GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

A plethora of indoor localization systems based on Wi‐Fi, radio frequency chips, ultra‐wide‐band, and bluetooth have been proposed, yet these systems do not work when the infrastructure is absent. On the other hand, infrastructure less systems benefit mostly from off‐the‐shelf smartphone sensors and do not need additional hardware. This study shows a similar indoor localization approach which turns smartphone built‐in sensors to good account. We take advantage of magnetic field strength fingerprinting approach to localize a pedestrian indoor. In addition, accelerometer and gyroscope sensors are utilized to find the pedestrian's traveled distance and heading estimation, respectively. Our aim is to solve the problem of device dependence by devising an approach that can perform localization using various smartphones in a similar fashion. We make the use of patterns of magnetic field strength to formulate the fingerprint database to achieve this goal. This approach solves two problems: need to update the database periodically and device dependence. We conduct experiments using Samsung Galaxy S8 and LG G6 for five different buildings with different dimensions in Yeungnam University, Republic of Korea. The evaluation is performed by following three different path geometries inside the buildings. The results show that the proposed localization approach can potentially be used for indoor localization with heterogeneous devices. The errors for path 1 and path 2 are very similar, however, localization error for path 3 is comparatively higher because of the complexity of the path 3. The mean and median errors for Galaxy S8 are 1.37 and 0.88 m while for LG G6, these are 1.84 and 1.21 m, respectively, while considering all buildings and all paths followed during the experiment. Overall, the proposed approach can potentially localize a pedestrian within 1.21 m at 50% and within 1.93 m at 75%, irrespective of the device used for localization. The performance of the proposed approach is compared with the K nearest neighbor (KNN) for evaluation. The proposed approach outperforms the KNN     

Nonparametric Model Comparison and Uncertainty Evaluation for Signal Strength Indoor Location

Indoor Location (IL) using Received Signal Strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (WiFi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used and the actual performance of the proposed fingerprinting algorithms are still controversial. In addition, the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: 1) A nonparametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models and 2) new accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our WiFi network, showing relevant information relating to the different fingerprinting schemes that can be used.     

Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation

In this paper, we proposed an enhanced pedestrian dead reckoning (PDR) system based on sensor fusion schemes using a smartphone. PDR is an effective technology for 3D indoor navigation. However, still, there are some obstacles to be overcome in its practical application. To track and simulate pedestrian’s position, which is confronted by environmental errors, walls, Bayesian errors, and other obstacles, our proposed PDR system enables estimation of stride based on the vertical accelerometer data and orientation from sensor fusion technique of magnetic angular rate and gravity sensor data by Madgwick filter. This localization system is independent of the received signal strength-based fingerprinting system. In addition, to estimate the current floor level, we make use of barometer information. To collect ground truth accurately and efficiently a prototype is implemented with the benchmark. We perform the same distance estimation for four different pedestrians to evaluate the accuracy of the proposed system. The real indoor experimental results demonstrate that the proposed system performs well while tracking the test subject in a 2D scenario with low estimation error (< 2 m). The 3D evaluation of the system inside a multi-story building shows that high accuracy can be achieved for a short range of time without position update from external sources. Then we compared localization performance between our proposed system and an existing (extended Kalman filter based) system.

     

Environment Adaptive Localization Method Using Wi-Fi and Bluetooth Low Energy

Received signal strength indicator (RSSI) based fingerprinting techniques for indoor positioning can be readily implemented via a wireless access point. These methods have therefore been widely studied in the field of positioning. However, fingerprinting suffers low accuracy of positioning on account of high noise occurrences which are caused by other wireless communication signals and environmental factors when the RSSI is received, and by relatively high errors on account of low position resolution compared to other methods such as time of flight and inertial navigation technology. In this paper, a modified fingerprint algorithm based on Wi-Fi and Bluetooth low energy applied to the log-distance path loss model is proposed to remove unnecessary Wi-Fi data, and produce the AP database that can be updated depending on the changes of the ambient environment as the indoor area is increasingly complicated and extended. Instead of using the existing fingerprinting techniques of consulting signal strengths as factors that are stored in a database, the proposed algorithm employs environmental variables to which the log-distance path loss model is applied. Therefore, the proposed algorithm has higher position resolution than existing fingerprint and can improve the accuracy of positioning because of its low dependence on reference points. To minimize database and eliminate inaccurate AP signals, the Hausdorff distance algorithm and median filter are applied. Using a database in which environment variables are stored, the results are inversely transformed into the log-distance path loss model for expression as coordinates. The proposed algorithm was compared with existing fingerprinting methods. The experimental results demonstrated the reduction of positioning improvement by 0.695 m from 2.758 to 2.063 m.     

基于自适应渐消记忆的蓝牙序列匹配定位算法

针对传统指纹定位算法建库耗时长和定位精度低的问题,该文提出一种基于自适应渐消记忆的蓝牙序列匹配定位算法。首先,利用行人航迹推算(PDR)和最近邻算法(NNA)对运动序列进行位置标定和接收信号强度(RSS)映射;然后,根据邻近位置的相关性,采用序列递归搜索算法构建指纹序列数据库;最后,通过自适应渐消记忆算法,并结合初始序列匹配度实现位置估计。实验结果表明,该算法在室内环境下能够获得较低的建库时间开销以及较高的定位精度。     

Wireless fingerprinting indoor positioning using affinity propagation clustering methods

According to rapid extension of wireless sensor network localization, indoor localization using fingerprint has turned out to be more considerable lately. It contains of a database called Receive Strength Signal Indicator vectors, which is a primitive amount in wireless sensor network fingerprinting positioning. The equivalence of a few strategies is brought up from the literary works, and some new variants are presented in this study. A combination of a clustering strategy named affinity propagation and statistical and probabilistic positioning procedures is considered in this review and at the same time, the impact of some components in our methodology onto positioning precision will be investigated. Affinity propagation clustering method set up a common baseline for assessing the relative accuracy of various indoor location methods effectively. Eventually two coarse localization methods as Mahalanobis norm method and similarity to exemplar receive strength signal vector are compared based on positioning accuracy and performance. Experimental outcomes prove that the intended algorithm will advance the accuracy and localization error compared with the method without clustering.     

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.

     

基于信号分布混合假设检验的Wi-Fi室内定位方法

Wi-Fi室内定位技术是目前移动计算领域的研究热点之一,而传统位置指纹定位方法没有考虑复杂室内环境下Wi-Fi信号分布的多样性问题,从而导致Wi-Fi室内定位系统的鲁棒性较差。为了解决这一问题,该文提出一种基于信号分布混合假设检验的Wi-Fi室内定位方法。首先根据Jarque-Bera(JB)检验结果对各个参考点处的Wi-Fi信号分布进行正态性评价;然后针对不同Wi-Fi信号分布特性,利用混合Mann-Whitney U检验/T检验方法构造匹配参考点集合,以实现对目标的区域定位;最后通过计算定位区域中匹配参考点的K近邻(K-Nearest Neighbor, KNN),完成对目标的位置坐标估计。实验结果表明,所提方法相比于传统Wi-Fi室内定位方法具有更高的定位精度和更强的系统鲁棒性。     

基于阵列天线和稀疏贝叶斯学习的室内定位方法

由于多径和非同源等因素的影响,传统基于蓝牙信号强度的室内定位方法的性能精度和稳定性都不高。针对基于蓝牙信号的复杂室内环境定位问题,该文提出基于低成本阵列天线的室内定位方法,该方法利用单通道轮采极化敏感阵列天线对蓝牙信号进行采样,然后结合暗室测量获得的准确阵列流形和极化快收敛稀疏贝叶斯学习(P-FCSBL)算法实现信源的角度估计,最后通过角度实现定位。该方法充分利用极化信息和角度信息来实现目标和多径信号的分离,同时对单信源的同时采样保证了估计的稳定性。最后通过实测数据处理验证了该方法的有效性。     

A Statistical Indoor Localization Method for Supporting Location-based Access Control

Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated to accommodate to different indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. First, in an offline training phase, we fit a locally weighted regression and smoothing scatterplots (LOESS) model on the signal strength received at different training locations, and build a radio map that contains the distribution of signal strength. Then, in an online estimation phase, we determine the locations of unknown points using maximum likelihood estimation (MLE) based on the measured signal strength and the stored distribution. In addition, we provide a 95% confidence interval to our estimation using a Bootstrapping module. Compared with other approaches, our method is simpler, more systematic and more accurate. Experimental results show that the estimation error of our method is less than 2m. Hence, it can better support LBAC applications than others. This work was partially supported by US National Science Foundation (NSF) under grants CNS-0644238 and CNS-0626822. A preliminary version of the paper has appeared in QShine 2008.     

Manifold‐based canonical correlation analysis for wireless sensor network localization

Signal‐strength‐based location estimation in wireless sensor networks is to locate the physical positions of unknown sensors via the received signal strengths. In this field, there are few localization researches sufficiently exploiting topology structures of the network in both signal space and physical space. The goal of this paper is to first establish two effective localization models based on specific manifold (or local) structures of both signal space and physical (location) space by using our previous locality preserving canonical correlation analysis (LPCCA) model and a newly‐proposed locality correlation analysis (LCA) model, and then develop their corresponding novel location algorithms, called location estimation—LPCCA (LE—LPCCA) and location estimation—LCA (LE—LCA). Since both LPCCA and LCA relatively sufficiently take into account locality characteristics of the manifold structures in both the spaces, our localization algorithms developed from them consequently achieve better localization accuracy than other publicly available advanced algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

A Comparative Study of Localization Methods in Indoor Environments

A comparative study, based on three different measurements (direction of ray arrival, time difference of arrival and received signal strength), to compute the unknown position of mobile stations in indoor environments is presented in this paper. The comparison is carried out considering the results of analyses in a real building in Madrid. To overcome the problems that arise in indoor areas due to the presence of non line of sight conditions, the fingerprinting technique is applied in each of the cases. Data for computations are provided by a simulation tool based on the uniform theory of diffraction and ray-tracing techniques. This information is stored in the fingerprinting database and contains information related to every mobile station, every reference node and every access point located inside the environment under analysis. Experimental results compare the mean error when localizing several mobile stations by using the three different approaches. The goal is to obtain high precision in the localization by means of alternative methods to the received signal strength classical measurement. These techniques will be useful in critical environments where high operational security requirement are demanded.     

Time Delay Estimation Method Based on Canonical Correlation Analysis

The localization of sources has numerous applications. To find the position of sources, the relative delay between two or more received signals for the direct signal must be determined. The generalized cross-correlation method is the most popular technique; however, an approach based on eigenvalue decomposition (EVD) is another popular one that utilizes the eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in low SNR and reverberation, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) to extend the operation SNR to the lower SNR and reverberation. The proposed algorithm uses an eigenvector that corresponds to the maximum eigenvalue in the generalized eigenvalue equation (GEVD). The estimated eigenvector contains all required information for time delay estimation. We have performed simulations with uncorrelated, correlated noise and reverberation for several SNRs, to show that time delays can be more accurately estimated (especially for low SNR) a CCA based algorithm versus the adaptive EVD algorithm.     

Modeling the Effect of Human Body on TOA Based Indoor Human Tracking

In time-of-arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line of sight (NLOS) scenario and result in significant ranging error. However, the previous studies on the behavior of indoor TOA ranging did not take the effects of human body into account. In this paper, measurement of TOA ranging error has been conducted in a typical indoor environment and sources of inaccuracy in TOA-based indoor localization have been analyzed. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of transmitter–receiver, signal to noise ratio and bandwidth of the system, which clearly shows the effects of human body on TOA ranging.     

Sequential learning for fingerprint based indoor localization

In this paper, we introduce a novel approach for improving performance of fingerprinting based indoor localization. Our proposal is a two-step procedure in which severe variation in the received signal strength is minimized during the first step via convex optimization, and distance metric learning is then used to estimate a more accurate location. Numerical results show that our proposal outperforms existing techniques in terms of accuracy and reliability.     

基于特征向量的多谱线自旋回波串信号参数估计

针对存在加性高斯白噪声多参数变量的多谱线自旋回波串(Spin Echo Train,SET)信号参数估计问题,提出基于特征向量的2-D参数估计方法.将SET信号构造成2-D数据矩阵,按照不同的方式构造Hankel块矩阵束,利用子空间转移不变结构解得特征向量,依据特征向量的结构规律获得衰减因子和频率,基于最小二乘方法进一步获得信号幅度估计.该方法具有自动配对的能力,在相对高信噪比以及频率可分辨的情况下能够实现参数的有效估计,且计算复杂度较低.仿真数据结果证明了算法的有效性.     

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.     

稀疏参考点下的室内定位方法

随着移动互联网的发展,人们对于室内的位置服务需求日益增加。基于Wi-Fi的指纹库室内定位算法具有成本低、定位误差小的优点,但指纹库信号采集需要消耗大量的时间和人力,本文对稀疏参考点下构建高效指纹数据库和高精度室内定位的方法进行了深入研究。本文改进了卡尔曼滤波有效解决了Wi-Fi的噪声和缺失点,设计了基于信号强度差分方差的无线接入点筛选策略来滤除信息量较低的接入点,提出了一种基于支持向量回归拟合的克里金插值算法（Kriging Interpolation Algorithm Based On Support Vector Regression, SVR-Kriging）进行指纹库的构建,最后通过接入点加权的K加权近邻法（AP weighted and Weighted K-Nearest Neighbor, AWKNN）完成定位。将该方法应用于实际的二维、三维定位场景,实验结果表明二维场景平均定位误差为1.01 m,三维场景平均定位误差为0.92 m。该方法解决了指纹数据库信号采集困难、接入点数据冗余的问题,有效地降低了定位误差。      

A Probabilistic Approach to WLAN User Location Estimation

We estimate the location of a WLAN user based on radio signal strength measurements performed by the user's mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.