WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

WiFi-SLAM Using G aussian Process Latent Variable Models

The Internet of Things (IoT) has gradually changed the way of people’s lives due to its ability of connecting everything together, and meanwhile the accurate location sensing plays a crucial role in achieving this goal Up to now, as one of the most representative outdoor localization systems, the global positioning system has been widely used, but its performance may be dramatically declined in indoor environment due to the serious multipath effect and signal attenuation caused by the complicated indoor structure At the same time, the location fingerprint-based localization approach has become a popular one in indoor environment, and meanwhile the corresponding calibrated signal simplification in location database construction has been primarily considered due to its significant practical meaning in avoiding the blind signal sampling In this paper, we propose to use an information-theoretic lens to construct the energy-efficient location fingerprint database for the localization in IoT Interestingly, by analyzing the information loss in signal sampling, we analogize the database construction process into the information propagation process in a lossy channel, and then formulate the relations of sample capacity and localization error from an information-theoretic view After that, by selecting an appropriate time interval to sample the independent and nonredundant signal, the minimum number of sampled signal under the given expected localization accuracy is determined Finally, the extensive experimental results show that compared with the state-of-the-art approaches, the proposed one can effectively simplify the calibrated data for the energy-efficient location database construction in different wireless localization networks

Calibrated Data Simplification for Energy-Efficient Location Sensing in Internet of Things

WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.

https://www.mdpi.com/1424-8220/19/10/2300/pdf

An Improved WiFi Positioning Method Based on Fingerprint Clustering and Signal Weighted Euclidean Distance

A radio map is a collection of signal fingerprints labeled with their collected locations. It is known that the performance of a fingerprint-based positioning systems is closely related to the precision and accuracy of the underlying radio maps. However, little has been studied on the performance of radio maps in relation to the fingerprint collection methods and the radio map models, which determine the accuracy and precision of radio maps, respectively. This paper evaluates the performance of various radio map construction methods in both indoor and outdoor environments. Four radio map construction methods, i.e., a point-by-point manual calibration, a walking survey, a semisupervised learning-based method, and an unsupervised learning-based method, have been compared. We also evaluate the performance of various types of radio map models that represent the characteristics of collected fingerprints. To demonstrate the importance of the radio map model, a new model named signal fluctuation matrix (SFM) was developed, and its performance was compared with that of the three conventional radio map models, respectively. The evaluation revealed that the performance of the radio maps was very sensitive to the design of radio map models and the number of fingerprints collected at each location. The performance achieved by SFM-based positioning was comparable with that of the other models despite using a small number of fingerprints.

Performance Evaluation of Radio Map Construction Methods for Wi-Fi Positioning Systems

Fingerprint crowdsourcing has recently been promoted as a promising solution for fingerprinting-based indoor localization systems to relieve the burden of site survey. When constructing an indoor localization map from crowd-sourced samples, the following challenges should be addressed: inaccurate sample annotation, unequal sample dimensionality, measurement device diversity, and nonuniform spatial distribution. In this paper, we propose the radio map construction from crowd-sourced sample (RMapCS) scheme to handle these challenges. The RMapCS consists of four main modules: outlier detection, source selection, fingerprint interpolation, and device calibration. For each device in each grid, we first propose an improved clustering algorithm to remove outliers and use a threshold-based approach to select only those important signal sources. For a grid without enough samples, we propose a fingerprint interpolation algorithm to construct its device-specific fingerprints. Then, we propose a device calibration algorithm to fuse samples from different devices to obtain grid fingerprints. We also propose a two-step online positioning algorithm consisting of both set comparison and similarity computation. We conduct field measurements and experiments to examine the localization performance. Results show that the proposed RMapCS can achieve significant improvements over the peer schemes and the average localization error can achieve around 1.5 m by using only the received signal strength-based fingerprints.

RMapCS: Radio Map Construction From Crowdsourced Samples for Indoor Localization

Wireless Local Area Network (WLAN) location fingerprinting has become a prevalent approach to indoor localization. However, its widespread adoption has been hindered by the need for manual efforts to collect location-labeled fingerprints for the calibration of a localization model. Several semi-supervised learning methods have been applied to reduce such manual efforts by exploiting unlabeled fingerprints, but they still require some amount of labeled fingerprints for initializing the learning process. In this research, in order to obviate the need for location labels or references, we propose a novel unsupervised learning method that calibrates a localization model using unlabeled fingerprints based on a hybrid global-local optimization scheme. The method determines the optimal placement of fingerprint sequences on an indoor map, under the constraint imposed by the inner structure shown on the map such as walls and partitions. An efficient interaction between a global and a local optimization in the hybrid scheme drastically reduces the complexity of the learning task. Experiments carried out in a single- and a multi-story building revealed that the proposed method could successfully build a precise localization model without any location reference or explicit efforts to collect labeled samples.

Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks

Particle filter and Viterbi tracking algorithm are two representative sensor fusion methods to estimate a location of a mobile device by using various signals such as Wi-Fi, Bluetooth, magnetic fields and sensors like 3-axis accelerometer and gyroscope in indoor environments. This paper measures and compares the performance of the two sensor fusion methods on two paths in the first floor of KI building, KAIST. One path is a random walk and another is a rectangle path circumventing the area. On the rectangle path, the two methods showed a similar performance to each other, approximately 2m average error distance. On the other hand, on the random walk path, the Viterbi tracking algorithm significantly outperformed the particle filter in accuracy. This indicates that the choice of sensor fusion method influences a little to positioning accuracy when pedestrian dead reckoning (PDR) is working properly. However, when the PDR is not properly working, the choice of sensor fusion method greatly influences to positioning accuracy. In our test, the Viterbi tracking algorithm revealed to be much more resilient to PDR errors than particle filters.

Byung-chul Moon

Papers

Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks

Comparative study of sensor fusion methods for hybrid positioning systems