Device-free Passive Localization Research Articles

Toward Long-Term Effective and Robust Device-Free Indoor Localization via Channel State Information

With the rapid development of location-based IoT applications in recent years, indoor device-free passive localization based on Wi-Fi channel state information (CSI) has attracted considerable attention. In this article, we propose a long-term effective, robust, and accurate device-free passive fingerprinting localization scheme LTLoc, which only requires a single communication link. It takes the amplitudes extracted from the CSI along with the calibrated phases as fingerprints and trains a deep neural network (DNN) regression model to estimate the target location. Since Wi-Fi signals are susceptible to various environmental factors, CSI fingerprints also change over time, making the performance of the localization model built with the fingerprint database drop dramatically over a long period, and recalibrating the entire positioning area is laborious and time-consuming. To address this problem, we design an adaptive DNN (AdaptDNN) based on meta-networks by combining deep learning and domain adaptive methods. It can use meta-network learning to determine which layers and features of the DNN need to be transferred to automatically adapt to CSI fingerprints change. Extensive evaluations in an indoor environment with significantly different CSI fingerprints over six days have shown that LTLoc’s effectiveness in coping with changing CSI fingerprints over a long period is significantly superior to existing work in terms of localization and adaptability.

Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper.

Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper.

LTE Signal Fingerprinting Device-Free Passive Localization in Changing Environments

This paper proposes a fingerprinting-based device Free Passive localization system based on the use of the LTE signal and it is robust to environment changes. The proposed methodology uses as fingerprints descriptors calculated on the CSI vectors rather than directly CSI vectors. The paper shows the performance of the proposed methods also assuming that the monitored environment might be different from the one characterized during the training phase as some equipment may be moved. Moreover, the paper compares the proposed method with signal fingerprinting approaches based on RSSI or direct CSI vectors. Experimental results, which consider one single LTE receiver in the monitored room, show the effectiveness of the proposed solution.

Indoor device-free passive localization with DCNN for location-based services

With the increasing demand of indoor location-based services, such as tracking targets in a smart building, device-free localization technique has attracted great attentions because it can locate the targets without employing any attached devices. Due to the limited space and complexity of the indoor environment, there still exist challenges in terms of high localization accuracy and high efficiency for indoor localization. In this paper, for addressing such issues, we first convert the received signal strength (RSS) signals into image pixels. The localization problem is then formulated as an image classification problem. To well handle the variant RSS images, a deep convolutional neural network is then structured for classification. Finally, for validating the proposed scheme, two real testbeds are built in the indoor environments, including a living room and a corridor of an apartment. Experimental results show that the proposed scheme achieves good localization performance. For example, the localization accuracy can reach up to 100% in the scenario of living room and 97.6% in the corridor. Moreover, the proposed approach outperforms the methods of the K-nearest-neighbor and the support vector machines in both the noiseless and noisy environments.

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.

Redundancy reduction for indoor device-free localization

To improve localization accuracy, device-free passive localization studies usually deploy a number of sensor nodes in indoor environments, which causes redundant features and produces large data vo...

DEVICE-FREE ELECTROMAGNETIC PASSIVE LOCALIZATION WITH FREQUENCY DIVERSITY

As an emerging wireless localization technique, the electromagnetic passive localization without the need of carrying any device, named device-free passive localization (DFPL) technique has drawn considerable research attention. The DFPL technique detects shadowed links in the monitored area and realizes localization with the received signal strength (RSS) measurements of these links. However, the current RSS-based DFPL techniques have two major challenges: one is that the RSS signal is particularly sensitive to noise, and the other is that it needs a sufficient number of nodes to provide enough RSS measurements of wireless links to guarantee good performance. To overcome these problems, in this paper we take advantage of compressive sensing (CS) theory to handle the spatial sparsity of the DFPL problem for reducing the number of nodes required by DFPL systems and exploit the frequency diversity technique to deal with the problem of the RSS sensitivity. Meanwhile, inspired by the fact that the target’s movement is continuous and that the target’s current location must be around the last location, we add prior information on the support region into the sparse reconstruction process for enhancing sparse reconstruction performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.

DEVICE-FREE ELECTROMAGNETIC PASSIVE LOCALIZATION USING LINK LINE INFORMATION IN WIRELESS SENSOR NETWORKS

The electromagnetic passive localization without the need of carrying any device, named device-free passive localization (DFPL) technique, is an emerging technology for determining an uncooperative target’s position. The DFPL technique detects the shadowed links in a monitored area and realizes localization with the received signal strength (RSS) measurements of these links. However, most current RSS-based DFPL schemes belong to the model-based DFPL method, since the localization accuracy depends on the shadowing model. Moreover, model-based DFPL methods require high memory and computing resources for accurate tracking performance, and thus may not be suitable for resourceconstrained applications. To overcome these problems, in this paper we propose a lightweight DFPL method which makes use of recent link lines detected by wireless sensor networks to estimate the target’s location. This method can be independent of the shadowing model and can also reduce the algorithm’s storage and computational resource requirements. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance and complexity.

Enhancing the Performance of Indoor Device-Free Passive Localization

Device-free passive localization (DFPL) has been an emerging application with fast increasing development. Channel State Information- (CSI-) based DFPL is recently paid more attention to for fine-granularity and stability of CSI. However, lots of dead spots exist in the area of interest. And the accuracy of localization is not still completely satisfactory, especially for outside of the first Fresnel zone. In our paper, we put forward a new metric to estimate the sensitivity of a receiver to changes in the detecting area. In our experiment, we observe that the performance of DFPL can be raised when the receiver is placed at the location with high receiver sensitivity. Hence, we develop a new high-performance indoor device-free passive localization (HiDFPL), which employs a Bayesian a posteriori approach and possesses high receiver sensitivity. The experiment results demonstrate the outstanding performance of the proposed scheme.

An Adaptive Framework Coping with Dynamic Target Speed for Device-Free Passive Localization

The problem of device-free passive localization aims on locating moving objects which do not carry any localization devices. The rationale of this problem is based on the fact that a moving object can result in the changes of received signal strength (RSS) of the wireless links. Existing studies on this problem usually do not consider the impact of dynamic target speed on device-free passive localization. However, the experiments show that the localization performance degrades substantially when an object is moving at dynamic speed. To meet this challenge, in this paper, we propose an adaptive device-free passive localization framework which has three components to detect target speed change and perform adaptive localization. This framework can be easily adapted for existing device-free localization methods which are based on the detection of signal strength changes. As demonstrated in the experiments, the proposed framework can lead to 50 and 30 percent improvement on median and maximum error respectively over the localization algorithms without considering dynamic moving speeds of the target.

Signal Eigenvector-Based Device-Free Passive Localization Using Array Sensor

Device-free passive (DFP) localization techniques have received increasing attention for location-based services due to their ability to realize localization without holding any wireless device. Most of the existing DFP localization systems are based on the measurement of received signal strength (RSS) only. However, the localization accuracy is easily affected by the spatial and temporal variance of RSS due to multipath fading and noise, even in a static environment. In this paper, we propose a novel localization system for DFP localization using an array sensor, which uses an antenna array at a receiver and is mainly based on the signal eigenvector. We use a fingerprinting technique with multiclass support vector machines (SVMs) based on a combination of array signal features with spatial and temporal averaging. We evaluate the localization performance of our proposed system in different propagation environments, i.e., line-of-sight (LOS) and non-line-of-sight (NLOS). In addition, we analyze two types of receive antenna placement, i.e., centralized and distributed antennas. The experimental results show that the localization accuracy can be improved by the proposed system, particularly in the centralized antenna case. Moreover, they show that the proposed system can improve localization accuracy compared with the conventional RSS-only-based system.

Ichnaea: A Low-Overhead Robust WLAN Device-Free Passive Localization System

WLAN Device-free passive (DfP) indoor localization is an emerging technology enabling the localization of entities that do not carry any devices nor participate actively in the localization process using the already installed wireless infrastructure. Current state-of-the-art DfP localization systems require a large overhead to construct an RF profile for the environment, that is then used as a reference for either motion detection or tracking. These profiles are also not robust to changes in the environment, requiring frequent manual maintenance or reconstruction. In this paper, we present the design, implementation and evaluation of Ichnaea, an accurate, robust, and low-overhead DfP localization system. Ichnaea uses a lightweight, typically two minutes, training period to learn the silence profile of the environment. It then applies statistical anomaly detection techniques and particle filtering, while adapting to changes in the environment, to provide its localization capabilities using standard WiFi hardware. Evaluation of Ichnaea in three typical testbeds with a side-by-side comparison to the state-of-the-art WLAN DfP systems shows that it can achieve a worst case median distance error of 2.5 m while requiring significantly lower deployment overhead and being robust to environment changes.

ROBUST SPARSITY-BASED DEVICE-FREE PASSIVE LOCALIZATION IN WIRELESS NETWORKS

As an emerging technique with a promising application prospect, the device-free passive localization (DFPL) technique has drawn considerable research efiorts due to its ability of realizing wireless localization without the need of carrying any device and participating actively in the localization process. Recent technological achievements of the DFPL technique have made it feasible to realize location estimation using the received signal strength (RSS) information of wireless links. However, one major disadvantage of the RSS-based DFPL technique is that the RSS measurement is too sensitive to noise and environmental variations, which incur the misjudgment of shadowed links and degradation of localization performance. Based on the natural sparsity of location flnding in the spatial domain, this paper proposes an environmental-adaptive sparsity-based localization method for the DFPL problem in the existence of model mismatch. The novel feature of this method is to adjust both the overcomplete basis (a.k.a. dictionary) and the sparse solution using a dictionary learning (DL) technique based on the quadratic programming approach so that the location solution can better match the changes of the RSS measurements between the node pairs to the spatial location of the target. Moreover, we propose a modifled re-weighting l1 norm minimization algorithm to improve reconstruction performance for sparse signals. The efiectiveness of the proposed scheme is demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.

Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments

The widespread usage of WLANs and mobile devices has fostered the interest in localization systems for wireless environments. The majority of research in the context of wireless-based localization systems has focused on device-based active localization, in which devices are attached to tracked entities. Recently, device-free passive localization (DfP) has been proposed where the tracked entity is neither required to carry devices nor to participate actively in the localization process. Previous studies have focused on small areas and/or controlled environments. In this paper, we present the design, implementation, and analysis of Nuzzer, a large-scale DfP localization system, which tracks entities in real environments, rich in multipath. We first present probabilistic techniques for DfP localization of a single entity and evaluate their performance both analytically and in typical office buildings. Our results show that Nuzzer gives location estimates with less than 2-meters median distance error. We then give an algorithm for estimating the number of entities in an area of interest and localizing them into coarse-grained zones to enhance the scalability of the system. This indicates the suitability of Nuzzer to a large number of application domains.

Support Vector Machine and Probability Neural Networks in a Device-Free Passive Localization (DFPL) Scenario

Abstract The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP), and a Probabilistic Neural Network (PNN) in order to detect movement based on changes in the wireless signal strength.