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.

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.

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.

Susceptibility-weighted imaging is suitable for evaluating signal strength in different brain regions of a rabbit model of acute hemorrhagic anemia

Acute hemorrhagic anemia can decrease blood flow and oxygen supply to brain, and affect its physiological function. While detecting changes in brain function in patients with acute hemorrhagic anemia is helpful for preventing neurological complications and evaluating therapeutic effects, clinical changes in the nervous systems of these patients have not received much attention. In part, this is because current techniques can only indirectly detect changes in brain function following onset of anemia, which leads to lags between real changes in brain function and their detection.

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.

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.

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.

Vehicle Relay Attack Avoidance Methods Using RF Signal Strength

The number of passenger cars equipped with a smart key system continues to increase due to the convenience of the system. A smart key system allows the driver to enter and start a car without using a mechanical key through a wireless authentication process between the car and the key fob. Even though a smart key system has its own security scheme, it is vulnerable to the so-called relay attacks. In a relay attack, attackers with signal relaying devices enter and start a car by relaying signals from the car to the owner’s fob. In this study, a method to detect a relay attack is proposed. The signal strength is used to determine whether the signal received is from the fob or the attacker’s relaying devices. Our results show that relay attacks can be avoided by using the proposed method.

Simulation Evaluation of Filtering Method for Improving Pedestrian Positioning Accuracy Using Signal Strengths

In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consider reducing traffic accidents by exchanging position information between pedestrians and vehicles by vehicle-to-pedestrian communication, we require accurate position information for pedestrians and vehicles. The GPS (global positioning system) is the most widely used method for acquiring position information. However, in urban areas, the GPS signal is affected by the surrounding buildings, which increases the positioning error. In this study, a method to improve the positioning accuracy of pedestrians using the signal strengths from vehicles and beacons was proposed. First, a Kalman filter was applied to the signal strength. Then, the path loss index was dynamically calculated using vehicle-to-vehicle communication. Finally, the position of a pedestrian was obtained using weighted centroid localization (WCL) after filtering the nodes. The positioning accuracy was evaluated using a simulator and demonstrated the superiority of the proposed method.

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.

Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression

We propose a novel indoor positioning algorithm based on the received signal strength(RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering(AC) strategy is used, an online phase of approximate localization at which cluster matching is used, and an online phase of precise localization with kernel ridge regression. Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the K-medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization, RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then, a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments, and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.

Sensor selection for received signal strength-based source localization in wireless sensor networks

Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained. A sensor selection method is proposed to improve the localization accuracy as well as to save energy consumption. By selecting only a limited number of sensor nodes based on the model accuracy and geometry structure analysis, localization performance is improved, and energy consumption is reduced. In addition, extensive simulations are presented to demonstrate that the estimation performance with the proposed sensor selection method is better than that without sensor selection.

Indoor Location Algorithm Based on the Measurement of the Received Signal Strength

The“distance-loss”model is amended by inserting a random distance-estimation variable.The estimation error is very small;thus,it does not change the log-normal distribution of the shadowing factor in the model.Then,an iterative method is introduced to reduce the influence of shadowing,and the location estimation based on the received signal strength will be improved.Simulations show that this algorithm is effective.

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。

基于深度学习的可见光通信系统室内三维定位

针对目前室内可见光通信系统三维定位的准确率与定位速度依然不佳的问题,提出一种基于深度学习的可见光通信系统室内定位方法。首先,设计了一个神经网络将指纹数据编码成二维阵列,利用卷积神经网络学习指纹阵列与目标位置之间的关系;然后,通过粒子群优化算法自动搜索卷积神经网络的超参数,以降低深度神经网络的训练难度。此外,设计了定位数据训练集、验证集与测试集的划分方法,有助于缓解神经网络的过拟合问题,并提高定位准确性。仿真结果表明,所提方法在6×6×4m3室内环境下的平均定位误差为0.024m,平均定位时间为0.478s。

WiFi与ZigBee双向移动跨网通信方法研究

物联网设备的爆发式增长推进了异构无线设备互联互通的进程,跨网通信技术(Cross-Technology Communication,CTC)允许同一频段下遵循不同底层协议的无线设备在无需网关的前提下实现直联,但移动状态下的双向跨网通信方法仍缺乏系统的研究.本文提出了一种基于能量感知的跨网通信方案——MobiCTC,它支持WiFi与Zig‑Bee设备移动状态下的双向跨网通信.WiFi到ZigBee方向,该方案利用RSSI(Received Signal Strength Indicator)作为解码信息,基于能级映射实现信息解码;ZigBee到WiFi方向,该方案采用CSI(Channel State Information)作为解码信息,充分挖掘CSI的幅度与相位信息,利用机器学习方法实现分类解码.最后,本文使用TelosB节点和USRP X310平台对MobiCTC方案进行了实验验证.实验结果表明,移动状态下WiFi到ZigBee方向的系统吞吐量为139.535 bps,较WiZig提高了1.82倍,符号错误率为0.016,与WiZig基本持平;ZigBee到WiFi方向的系统吞吐量为250 bps,较FreeBee提高了15.7%,符号错误率为0.0516,较ZigFi下降了23.21%.

Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation issues.As complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational efficiency.Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online phase.Bayesian networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user locations.These networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to navigate.This article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user location.The proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s location.It operates in both online and offline phases.In the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s location.Experimental results unequivocally showcase the superior performance of the proposed model when compared to other existing models and methods.The proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing approaches.Notably,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

Application of Adaptive Whale Optimization Algorithm Based BP Neural Network in RSSI Positioning

The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.