Hidden Markov Model Based Localization Using Array Antenna

We present a hidden Markov model (HMM) based localization using array antenna. In this method, we use the eigenvector spanning signal subspace as a location dependent feature. The eigenvector does not depend on received signal strength but on direction of arrival of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the radio wave. The conventional localization method based on fingerprinting does not take previous information into account. In our proposal algorithm with HMM, we take previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference point according to setting in advance. In an indoor environment represented in a quantized grid, we design the transition probability due to previous estimated position. Because of this, target’s movable range is obtained. In addition, we use maximum likelihood estimation method based on statics of correlation values. The correlation value is an indicator of pattern matching in a fingerprinting method. The most likely trajectory is calculated by Viterbi algorithm with above mentioned probabilities. The experimental results show that the localization accuracy is improved owing to the use of HMM.     

基于BP神经网络和泰勒级数的室内定位算法研究

 在研究分析室内无线信号传播特性和传统的室内定位算法的基础上,提出了用BP神经网络来拟合室内无线信号传播模型,避免了对无线信号传播模型中参数A和n的不精确估计.在训练完成的BP神经网络的输入层输入接收信号强度值RSSI(Received Signal Strength Indicator),在输出层即可得到对应的距离值,再利用泰勒级数展开法确定盲节点的坐标位置.最终通过Matlab仿真和ZigBee平台实验验证了算法的可行性和有效性.     

An Accurate Indoor RSSI Localization Algorithm Based on Active RFID System with Reference Tags

Radio frequency identification technologies are popular since their cost is very low and its data transmission based upon radio-wave communications. Where, the objects that are attached to tags are located using the reference tags. However, RSSI information suffers from the multipath propagation and anisotropic interference. So, the localization accuracy will be affected severely. Also, the multipath-propagation increases whenever the reference-tags increase, and so does for the cost, and signal interference. This paper presents a boundary virtual reference label algorithm for improving the indoor-efficiency by inserting some virtual reference tags on the boundary with establishing a linear regression model that eliminates unwanted tag information from the estimation procedure. The Results show that the localisation precision of the proposed approach has significantly increased, at least 78% without adding extra reference tags or radio frequency interference which represents a significant improvement over other algorithms .

     

Neural-based approach for localization of sensors in indoor environment

Location of wireless sensor nodes is an important piece of information for many applications. There are many algorithms present in literature based on Received Signal Strength (RSSI) to estimate the location. However the radio signal propagation is easily influenced by diffraction, reflection and scattering. Therefore algorithms purely based on RSSI may not accurately predict the position of the node. In the present work, an algorithm for estimating the position of mobile nodes is proposed which is based on a combination of Received Signal Strength (RSSI) and Link Quality Indicator (LQI). Artificial Neural Networks are used to establish the relationship between the location of the mobile node and the experimentally obtained values of RSSI and LQI. Two different algorithms namely, Bayesian Regularization and Gradient Descent are used to develop the neural network model. Proposed algorithms improve the localization accuracy and perform better than other state-of-the-art algorithms.     

Automatic virtual calibration of range‐based indoor localization systems

The localization methods based on received signal strength indicator (RSSI) link the RSSI values to the position of the mobile to be located. In the RSSI localization techniques based on propagation models, the accuracy depends on the tuning of the propagation models parameters. In indoor wireless networks, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we present an automatic virtual calibration method of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose a novel RSSI‐based localization algorithm that selects the RSSI values according to their strength, and uses a calibrated propagation model to transform these values into distances, in order to estimate the position of the mobile. Copyright © 2011 John Wiley & Sons, Ltd.     

基于RSSI和惯性导航的融合室内定位算法

针对目前对高精度室内定位算法的需求,提出一种基于接收信号强度识别（RSSI）和惯性导航的融合室内定位算法。基于无线传感网中ZigBee节点的RSSI值,采用位置指纹识别算法,对网络中的未知节点进行定位。结合惯性传感单元（IMU）提供的惯性数据,对RSSI定位结果进行融合修正。利用Kalman滤波器,采用状态方程描述待定位节点位置坐标的动态变化规律,从而实现一种以无线传感网络定位为主、IMU为辅的融合定位方法。仿真结果表明,提出的融合定位算法既能改善单独使用RSSI定位受环境干扰较大的问题,又能避免单独使用惯性导航带来的累积误差,极大地提高了定位精度。     

改进指纹强度下的WiFi室内定位算法

目前的室内定位算法利用信号传播模型求出信号强度后直接进行求解定位,使得定位误差较大.为提高定位精度,提出利用信号能量的欧氏距离方法进行加权,然后对改进后的信号强度进行阈值滤波加均值滤波双重处理.定位阶段,在K最近邻算法基础上利用距离的倒数作为加权函数的算法进行定位.经仿真结果表明,改进后的算法相比于一些典型的定位算法,定位精度有较大提高.     

轻量扩展的射频指纹地图构造方法

随着群智感知和机器学习的融合,基于射频指纹的室内定位技术引起研究者的广泛关注。然而现有工作存在指纹地图构建阶段开销过大形成的可扩展性和实时性瓶颈问题。针对这一问题,该文提出一个新颖的轻量可扩展指纹地图构造方法(FFIL)。在指纹构建阶段,将整个室内环境划分为多个环路快速分割地图并获取射频指纹;在指纹匹配阶段,首先计算AP与目标点间的距离,然后选择与圆环半径最相似的环路上的参考点一一匹配;在定位阶段,采用等高线聚类算法来提高定位精度。通过真实数据驱动的大量仿真和实验证明,FFIL能减小指纹地图构建的开销,同时提高定位精度和系统实时性。     

RFID Indoor positioning based on Semi-supervised Actor-Critic Co-training

For large-scale radio frequency identification ( RFID) indoor positioning system, the positioning scale isrelatively large, with less labeled data and more unlabeled data, and it is easily affected by multipath and whitenoise. An RFID positioning algorithm based on semi-supervised actor-critic co-training (SACC) was proposed tosolve this problem. In this research, the positioning is regarded as Markov decision-making process. Firstly, theactor-critic was combined with random actions and selects the unlabeled best received signal arrival intensity(RSSI) data by co-training of the semi-supervised. Secondly, the actor and the critic were updated by employingKronecker-factored approximation calculate (K-FAC) natural gradient. Finally, the target position was obtained byco-locating with labeled RSSI data and the selected unlabeled RSSI data. The proposed method reduced the cost ofindoor positioning significantly by decreasing the number of labeled data. Meanwhile, with the increase of thepositioning targets, the actor could quickly select unlabeled RSSI data and updates the location model. Experimentshows that, compared with other RFID indoor positioning algorithms, such as twin delayed deep deterministic policygradient (TD3), deep deterministic policy gradient (DDPG), and actor-critic using Kronecker-factored trustregion ( ACKTR), the proposed method decreased the average positioning error respectively by 50.226%,41.916%, and 25.004%. Meanwhile, the positioning stability was improved by 23.430%, 28.518%, and38.631%.     

Scene analysis indoor positioning enhancements

The emergence of innovative location-oriented services and the great advances in mobile computing and wireless networking motivated the development of positioning systems in indoor environments. However, despite the benefits from location awareness within a building, the implicating indoor characteristics and increased user mobility impeded the implementation of accurate and time-efficient indoor localizers. In this paper, we consider the case of indoor positioning based on the correlation between location and signal intensity of the received Wi-Fi signals. This is due to the wide availability of WLAN infrastructure and the ease of obtaining such signal strength (SS) measurements by standard 802.11 cards. With our focus on the radio scene analysis (or fingerprinting) positioning method, we study both deterministic and probabilistic schemes. We then describe techniques to improve their accuracy without increasing considerably the processing time and hardware requirements of the system. More precisely, we first propose considering orientation information and simple SS sample processing during the training of the system or the entire localization process. For dealing with the expanded search space after adding orientation-sensitive information, we suggest a hierarchical pattern matching method during the real-time localization phase. Numerical results based on real experimental measurements demonstrated a noticeable performance enhancement, especially for the deterministic case which has additionally the advantage of being less complex compared to the probabilistic one.     

基于射频识别的室内定位技术综述

用于室内定位的技术主要包括红外传播技术、802.11 WLAN技术、无线传感器网络(WSNs)、超声波定位。而射频识别(RFID)技术依赖其合理的系统造价、良好的定位精确度、较强的抗干扰能力、无接触通信以及RFID射频标签的部署、携带便捷等诸多优点,正逐渐应用于室内追踪定位中。本文从用于室内定位的RFID标签的角度出发,将当前主要的RFID室内定位技术分为无源、有源和半无源RFID定位3类,并对每一类中涉及到的主要定位算法的当前发展状况及其优缺点做了详细分析。     

Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals

Current localization techniques in outdoors cannot work well in indoors. Wi-Fi fingerprinting technique is an emerging localization technique for indoor environments. However in this technique, the dynamic nature of WiFi signals affects the accuracy of the measurements. In this paper, we use affinity propagation clustering method to decrease the computation complexity in location estimation. Then, we use the least variance of Received Signal Strength (RSS) measured among Access Points (APs) in each cluster. Also we assign lower weights to altering APs for each point in a cluster, to represent the level of similarity to Test Point (TP) by considering the dynamic nature of signals in indoor environments. A method for updating the radio map and improving the results is then proposed to decrease the cost of constructing the radio map. Simulation results show that the proposed method has 22.5% improvement in average in localization results, considering one altering AP in the layout, compared to the case when only RSS subset sampling is considered for localization because of altering APs.

     

Power control for data load balancing with coverage in dynamic femtocell networks

We consider the load balancing and coverage problem of femtocell networks in indoor environment. We propose a novel framework exploiting the Voronoi diagram with respect to the radio propagation distance. Our initial power assignment scheme achieves maximal indoor coverage with minimized maximum required transmit power, which results in reduced interference. Our approach can adopt any radio propagation model to achieve more accurate coverage for an indoor environment with various obstacles. Time varying data traffic may cause unbalanced data load of base stations producing traffic overload. Our dynamic power control algorithm redistributes the data load by automatically adjusting transmit power levels according to data traffic estimation while preserving coverage. Moreover, we present an algorithm to cope with the dynamic deletion and insertion of femto base stations. Simulations show that the proposed scheme achieves better coverage, reduced interference, and good load balance compared to previous algorithms.     

基于传播图论的多链路信道仿真及其在TDOA定位系统性能评估中的应用

为了解决在真实环境下到达时间差(time difference of arrival,TDOA)定位性能评估所面临的测量成本高、不可控干扰因素较多等问题,提出了 一种利用传播图论多链路信道仿真来评估TDOA定位系统性能的方法.即采用传播图论的方法生成不同的信道冲激响应,并通过软件无线电装置产生经信道畸变的射频信号,作为...     

Phase fluctuations of cross-polarized signals at 4 GHz in fine weather on earth-space paths

On an earthspace propagation path with the low elevation angle of 10 deg, the phase between co- and crosspolar signals occasionally showed rapid and irregular fluctuations during fine weather. These fluctuations were generally significant during the daytime, and were strongly in phase with the occurrence of the copolar amplitude scintillations. This could be attributed to the combined effects of the crosspolar phase pattern of the receiving antenna and small fluctuations of the angle of arrival of the radiowaves.     

Enhanced calibration technique for RSSI-based ranging in body area networks

Indoor positioning systems based on Received Signal Strength Indicator (RSSI) in Wireless Sensor Networks (WSNs) are commonly used. The position accuracy in these systems is highly affected by the wireless medium variability, and therefore, a precise calibration is necessary to translate the power measurements to corresponding distance between each pair of nodes. In this paper, we propose a calibration scheme that is tailored to Body Area Networks (BANs) applications. The a priori knowledge about the environment conditions in these applications can increase the accuracy of the localization system, improve its robustness to interference, and reduce the number of RSSI measurements which are required for the calibration process compared to the traditional calibration methods. We define a criterion to obtain the calibration scheme using different a priori knowledge for both the mapping table and the path-loss model parameters. For evaluation of our new calibration scheme, we conducted a series of experiments in a real-world indoor environment, focusing on a proximate environment that is commonly used in BANs. We showed that for a tracking application, calibration methods utilizing the a priori knowledge are superior in terms of localization accuracy over other existing calibration methods with relatively small number of offline measurements.     

Optimization of an RFID location identification scheme based on the neural network

An indoor localization technology is increasingly critical as location‐aware applications evolve. Researchers have proposed several indoor localization technologies. Because most of the proposed indoor localization technologies simply involve using the received signal strength indicator value of radio‐frequency identification (RFID) for indoor localization, radio‐frequency interference, and environmental factors often limit the accuracy of localization results. Therefore, this study proposes an accurate RFID localization based on the neural network (ARL‐N²), a passive RFID indoor localization scheme for identifying tag positions in a room, combining a location identification based on dynamic active RFID calibration algorithm with a backpropagation neural network (BPN). The proposed scheme composed of two phases: in the training phase, an appropriate BPN architecture is constructed using the training data derived from the coordinates of reference tags and the coordinates obtained using the localization algorithm. By contrast, the online phase involves calculating the tracking tag coordinates and using these values as BPN inputs, thereby enhancing the estimated location. A performance evaluation of the ARL‐N² schemes confirms its high localization accuracy. The proposed method can be used to locate critical objects in difficult‐to‐find areas by creating minimal errors and applying and economical technique. Copyright © 2013 John Wiley & Sons, Ltd.     

A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network

In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also the coordinates of the anchor points (APs). The localization accuracy depends on the errors in the distance estimation with the RSS measurements and the size of the polygon composed of the APs used for the lateration. Since errors are inevitably involved in the RSS measurement, we focus on reducing the size of the polygon to increase the localization accuracy. We use the centroid of the polygon as a reference point to estimate the relative location of a target in the polygon composed of the APs hearing the target. Once the relative position is estimated, only the APs covering the area are used for localization. We implement the localization method and evaluate the accuracy of the proposed method in various radio propagation environments. The experimental results show that the proposed method improves the localization accuracy and is robust against the dynamically changing radio propagation environments over time.     

一种基于动态射频地图的室内定位方法

为了避免室内定位受环境变化、遮蔽物遮挡等因素的影响,提出了一种动态射频地图构建方法,通过参数估计节点动态感知环境参数的变化,采用回归分析方法实时估计信号传播经验模型的参数,基于参数估计节点估计的环境参数、模型参数及信号强度等信息建立具有环境自适应能力的动态射频地图,然后采用粒子滤波算法实现对未知节点的位置估计。仿真及实验结果表明,该地图构建方法可动态适应环境的变化,定位算法可取得较精确的位置估计结果。     

Investigation of radio channel uncertainty in distance estimation in wireless sensor networks

The distance estimation between nodes is a crucial requirement for localization and object tracking. Received signal strength (RSS) measurement is one of the used methods for the distance estimation in wireless networks. Its main advantage is that there are no additional hardware requirements. This paper describes a lateration approach for localization and distance estimation using RSS. For the purpose of investigation of RSS uncertainty, several scenarios were designed for both indoor and outdoor measurements. The first set of RSS measurement scenarios was proposed with the intention of hardware independent investigation of radio channel. For the second set of measurements, we employed IRIS sensor nodes to evaluate the distance estimation with certain devices. The experiments considered also obstacles in the radio channel. The results obtained in the proposed scenarios present usability of the method under different conditions. There is also a signal propagation model constructed from measured data at a node, which subsequently serves for distance determination.