Device-free Localization Method Research Articles

Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

With the widespread use of WiFi devices and the availability of channel state information (CSI), CSI-based device-free localization (DFL) has attracted lots of attention. Fingerprint-based localization methods are the primary solutions for DFL, but they are faced with the fingerprint similarity problem due to the complex environment and low bandwidth of the commercial WiFi. Meanwhile, fingerprints may change unpredictably due to multipath WiFi signal propagation in time-varying environments. To tackle these problems, this paper proposes an adaptive online learning DFL method, which adaptively updates the localization model to ensure long-term accuracy and adaptability. Specifically, the CSI signals of the target located at different reference points are first collected and transformed to discriminable fingerprints using the weights of Multilayer Online Sequence Extreme Learning Machine (ML-OSELM). After that, an online learning DFL model is built to adapt to the changes of the environment. Experimental results in a time-varying indoor environment validate the adaptability of the proposed method against environmental changes and show that our method can achieve 10% improvement over other methods.

A new device-free localization method for RSS data with considering correlations

Device-free localization (DFL) is an emerging intelligent sensing technique with challenging issues due to the highly correlated data. Localization performance has been demonstrated from algorithmic as well as modelling perspectives, for example, by including various sparsity constraints. The general sparse regularization method simultaneously estimates parameters and selects features, but its performance relies heavily on the correlations among features, which leads to generalization errors. Our goal in this study is to formulate a DFL method based on independently interpretable sparse coding to improve the accuracy and efficiency of localization. The scheme uses a novel correlation constraint of sensing matrix columns to handle the high correlation of the received signal strength (RSS) data, overcoming not only the fluctuation of RSS due to environmental changes but also avoiding overfitting through identification of informative variables that provide interpretable results. Meanwhile, the minimax concave penalty (MCP) is proposed as a sparse regularizer to obtain strong sparsity and an exact estimate. We propose using the difference of convex functions algorithm (DCA) and non-convex proximal splitting to address the non-convexity problem. Extensive experiments were performed in a cluttered indoor laboratory and open outdoor environment to verify the efficacy of the proposed method.

Geometric Midpoint Algorithm for Device-Free Localization in Low-Density Wireless Sensor Networks

Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%.

Enhancing PIR-Based Multi-Person Localization Through Combining Deep Learning With Domain Knowledge

Device-free localization (DFL) techniques based on pyroelectric infrared (PIR) sensors have attracted much attention due to their advantages of low cost, low power consumption, and privacy protection. However, existing PIR-based DFL methods still have a practical limitation that they usually require a high deployment density in multi-person scenarios. In this article, we propose a new PIR-based DFL method which requires a significantly lower deployment density in the multi-person scenario. The proposed method is based on deep learning. Especially, instead of directly utilizing the commonly used neural network architectures, we proposed a task-specific network architecture that integrates the domain knowledge of PIR sensors to improve its localization performance. Specifically, following the idea of modular learning, we design the proposed network architecture to contain two modules for counting the number of existing persons and another is for predicting their locations, respectively. Moreover, we also design the modules for person counting and localization to contain two secondary modules for different sub-tasks. Besides, to further improve the localization performance, we also propose two data augmentation strategies that aim at enhancing the training data diversity from the aspects of the moving speed of existing persons and the influence of surrounding noise. Through the proposed method, we succeed to remarkably reduce the deployment density of the traditional PIR-based methods by about 76%, while maintaining the localization accuracy.

PRSRTI: A Novel Device-Free Localization Method Using Phase Response Shift Based Radio Tomography Imaging

Device-free localization (DFL) is a hot topic in passive sensing such as intrusion detection, pose estimation, and contact-tracing. To improve the performance, we propose a novel fine-grained DFL method using the phase response shift (PRS) to localize targets, which can mitigate the effect of multipath, and noise without phase ambiguity. The phases of received multi-frequency signals are linearly fitted as the phase response (PR). The shift of target-induced PR with respect to non-target PR is considered as the PRS. The PRSs at every location of the monitored area are observed with different length communication links, and are summarized as a PRS weight model. The PRS-based radio tomography imaging (PRSRTI) localization model is derived, and utilizes the PRS, and the weight model to localize targets. Experimental results validate that its performance is obviously higher than the received signal strength based radio tomography imaging (RTI).

Multi-targets device-free localization based on sparse coding in smart city

With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city.

Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity.

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.

A two-step sequential method for device-free localization using wireless sensor networks

The device-free localization that locates an uncooperative target by analyzing its shadowing effect on wireless links in low-cost wireless sensor networks has become an attractive technique. Due to its ability of realizing localization without the need of equipping the target with any wireless device, device-free localization has lots of potential applications ranging from emergency rescue to patient monitoring. However, since the change of the received signal strength on links easily becomes unpredictable due to all kinds of dynamical factors, device-free localization is still a challenging problem. In this article, we propose a novel two-step sequential device-free localization method to enhance device-free localization performance. The sequential device-free localization first utilizes the geometric-based method to quickly obtain the coarse positioning result with low storage and computational requirements, and then exploits the location information of selected wireless nodes to improve the accuracy of the geometric-based localization result based on the optimization-based method, which ensures its robust performance. Experimental results in indoor and outdoor scenarios demonstrate the effectiveness and robustness of the proposed scheme.

An Accurate Geometrical Multi-Target Device-Free Localization Method Using Light Sensors

Device-free localization (DFL) is a technique that can locate targets wearing no devices, and has become an attractive technique for wide applications, such as intrusion detection and gesture recognition. In recent years, research on indoor localization using light sensor mainly focuses on traditional active localization methods, which require photodiodes being attached to targets. In this paper, we explore the visible light-based DFL method. In DFL, shadowed areas caused by different targets may overlap when multiple targets simultaneously exist in a small monitored area, which increases difficulties both in counting target and achieving accuracy localization. This paper presents an accurate geometrical multi-target device-free localization method, which includes a sector ring model and an adaptive multi-target positioning (AMTP) algorithm. The proposed model does well in describing the relationship between the target location and the shadowed area, which provides essential observation information for DFL. The proposed AMTP algorithm improves the accuracy of counting and locating multiple targets through updating weight map of the monitored area in each iteration. Numerous simulation results show that the proposed DFL method effectively achieves multi-target location, and outperforms the traditional geometrical localization method and the fingerprinting localization method both in counting and localization accuracy.

Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks.

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

The sparse distribution of targets in monitored areas is an important prior for device-free localization (DFL) with radio tomography networks. In this article, our goal is to develop an enhanced sparse representation-based DFL method that takes the full potential of sparsity for location reconstruction. An expanded sensing matrix spanning the concatenation of a sampling matrix and a unit error-correcting base is proposed for modelling the measurement process. The sampling matrix can either be composed of the ellipse model from calibrated networks or the received signal strength (RSS) fingerprint-based model induced by training samples with one person at predefined locations. Thus, the sparsity of targets is enhanced under the expanded sensing matrix and the ℓ 1 -minimization-based approximations are derived for the recovery of locations. Experimental studies in an open outdoor scenario, in a line-of-sight (LOS) indoor scenario, and in a non-line-of-sight (NLOS) indoor scenario, are conducted to verify the efficacy of the proposed method.

Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithm

Device-free localization (DFL) plays an increasingly important role in many security and military applications. It can realize localization without the requirement of equipping targets with any devices for signal transmitting or receiving. To reduce the number of measurements in DFL, compressive sensing (CS) theory has been applied. By exploiting the sparse nature of location finding problem, the target location vector can be estimated from a few measurements. However, in changing environments, measurements may diverge from those in a fixed dictionary (sensing matrix), and the mismatches between the dictionary and runtime measurements can significantly deteriorate the localization performance of CS-based DFL methods. To address this, we propose a novel dictionary refinement-based DFL method. It adopts the saddle surface model to characterize the shadowing effects caused by targets and parameterizes the dictionary with the shadowing rate of each link as the underlying parameters. Then, the variational expectation-maximization algorithm is adopted to realize joint localization and dictionary refinement. Simulation results show that the proposed approach achieves higher accuracy and robustness compared with the state-of-the-art fixed dictionary DFL methods.

Fingerprint-Based Device-Free Localization Performance in Changing Environments

Device-free localization (DFL) systems locate a person in an environment by measuring the changes in received signals on links in a wireless network. A fingerprint-based DFL method collects a training database of measurement fingerprints and uses a machine learning classifier to determine a person's location from a new fingerprint. However, as the environment changes over time due to furniture or other objects being moved, the fingerprints diverge from those in the database. This paper addresses, for DFL methods that use received signal strength as measurements, the degradation caused as a result of environmental changes. We perform experiments to quantify how changes in an environment affect accuracy, through a repetitive process of randomly moving an item in a residential home and then conducting a localization experiment, and then repeating. We quantify the degradation and consider ways to be more robust to environmental change. We find that the localization error rate doubles, on average, for every six random changes in the environment. We find that the random forests classifier has the lowest error rate among four tested. We present a correlation method for selecting channels, which decreases the localization error rate from 4.8% to 1.6%.

FALE

Many emerging applications and the ubiquitous wireless signals have accelerated the development of Device Free localization (DFL) techniques, which can localize objects without the need to carry any wireless devices. Most traditional DFL methods have a main drawback that as the pre-obtained Received Signal Strength (RSS) measurements (i.e., fingerprint) in one area cannot be directly applied to the new area for localization, and the calibration process of each area will result in the human effort exhausting problem. In this paper, we propose FALE, a fine-grained transferring DFL method that can adaptively work in different areas with little human effort and low energy consumption. FALE employs a rigorously designed transferring function to transfer the fingerprint into a projected space, and reuse it across different areas, thus greatly reduce the human effort. On the other hand, FALE can reduce the data volume and energy consumption by taking advantage of the compressive sensing (CS) theory. Extensive real-word experimental results also illustrate the effectiveness of FALE.