Wireless Local Area Network Localization Research Articles

An Information-Theoretic View of WLAN Localization Error Bound in GPS-Denied Environment

This paper uses an information-theoretic lens to view the error bound of wireless local area network (WLAN) localization, which is recognized as one of the superior candidate localization techniques in the GPS-denied environment. Interestingly, we analogize the process of WLAN localization into one of information propagation in a parallel Gaussian noisy channel, and then derive the corresponding localization error bound from the channel capacity of the analogical information propagation system. Experimental results show that compared with the widely-known Cramer-Rao Lower Bound (CRLB), the proposed approach performs better in localization error bound estimation under most of the cases of different access point deployment and reference point calibration.

Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering

Applications on Location Based Services (LBSs) have driven the increasing demand for indoor localization technology. The conventional location fingerprinting based localization involves heavy time and labor cost for database construction, while the well‐known Simultaneous Localization and Mapping (SLAM) technique requires assistant motion sensors as well as complicated data fusion algorithms. To solve the above problems, a new pedestrian motion learning based indoor Wireless Local Area Network (WLAN) localization approach is proposed in this paper to achieve satisfactory LBS without the demand for location calibration or motion sensors. First of all, the concept of pedestrian motion learning is adopted to construct users’ motion paths in the target environment. Second, based on the timestamp relation of the collected Received Signal Strength (RSS) sequences, the RSS segments are constructed to obtain the signal clusters with the newly defined high‐dimensional linear distance. Third, the PageRank algorithm is performed to establish the hotspot mapping relations between the physical and signal spaces which are then used to localize the target. Finally, the experimental results show that the proposed approach can effectively estimate the target’s locations and analyze users’ motion preference in indoor environment.

GrassMA: Graph-Based Semi-Supervised Manifold Alignment for Indoor WLAN Localization

Wireless local area network (WLAN) fingerprinting has been extensively studied for indoor localization due to the formidable deployability of WLAN in indoor environment, but one major bottleneck of its practical implementation is the extensive calibration effort required to construct the radio map through the fingerprinting which is time consuming and labor intensive. In response to this compelling problem, we newly design the graph-based semi-supervised manifold alignment approach which relies on the concept of graph construction to construct a cost-efficient radio map with a small number of labeled fingerprints. In addition, the unlabeled user traces are also considered to compensate for the sparsity of the raw radio map as well as enhance its robustness to the environmental change. The extensive experiments conducted in an actual indoor WLAN environment demonstrate the performance improvement (by 27.48% at most with respect to the confidence probability of errors within 3 m) by our system compared with the existing ones using the fingerprints solely.

A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

An Information-Based Approach to Precision Analysis of Indoor WLAN Localization Using Location Fingerprint

In this paper, we proposed a novel information-based approach to precision analysis of indoor wireless local area network (WLAN) localization using location fingerprint. First of all, by using the Fisher information matrix (FIM), we derive the fundamental limit of WLAN fingerprint-based localization precision considering different signal distributions in characterizing the variation of received signal strengths (RSSs) in the target environment. After that, we explore the relationship between the localization precision and access point (AP) placement, which can provide valuable suggestions for the design of the highly-precise localization system. Second, we adopt the heuristic simulated annealing (SA) algorithm to optimize the AP locations for the sake of approaching the fundamental limit of localization precision. Finally, the extensive simulations and experiments are conducted in both regular line-of-sight (LOS) and irregular non-line-of-sight (NLOS) environments to demonstrate that the proposed approach can not only effectively improve the WLAN fingerprint-based localization precision, but also reduce the time overhead.

Radio map updated method based on subscriber locations in indoor WLAN localization

With the rapid development of wireless local area network(WLAN) technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model(HMM), is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm,the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.

利用最大散度差于室内无线网络定位分析

在本研究中将最大散度差应用在无线网络室内定位上，最大散度差主要是可以解决线性鉴别分析在计算类内散度矩阵时所遭遇的奇异性矩阵问题，它是利用类间散度矩阵与类内散度矩阵之差作为目标函数，由于不需要构造类内散度矩阵的反矩阵，故可以避免掉奇异性矩阵的问题。本研究中其定位的方式是透过指纹特征比对法的概念来进行定位，共分为两个阶段：在离线阶段，利用最大散度差将原始讯号数据库作前置处理；在在线阶段，将接收到的实时讯号数据利用最大似然法计算出目前接收讯号的所在位置。将最大散度差应用于室内无线网络进行定位，不仅可以避免奇异性矩阵的问题并且还可以降低定位时的计算量，从实验结果得知此应用是可以实行的。 In this paper, we utilize Maximum Scatter Difference (MSD) for indoor localization over Wireless Local Area Network (WLAN) received signal data. Maximum Scatter Difference can solve the singular matrix problems by linear discriminant analysis calculates the inverse matrix of within-class scatter matrix. The objective functions of Maximum Scatter Difference are to maximize the inter-class scatter matrix and minimize the within-class scatter matrix. Therefore, Maximum Scatter Difference cannot need to construct the inverse matrix of within-class scatter matrix. It can avoid the singular matrix problems. We utilize location fingerprinting approaches for indoor localization. The approaches are divided into two parts including off-line and on-line stages. In the off-line stage, we collected received signal data are projected onto the feature space of Maximum Scatter Difference. In the on-line stage, we receive real-time signal data compared with the off-line stage that was collected data using Maximum Likelihood (ML) to estimate current location. Maximum Scatter Difference applied to indoor WLAN localization that not only avoids the singular matrix but also reduces the localization computation. From Simulation results show that this algorithm can be implemented.

Access Point Height Based Location Accuracy Characterization in LOS and OLOS Scenarios

Location estimation in a wireless local area network (WLAN) using received signal strength indication (RSSI) has gained considerable attention in recent years. In a conventional RSSI based indoor WLAN localization, mobile node position is estimated through access point (AP) placed at ceiling height. Researchers have proposed solutions for location estimation in line of sight (LOS) scenarios, by installing the AP at a fixed position. This paper demonstrates the improved location accuracy in LOS and obstructed line of sight (OLOS) scenarios by placing the AP at lower heights. The RSSI variations caused by shadow fading for changing AP heights are used to estimate the location accuracy. The localization performance is computed in terms of Cramer-Rao lower bound (CRLB) of range estimate under dynamic environments which is relatively less complex computation technique and is calibration free. Simulation results reveal that the proposed method has better performance than the multilateration with linearization for access point localization algorithm. The minimum mean localization errors are obtained by deploying the access point at 2 m height. The results also demonstrate that the indoor localization accuracy improves for higher order path loss exponent.