Device-free Wireless Localization Research Articles

A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification

Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

Radio Tomographic Imaging Based On Quartile Outliers Filter and PCA

Device-free wireless localization (DFL) that does not need the target equipped with any device is a new technique which could estimate the location by analyzing its shadowing effect on surrounding wireless links. This technique neither requires the target to be equipped with any device nor involves privacy concerns, which makes it an attractive and promising technique for many emerging smart applications. Therefore, how to characterize the influence of human behaviors is the key question. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current radio tomographic imaging (RTI) system. This paper proposes a device-free passive localization algorithm based on Quartile Outliers Filter (QOF) and Principal Component Analysis (PCA). In this work, we explore and exploit lower quartile and upper quartile of the original RSSI to identify and remove the outliers caused by the multipath effect or hardware facilities, and apply PCA to extract the most useful features. Extensive experiments performed in a clutter indoor laboratory and a building courtyard with 20 wireless nodes demonstrate that the outstanding performance of the proposed scheme.

Device-Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Currently, localization has been one of the research hot spots in Wireless Sensors Networks (WSNs). However, most localization methods focus on the device-based localization, which locates targets with terminal devices. This is not suitable for the application scenarios like the elder monitoring, life detection, and so on. In this paper, we propose a device-free wireless localization system using Artificial Neural Networks (ANNs). The system consists of two phases. In the off-line training phase, Received Signal Strength (RSS) difference matrices between the RSS matrices collected when the monitoring area is vacant and with a professional in the area are calculated. Some RSS difference values in the RSS difference matrices are selected. The RSS difference values and corresponding matrix indices are taken as the inputs of an ANN model and the known location coordinates are its outputs. Then a nonlinear function between the inputs and outputs can be approximated through training the ANN model. In the on-line localization phase, when a target is in the monitoring area, the RSS difference values and their matrix indices can be obtained and input into the trained ANN model, and then the localization coordinates can be computed. We verify the proposed device-free localization system with a WSN platform. The experimental results show that our proposed device-free wireless localization system is able to achieve a comparable localization performance without any terminal device.

CSI-Based Device-Free Wireless Localization and Activity Recognition Using Radio Image Features

Device-free wireless localization and activity recognition is an emerging technique, which could estimate the location and activity of a person without equipping him/her with any device. It deduces the state of a person by analyzing his/her influence on surrounding wireless signals. Therefore, how to characterize the influence of human behaviors is the key question. In this paper, we explore and exploit a radio image processing approach to better characterize the influence of human behaviors on Wi-Fi signals. Traditional methods deal with channel state information (CSI) measurements on each channel independently. However, CSI measurements on different channels are correlated, and thus lots of useful information involved with channel correlation may be lost. This motivates us to look on CSI measurements from multiple channels as a radio image and deal with it from the two-dimensional perspective. Specifically, we transform CSI measurements from multiple channels into a radio image, extract color and texture features from the radio image, adopt a deep learning network to learn optimized deep features from image features, and estimate the location and activity of a person using a machine learning approach. Benefits from the informative and discriminative deep image features and experimental results in two clutter laboratories confirm the excellent performance of the proposed system.

Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach

Device-free wireless localization and activity recognition (DFLAR) is a new technique, which could estimate the location and activity of a target by analyzing its shadowing effect on surrounding wireless links. This technique neither requires the target to be equipped with any device nor involves privacy concerns, which makes it an attractive and promising technique for many emerging smart applications. The key question of DFLAR is how to characterize the influence of the target on wireless signals. Existing work generally utilizes statistical features extracted from wireless signals, such as mean and variance in the time domain and energy as well as entropy in the frequency domain, to characterize the influence of the target. However, a feature suitable for distinguishing some activities or gestures may perform poorly when it is used to recognize other activities or gestures. Therefore, one has to manually design handcraft features for a specific application. Inspired by its excellent performance in extracting universal and discriminative features, in this paper, we propose a deep learning approach for realizing DFLAR. Specifically, we design a sparse autoencoder network to automatically learn discriminative features from the wireless signals and merge the learned features into a softmax-regression-based machine learning framework to realize location, activity, and gesture recognition simultaneously. Extensive experiments performed in a clutter indoor laboratory and an apartment with eight wireless nodes demonstrate that the DFLAR system using the learned features could achieve 0.85 or higher accuracy, which is better than the systems utilizing traditional handcraft features.

Toward Accurate Device-Free Wireless Localization With a Saddle Surface Model

Device-free wireless localization (DFL) is a technique that can locate a target by analyzing its shadowing effect on wireless links, which causes the variation of link measurements, while removing the requirement of equipping the target with a device. It can provide fundamental data for pervasive computing, smart environment, and traffic surveillance applications. The observation model, which represents the relationship between wireless link measurement and target location, is very important for DFL, since it characterizes the shadowing effect of the target on wireless links and, therefore, determines the performance of the DFL system. In this paper, inspired by measurement results, we propose a saddle surface (SaS) model to describe the shadowing effect. The SaS model characterizes the elaborate information within the spatial impact area and provides more useful observation information for the location estimation algorithm. We incorporate the SaS model into the particle filter framework for location estimation. Extensive experiments in indoor and outdoor scenarios are carried out to evaluate the performance of the proposed schemes. The tracking errors of 0.78 and 0.21 m in the given two scenarios demonstrate the better performance of the proposed SaS model compared with existing models.

Time and Energy Efficient TOF-Based Device-Free Wireless Localization

Device-free wireless localization (DFL) is a promising technique. It can localize and track a target without carrying any electronic device. Compared with traditional received signal strength (RSS)-based DFL, the recently proposed time-of-flight (TOF)-based DFL technique could achieve better performance. However, TOF-based DFL requires that each pair of nodes should perform a pairwise TOF measurement sequentially, which makes it impractical for being utilized in time and energy sensitive applications. Inspired by the fact that a target shadows only a small subset of wireless links, which travel through its spatial impact area, we incorporate link state estimation function into particle filter (PF) framework to predict shadowed links and design a parallel scanning scheme to scan multiple shadowed links simultaneously. The aforementioned methods guarantee that the effective TOF measurements can be acquired timely and energy efficiently. Meanwhile, to achieve reasonable localization performance with these reduced-under-sampled link measurements, we utilize compressive sensing (CS) algorithm to reconstruct the shadowing effect map (SEM). With the map as observation likelihood function, PF algorithm could estimate targets location accurately. Experimental results with an 802.15.4a chirp spread spectrum ranging hardware testbed show that the average running time and energy consumption reduce remarkably.

Lightweight Robust Device-Free Localization in Wireless Networks

Due to its ability of realizing localization without the need of equipping the target with a wireless device, the device-free wireless localization technique has become a crucial technique for many security and military applications. However, there still lacks an efficient scheme which could achieve robust location estimation performance with low computational cost. To solve this problem, we propose a lightweight robust Bayesian grid approach (BGA) in this paper. The BGA utilizes not only the observation information of the shadowed links, but also the prior information involved in the previous estimations and the constraint information involved in the non-shadowed links, which ensure its robust performance. Meanwhile, the BGA can be carried out with a series of lightweight grid multiplication and addition operations, which eliminates the complex matrix inversion computation involved in the traditional algorithm. The experimental results demonstrate that BGA could achieve a mean tracking error of 0.155 m with a running time of only 1.5 ms.