信息,并映射为肌肉的激励信号;底层肌肉驱动层,通过引入肌腱模型来产生与真实人体肌肉力特性相同的驱动力.为了提高仿真行走运动的逼真性,在控制参数优化过程中引入步态相似度作为评价标准.在Matlab中的Simulink环境下进行虚拟人行走运动仿真实验的结果表明,文中系统能够自主控制虚拟人平稳行走且运动有很好的逼真性;仿真结果表明,该系统可行性强,且在优化过程中加入步态相似度评价标准获得的控制参数能够产生更为逼真的虚拟人行走运动.     

Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, the most popular solutions are the Wi-Fi fingerprint-based approaches. The core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner. However the low participation rate blocks the practical use. In this work, we propose a passive crowdsourcing channel state information (CSI) based indoor localization scheme, C²IL. Despite a crowdsourcing based approach, our scheme is totally transparent to the client and the only requirement is to connect to our 802.11n access points (APs). C²IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI. Knowing the walking speed of a client and its surrounding APs, a graph matching algorithm is employed to extract the received signal strength (RSS) fingerprints and establish the fingerprint map. For localization phase, we design a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking. We develop and deploy a practical working system of C²IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2 m at 80% time in a very complex indoor environment.     

Development of JARoW-II active robotic walker reflecting pelvic movements while walking

This paper addresses the problem of a novel walking assist scheme considering pelvic movements. Generally, pelvic motion includes pelvic tilt, pelvic rotation, and lateral pelvic displacement. When a human walks, the pelvis is meant to both tilt and rotate. Specifically, rotational movement on the pelvis’ transverse plane and tilting movement on its coronal plane are related to stride length and step width in walking and center-of-gravity swaying in the left-and-right direction, respectively. With these considerations, we introduce the innovative design of our second generation assist robotic walker (JARoW-II) for elderly people in need of supervision. And, this paper proposes a pelvic based walking-support control technique employing JARoW-II. By facilitating pelvic movements while walking, we try to enhance and/or maintain ambulatory performances such as stride length. As another important feature, the scheme is realized without use of specific manual controls or additional equipment. In detail, JARoW-II allows to accurately generate both the direction and location of walking movement and the pelvic movement in a way that corresponds to the user’s walking steps. In this paper, the implementation details based on the walking-support scheme are explained, and the effectiveness of the scheme by using JARoW-II is verified through extensive experiments in everyday environments.     

基于并行空时深度学习网络的无设备身份识别方法

针对现有人员身份识别方法存在灵活性差、特征提取不足的问题,分析人体步态对信道状态信息(CSI)幅值产生的影响,提出一种基于Wi-Fi信道状态信息的无设备人员身份识别方法.将CSI幅值矩阵的每列子载波作为样本,利用小波变换获取样本中代表人员活动的低频信息.通过并行深度学习模型WiD(Wi-Fi identificatio...     

Adaptive robust Max-SLNR precoder for MU-MIMO-OFDM systems with imperfect CSI非完美信道状态信息下多用户MIMO-OFDM系统自适应稳健的最大化信泄噪比预编码算法

The accuracy of channel state information (CSI) available at a base station (BS) has a direct impact on the performance of precoding in wideband multi-user multiple input, multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems and depends on many factors, including: the delay between estimation and beamforming at the BS (also called the CSI delay), Doppler spread, the channel estimation method used, the average transmit power of pilot symbols, and the average number of pilot symbols that must be estimated per channel parameter. In this paper, the coefficient of CSI error needed to adapt to fading channels is modeled as a function of Doppler spread, CSI delay, and signal-to-noise ratio (SNR). In terms of the Gaussian-Markov CSI error model, an adaptive robust maximum signal-to-leakage-and-noise ratio (Max- SLNR) precoder is designed to track the statistical parameters of CSI error. The Doppler spread and SNR can be obtained through real-time estimation based on orthogonal pilot patterns. Simulation results show that, compared to non-adaptive robust and non-robust precoders of Max-SLNR, the proposed adaptive robust Max- SLNR precoder performs much better in terms of bit error rate (BER). Moreover, as either the average number of training symbols per channel parameter or the average transmit power increases, the BER performance of the proposed precoder approaches that of a precoder with ideal CSI.     

面向室内定位的改进行人航位推算方法

为提高室内定位精度,本文对行人航位推算的步数检测、步长估计及航向估计进行了改进研究;首先,将自相关分析法和自适应波峰检测法相结合提出了一种新检测算法;其次,基于Scarlett模型提出了融合前一步步长的改进步长估算模型;再次,基于方向传感器算法,通过设定主导航向并结合卡尔曼滤波,来减少直线行走航向估计误差;最后,通过实验验证改进方法提高了步数检测准确率和步长检测精度,减少了航向角误差,取得了较好的室内定位效果。     

A Control Strategy for Terrain Adaptive Bipedal Locomotion

Rhythmic movements of a five-link sagittal biped with muscle-likeactuators are considered. In walking, as the support phases changecontact is periodically made with the environment. The inputs toevery actuator are modeled after the inputs to muscles in mammals. Thesystem possesses intrinsic position and velocity feedback due to theactuator dynamics. A control strategy is articulated that is novelin that it; a) is physiologically viable; b) simplifies the dynamics;and c) adapts to the speed of walking, going up and down stairs,going up or down inclines, maneuvering over obstacles or holes, andthe tempo and stride length of walking. Walking simulations of afive-link sagittal biped are presented.     

基于无线信道状态相位信息优化的定位算法

随着位置服务需求的增长,基于Wi-Fi接收信号的室内定位技术一直是研究热点之一.通过检测环境变化对Wi-Fi无线信道状态信息CSI的影响,从而实现对室内人员的定位具有通用性强、部署成本低等优点.针对大多系统仅使用CSI中幅度信息所带来准确性和稳定性不足的问题,设计并实现了一种基于CSI相位信息优化的定位算法,该方法通过采集幅度和相位参数相结合作为位置指纹特征,并对特征数据进行预先平滑去噪后进行指纹库的构建,然后通过机器学习方法进行人员位置的分类识别.由于相位和幅度信息可以相互补充,弥补了某些易混淆位置的分类错误,从而解决了采用单一特征的定位准确性和稳定性问题.实验进行了两种不同多径场景下的实验,比较了不同指纹特征选取、数据预处理方法以及三种机器学习算法对定位准确度的影响,其结果表明采用本文所提出算法总体上可以在仅使用CSI幅度特征的基础上提高13%.     

Adaptive robust beamformer formulti-pair two-way relay networks with imperfect channel state information

In wideband multi-pair two-way relay networks, the performance of beamforming at a relay station (RS) is intimately related to the accuracy of the channel state information (CSI) available. The accuracy of CSI is determined by Doppler spread, delay between beamforming and channel estimation, and density of pilot symbols, including transmit power of pilot symbols. The coefficient of the Gaussian-Markov CSI error model is modeled as a function of CSI delay, Doppler spread, and signal-to-noise ratio, and can be estimated in real time. In accordance with the real-time estimated coefficients of the error model, an adaptive robust maximum signal-to-interferenceand- noise ratio (Max-SINR) plus maximum signal-to-leakage-and-noise ratio (Max-SLNR) beamformer at an RS is proposed to track the variation of the CSI error. From simulation results and analysis, it is shown that: compared to existing non-adaptive beamformers, the proposed adaptive beamformer is more robust and performs much better in the sense of bit error rate (BER); with increase in the density of transmit pilot symbols, its BER and sum-rate performances tend to those of the beamformer of Max-SINR plus Max-SLNR with ideal CSI.     

Kinematics based sensory fusion for wearable motion assessment in human walking

Measuring the kinematic parameters in unconstrained human motion is becoming crucial for providing feedback information in wearable robotics and sports monitoring. This paper presents a novel sensory fusion algorithm for assessing the orientations of human body segments in long-term human walking based on signals from wearable sensors. The basic idea of the proposed algorithm is to constantly fuse the measured segment's angular velocity and linear acceleration via known kinematic relations between segments. The wearable sensory system incorporates seven inertial measurement units attached to the human body segments and two instrumented shoe insoles. The proposed system was experimentally validated in a long-term walking on a treadmill and on a polygon with stairs simulating different activities in everyday life. The outputs were compared to the reference parameters measured by a stationary optical system. Results show accurate joint angle measurements (error median below 5°) in all evaluated walking conditions with no expressed drift over time.