WLAN Indoor Localization Research Articles

Noise reduction for radio map crowdsourcing building in WLAN indoor localization system

Recent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost-effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor localization system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduce the noise in the location space of the radio map, the short-term Fourier transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

Linear Regression Algorithm against Device Diversity for the WLAN Indoor Localization System

Recent years have witnessed a growing interest in using WLAN fingerprint‐based methods for the indoor localization system because of their cost‐effectiveness and availability compared to other localization systems. In this system, the received signal strength (RSS) values are measured as the fingerprint from the access points (AP) at each reference point (RP) in the offline phase. However, signal strength variations across diverse devices become a major problem in this system, especially in the crowdsourcing‐based localization system. In this paper, the device diversity problem and the adverse effects caused by this problem are analyzed firstly. Then, the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression (LR) algorithm. Based on the analysis, the LR algorithm is proposed to create a unique radio map in the offline phase and precisely estimate the user’s location in the online phase. After applying the LR algorithm in the crowdsourcing systems, the device diversity problem is solved effectively. Finally, we verify the LR algorithm using the theoretical study of the probability of error detection. Experimental results in a typical office building show that the proposed method results in a higher reliability and localization accuracy.

Anonymous crowdsourcing-based WLAN indoor localization

In order to solve the problem of location privacy under big data and improve the user positioning experience, a new concept of anonymous crowdsourcing-based WLAN indoor localization is proposed by employing the Micro-Electro-Mechanical System (MEMS) motion sensors as well as WLAN module in off-the-shelf smartphones. First of all, the crowdsourced motion traces with similar Received Signal Strength(RSS) sequences are assembled into a motion graph. Second, the mobility map is constructed according to traces segmentation and clustering. Third, the pixel template matching is adopted to physically label the pre-constructed mobility map. Finally, the robust Extended Kalman Filter (EKF) is designed to perform localization by matching the newly-collected RSS measurements against the mobility map. The extensive experimental results show that the proposed approach is capable of constructing a physically-labeled mobility map from the sporadically-collected crowdsourced motion traces as well as achieving satisfactory localization accuracy in a cost-efficient manner.

Optimization of Fingerprints Reporting Strategy for WLAN Indoor Localization

This paper investigates how to optimize the fingerprints reporting strategy to improve localization accuracy, and how the optimal strategy theory can be utilized to streamline the design of WLAN fingerprinting localization systems. In particular, we first reveal that the fingerprints reporting problem is essentially an NP-Hard size-constrained supermodular maximization problem, and then show the inapplicability of the state-of-the-art approximation algorithms to the problem. We then propose a new algorithm and show that if the number of fingerprints measurements is large enough, then the localization accuracy is at most $1-\varepsilon$ times worse than the optimal value, with $\varepsilon$ any given constant close to 0. Moreover, we demonstrate how the optimal strategy theory can be utilized to improve accuracy of location estimation by resolving the issue of similar fingerprints for both faraway and close-by locations, with an iterative algorithm developed to cross check fingerprints sampled in different locations, in order to derive the best possible result of localization. Further, we reveal the relationship between accuracy of location estimation and coverage of Wi-Fi signals in indoor spaces when planning deployment of APs. Experiment results are presented to validate our theoretical analysis.

WLAN 실내 위치인식 시스템에서 효율적인 RP의 분배방법과 활용법에 관한 연구

WLAN 실내 위치인식 시스템에서 효율적인 RP의 분배방법과 활용법에 관한 연구

WLAN indoor localization method using angle estimation

In WLAN indoor localization systems, an improved position fingerprinting algorithm is proposed to obtain higher accuracy. The algorithm constructs the nonlinear relationship between received signal strength indication (RSSI) values and the angles formed by horizontal line and the line from transmitters to receivers, instead of traditionally training the relationship between RSSI values and physical coordinates. The localization area is divided into a number of small rectangular areas, and the test points are sorted out by K-Nearest-Neighbor (KNN) algorithm. In a small rectangular area, RSSI values and the angles are trained by support vector machine (SVM), so as to estimate the angles formed by horizontal line and the line from test points to each access point (AP). Finally, coordinates of the test points are estimated using the geometric relationship. Two experimental sections have been conducted under different conditions: one is in the laboratory, and the other in a typical office space. The proposed algorithm is compared with v-SVM algorithm, KNN algorithm and ML algorithm. Experimental results prove that our proposed algorithm outperforms other methods in term of localization accuracy under various situations.

Improve the Robustness of Indoor Localization Using DRSS

In this paper, we present a fingerprint positioning technique based on differential received signal strength (DRSS).By using DRSS fingerprints, the problem of degraded accuracy caused by heterogeneous devices in indoor WLAN localization. The robustness of DRSS fingerprints is proved, both theoretically and practically, to be better than that of RSS, thus the applicability as well as accuracy is improved in a WLAN indoor localization system with various devices.

변형된 데이터베이스와 선택적 연산을 이용한 WLAN 실내위치인식 알고리즘

Abstract : Recently, the Fingerprint, which is one of the methods of indoor localization using WLAN, has been many studied owing to robustness about ranging error by the diffraction and refraction of radio waves. However, in the signal gathering process and comparison operation for the measured signals with the database, this method requires time consumption and computational complexity. In order to compensate for these prob-lems, this paper presents, based on proposed modified database, WLAN indoor localization algorithm using se-lective operation of collected signal in real time. The proposed algorithm reduces the configuration time and the size of the data in the database through linear interpolation and thresholding according to the signal strength, the localization accuracy, while reducing the computational complexity, is maintained through se-lective operation of the signals which are measured in real time. The experimental results show that the accu-racy of localization is improved to 17.8% and the computational complexity reduced to 46% compared to conventional Fingerprint in the corridor by using proposed algorithm.

AP Selection Algorithm in WLAN Indoor Localization

AP Selection Algorithm in WLAN Indoor Localization

WILOCSIM: SIMULATION TESTBED FOR WLAN LOCATION FINGERPRINTING SYSTEMS

This paper introduces a novel simulation testbed for investigating WLAN indoor localization systems. This testbed referred to as WiLocSim consists of a novel beacon received signal strength (RSS) simulator which provides realistic modeling of beacon signal characteristics such as multipath propagation, measurement noise and body loss. Each component of the simulator is individually modeled and verifled prior to integration. In addition, the capabilities of the testbed are demonstrated using two variants of the nearest neighbor classiflcation based indoor localization algorithm. Unlike conventional measurement based performance evaluation, the proposed testbed provides a reproducible environment for accurate evaluation and analysis of indoor localization systems. More importantly, it signiflcantly reduces the high labor cost typically required in measurement based testbed.

A New WLAN Indoor Localization System Based on Distance-loss Model with Area Partition

摘要: 该文基于室内RSS模型,结合定位空间的区域划分,构建了一个室内用户定位系统;在各个区域内,将在已知位置的信息收集点采集到的各无线接入点信号强度,与计算机模拟实际环境获得的信号强度值相比较,以此设定RSS模型参数;然后采用最大似然法估计用户位置。实际系统测定结果表明,该方法可以获得较为精确的室内用户位置估计。 关键词: 室内定位系统; 区域划分; 距离-损耗模型; 最大似然估计