Constrained Weighted Least Squares Location Algorithm Using Received Signal Strength Measurements

Determine the location of a target has gained considerable interest over the past few years. The Received Signal Strength(RSS) measurements and Differential RSS(DRSS) measurements can be converted to distance or distance ratio estimates for constructing a set of linear equations. Based on these linear equations, a constrained weighted least Squares(CWLS) algorithm for target localization is derived. In addition, an iterative technique based on Newton's method is utilized to give a solution. The covariance and bias of the CWLS algorithm is derived using perturbation analysis. Simulation shows that the proposed estimator achieves better performance than existing algorithms with reasonable complexity.

Multi-Variable Flocking Control for Multi-Agent Systems via a Received Signal Strength Indicator

A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.

S^3 LQA: A Link Quality Assessment Metric for WSNs Based on Symbol Error and Received Signal Strength

With the rapid evolution of WSNs technology, it is very important to evaluate link quality quickly and accurately, so that the routing protocols can take relevant strategies in time to keep the entire network working steadily and efficiently. However, the issue of layer is still open to research. To tackle this issue, a improving link quality assessment methods on physical novel link quality assessment metric called S3LQA is proposed, which estimates the link quality of wireless sensor networks by CC2420 wireless radio frequency transceiver principles and free space propagation theory. The metric adopts both complete and incomplete packages to improve the evaluation performance effectively based on IEEE802. 15.4 frame format and DSSS-O- QPSK mechanism. The experimental results show that the proposed method can improve energy cost and achieves hatter real-timin nerformance than traditional counting-based （PRR） link aualitv assessment metric.

Accuracy Analysis of Position Estimation Based on Measurements of Received Signal Strength Difference

The performance of a cellular location system based on received signal strength difference （RSSD） is investigated. In the cellular location system, each mobile station needs to measure the signal strength transmitted by surrounding base stations, and sends its measurements to the service base station. Using the strength difference between the service base station and neighboring base stations, the position of a mobile station is estimated. The related Cramer-Rao lower bound （CRLB） on the location error of this method was derived, and numerical simulations are made to discuss the influences of the number of base stations, correlation coefficient of shadowing attenuation, and cell radius on CRLB. The results show that the CRLB is positively correlated with the standard deviation of shadowing attenuation and cell radius, but negatively correlated with the number of base stations and the correlation coefficient of shadowing attenuation. In addition, the CRLB results obtained in this paper were compared with those of the cellular location system based on received signal strength （RSS） measurements, which reveals that the former is more tight.

Analysis of RFID Based Positioning Technique Using Received Signal Strength and Directional Antenna

Automatic robot navigation is being utilized in many industries for the purpose of high speed work delivery. Color follower, fix path follower robots are current solution to this activities but dynamic path configuration is not possible in these robots. Hence new system proposes effective and fully dynamic path follower robots using RFID and directional antenna. Radio Frequency Identification (RFID) system permits automatic identification of objects with RFID tags using radio waves which have been widely used in mobile robot navigation, localization and mapping both in indoor and outdoor environment. This article presents a navigation strategy for autonomous mobile robot using passive RFID system. Proposed robot system is provided with RFID tag functionality which will load tag number and direction instruction. At some turning point, user will put RF tag, this tag will be read by RF reader which is placed on robot. As per direction instruction robot will change the direction and reach to the destination. Also as per the movement, robot will send its GPS location to PC (Personal Computer) which will be displayed on PC. Hence main goal is to provide more reliable and low energy consumption based indoor positioning system which will be achieved using directional antenna.

基于混合滤波的RSSI无线网络测距算法

针对基于ZigBee网络的节点接收信号强度指示(received signal strength indication,RSSI)在复杂环境测量会产生偏差的问题,提出一种基于混合滤波的无线网络测距算法。该方法在运用卡尔曼滤波的基础上融合了基于中值自适应加权高斯滤波的混合滤波,首先用卡尔曼滤波算法去除波动性较大的RSSI值,再利用中位值抗差性原理和自适应函数降低RSSI数据的波动。仿真实验结果表明,混合滤波无线网络测距算法能够较大程度减小异常值带来的波动,有效提高RSSI采样精度。

一种WiFi混合滤波的RSSI室内定位算法

针对接收信号强度指示(RSSI)在基于WiFi的室内定位应用中易受到环境影响出现波动、突变进而导致定位精度降低的问题,提出了一种WiFi混合滤波的RSSI室内定位算法。首先,根据WiFi信号距离衰减模型设计一种自适应阈值滤波器剔除观测信号异常值;然后,根据自适应阈值滤波、卡尔曼(Kalman)滤波、均值(Mean)滤波设计了一种混合滤波算法(ATKM)对RSSI数据进行滤波;最后,提出了一种基于斯皮尔曼-欧几里得距离的加权K近邻算法(SEWKNN)进行位置估计。在真实环境下的实测结果表明,本文提出的ATKM滤波算法能显著降低RSSI信号的波动,提出的SEWKNN算法在室内环境中平均定位误差为1.17 m,在走廊环境中平均定位误差为1.53m,相比传统的WKNN算法平均定位误差分别下降18.18%和16.84%。

基于RSSR融合RNGO-Elman神经网络的室内可见光定位

针对动态环境下基于接收信号强度的传统可见光定位方法定位精度低、稳定性差等问题,提出一种基于接收信号强度比的改进北方苍鹰算法(NGO)优化Elman神经网络(RNGOElman)的室内可见光定位系统。提出选择一个辅助参考点,将待测参考点与辅助参考点的接收信号强度比值和接收机的真实位置作为训练集数据,建立不受动态环境影响的指纹数据库。针对NGO算法收敛速度慢、容易陷入局部最优等问题,利用折射反向学习策略初始化种群,增加种群多样性,引入非线性权重因子来加快收敛速度,避免陷入局部最优。使用优化后的NGO算法来优化Elman神经网络的初始权值和阈值,构建RNGO-Elman动态定位预测模型。仿真结果表明,在4m×4m×3m的实验空间下,优化后的RNGO-Elman定位模型平均定位误差为1.34cm,定位精度相较于Elman定位算法、NGO-Elman定位算法分别提高了82%,21%。在LED发射功率波动时,基于RSSR的RNGO-Elman定位误差为1.29cm,1.38cm。所提可见光定位方法具有定位精度高、定位性能稳定等优点。

一种粒子群优化的RSSI室内定位算法

针对基于接收信号强度指示(Received Signal Strength Indicator,RSSI)测距模型的室内定位算法在复杂多变的室内环境下难以根据当前室内环境实时更新模型参数而导致定位精度下降的问题,提出了一种粒子群优化的RSSI室内定位算法。该算法利用蓝牙低功耗网状(Bluetooth Low Energy Mesh,BLE Mesh)网络中各节点可以相互通信的特点及对数路径损耗模型参数对距离转换的直接影响,将各锚节点间预测距离和真实距离的均方误差作为约束条件,采用粒子群优化对对数路径损耗模型的参数进行迭代优化,最终获得符合当前室内环境的模型参数,以进行室内定位。实验结果表明,提出的算法具有较好的收敛性能,且定位误差在1 m以内,能够有效满足室内定位的实际需求。

Localization Algorithm of Indoor Wi-Fi Access Points Based on Signal Strength Relative Relationship and Region Division

Precise localization techniques for indoor Wi-Fi access points(APs)have important application in the security inspection.However,due to the interference of environment factors such as multipath propagation and NLOS(Non-Line-of-Sight),the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS(Received Signal Strength)is difficult to accurately measure.Therefore,the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper.The algorithm hierarchically divide the room where the target Wi-Fi AP is located,on the region division line,a modified signal collection device is used to measure RSS in two directions of each reference point.All RSS values are compared and the region where the RSS value has the relative largest signal strength is located as next candidate region.The location coordinate of the target Wi-Fi AP is obtained when the localization region of the target Wi-Fi AP is successively approximated until the candidate region is smaller than the accuracy threshold.There are 360 experiments carried out in this paper with 8 types of Wi-Fi APs including fixed APs and portable APs.The experimental results show that the average localization error of the proposed localization algorithm is 0.30 meters,and the minimum localization error is 0.16 meters,which is significantly higher than the localization accuracy of the existing typical indoor Wi-Fi access point localization methods.

Design and application of wireless signal strength measurement system on the near-ground

The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.

基于改进麻雀搜索算法优化的RSSI定位

针对传统接收信号强度指示(RSSI)测距受环境中不同因素干扰,导致定位精度不高的问题,提出基于改进麻雀搜索算法(ISSA)优化的RSSI定位算法。首先,通过混合滤波对RSSI值优化处理,剔除异常值并消除波动,RSSI测距模型将滤波后可靠的信号值换算为距离。其次,对基础麻雀搜索算法(SSA)进行改进,得到未知节点的精确坐标。实验结果表明:与另一种ISSA及粒子群优化万有引力搜索算法(PSOGSA)混合定位算法相比,该算法具有更高的定位精度。

Localizing Jammer in an Indoor Environment by Estimating Signal Strength and Kalman Filter

Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.

RSSI和PC-CSI加权融合的指纹定位方法

针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。

RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.

基于深度学习与RSS滤波的可见光室内定位算法

为了提高可见光室内三维定位的准确率与稳定性,提出一种基于深度学习与接收信号强度滤波的可见光室内定位方法。首先,采集每个参考点接收的所有接收信号强度样本,使用聚类算法对接收信号强度样本进行滤波,剔除低值簇与高值簇中的高偏差接收信号强度样本;然后,在高精度无线电地图上训练深度神经网络回归模型,学习接收器位置与接收信号强度样本之间的统计关系。仿真结果表明,方法在6m×6m×3m室内环境下的平均定位误差小于4cm,且支持LED数量的扩展。

基于RSS正确性判定的矿井机车无线定位方法

实现矿井机车的定位,对矿井机车进行科学调度和实时控制,对于提高矿井生产效率、增强矿井安全生产水平具有重要意义。将基于WLAN的无线定位技术应用于矿井下,针对无线信号受到干扰而出现错误数据的情况,结合矿井巷道的一维线状特性和矿井WLAN部署方案,对传统的指纹识别定位算法进行改进,提出一种带有错误检测的矿井机车无线定位方法,使用AP信号强度的正确指数对采集到的信号强度进行正确性判定,从而提高定位精度。提出的算法相对于传统的指纹识别定位算法,仿真表明,平均定位精度提高了33%,实验表明,平均定位精度提高了32%。

基于RSS手指模的煤矿井下WLAN定位方法

到达信号强度(RSS)手指模定位技术已广泛应用于室内定位技术,提出了适用于煤矿井下由于电磁波多径效应而变得复杂的环境的RSS手指模定位算法。通过对煤矿井下电磁环境信息的采集,对采集到的信息进行处理,使用贝叶斯公式法估计出概率较大的3个位置,然后再使用最邻近法的欧几里德距离估计出位置。通过对实验数据的统计分析,仿真结果表明:提出的基于RSS手指模改进的融合算法的节点定位精度要比K邻近法的定位精度要高,定位性能要优越。

基于RSSI的测距差分修正定位算法

为了抑制RSSI误差对无线传感器节点自身定位精度的影响,以三边定位算法为基础,定义了个体差异差分系数、距离差分系数和距离差分定位方程,把离目标节点最近的信标节点作为参考节点对基于RSSI的测距进行差分修正,并将差分法和质心法相结合提出了一种测距差分修正定位算法。该定位算法无需增加额外硬件开销,容易实现,定位误差可小于2.5m,适合于处理能力和能量有限的无线传感器网络节点。

智能空间中RSSI定位问题研究

定位服务是智能空间所必须提供的基本服务,而定位精度的高低在很大程度上取决于距离测量精度.本文分析了现有无线测距技术运用到智能空间中存在的诸多问题,结合无线传感器节点的硬件特性,选择了基于RSSI的定位技术,提出了三种基于RSSI定位的实现机制:最小二乘曲线拟合法,信号强度分布法以及混合定位法.在此基础上,针对所提出的三种定位技术从定位计算量、定位误差等方面进行了实验测试与对比分析.结果表明,混合定位法所需的计算量小、定位精度高,能更好地满足于资源受限环境下的定位服务需求.由此可见,借助于本文提出的混合定位法,结合适当的迭代定位算法可以有效地应用于实际系统的定位.