Device-free Localization Methods Research Articles

Intelligent Mesh Cluster Algorithm for Device-Free Localization in Wireless Sensor Networks

Device-free localization (DFL) is a technology designed to determine the positions of targets without the need for them to carry electronic devices. It achieves this by analyzing the shadowing effects of radio links within wireless sensor networks (WSNs). However, obtaining high precision in DFL often results in increased energy consumption, severe electromagnetic interference, and other challenges that impact positioning accuracy. Most DFL schemes for accurate tracking require substantial memory and computing resources, which make them unsuitable for resource-constrained applications. To address these challenges, we propose an intelligent mesh cluster (IMC) algorithm that achieves accurate tracking by adaptively activating a subset of wireless links. This approach not only reduces electromagnetic interference but also saves energy. The IMC algorithm leverages geometric objects, such as meshes and mesh clusters formed by wireless links, to achieve low computational complexity. By scanning a subset of mesh cluster-related wireless links near the DFL target, the algorithm significantly reduces the computational requirements. The target’s location estimate is determined based on the connection information among the mesh clusters. We conducted numerous simulations to evaluate the performance of the IMC algorithm. The results demonstrate that the IMC algorithm outperforms grid-based and particle filter-based DFL methods, confirming its effectiveness in achieving accurate and efficient localization.

An accurate approach of device-free localization with attention empowered residual network

Device-free localization (DFL) has been recognized as an emerging technology in the Internet of Things (IoT) field. Based on soft computing techniques, e.g., cloud systems, neural networks, wireless sensor networks, etc., DFL can locate the targets that do not carry any equipment. A crucial problem for the DFL is how to extract significant patterns from weak signal variations in order to achieve high accuracy and robustness. To solve this issue, we transform the DFL procedure into a classification task. In this paper, a channel-dependent attention empowered residual network (CA-ResNet) with three residual layers is designed for precise localization by extracting underlying signal features. The CA-ResNet takes the advantages of the residual connection and the attention mechanism in feature extraction, which is expected to have a high level of robustness in the face of extreme noise and abnormal circumstances for DFL. Experiment results based on real-world data demonstrate that our proposed approach provides a high level of localization performance for a suitable grid size. For instance, the CA-ResNet approach achieves high localization accuracy of almost 100% in general cases. Under the condition of introducing noise to data, e.g., a signal to noise ratio greater than −5 dB, our proposed method maintains high accuracy and strong robustness. Compared with the state-of-the-art DFL methods in several cases, the best localization accuracy obtained by our proposed method is 16.4% better than the compared AugRF, 15.5% better than the radio robust image fingerprint indoor localization algorithm (RRIFLoc), and 6.9% better than the convolutional autoencoder neural network, especially in challenging cases that partial sensor nodes are broken or power off under noisy condition.

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.

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in the IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to an undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of the model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds −10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.

Never Use Labels: Signal Strength-Based Bayesian Device-Free Localization in Changing Environments

Device-free localization (DFL) methods use measured changes in the received signal strength (RSS) between many pairs of RF nodes to provide location estimates of a person inside the wireless network. Fundamental challenges for RSS DFL methods include having a model of RSS measurements as a function of a person's location, and maintaining an accurate model as the environment changes over time. Current methods rely on either labeled empty-area calibration or labeled fingerprints with a person at each location. Both need to be frequently recalibrated or retrained to stay current with changing environments. Other DFL methods only localize people in motion. In this paper, we address these challenges by, first, introducing a new mixture model for link RSS as a function of a person's location, and second, providing the framework to update model parameters without ever being provided labeled data from either empty-area or known-location classes. We develop two new Bayesian localization methods based on our mixture model and experimentally validate our system at three test sites with seven days of measurements. We demonstrate that our methods localize a person with non-degrading performance in changing environments, and, in addition, reduce localization error by $\mathbf {11-51}$ 11 - 51 percent compared to other DFL methods.

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.

SA-M-SBL: An Algorithm for CSI-Based Device-Free Localization With Faulty Prior Information

As an emerging technique, device-free localization (DFL) is promising to localize the target without attaching any transceivers. Recently, the benefits of channel state information (CSI) on DFL have been revealed in this paper. Motivated by this, in this paper, we propose to exploit the channel diversity of CSI measurements for multi-target DFL under the compressive sensing (CS) framework. The CSI-based multi-target DFL problem is formulated as a joint sparse recovery problem which reconstructs the unknown sparse vectors of multiple channels. Moreover, in practice, some faulty prior information (e.g., coarse positions) is usually available. To take advantage of this information for joint sparse recovery, novel support knowledge-aided multiple sparse Bayesian learning (SA-M-SBL) algorithm is introduced, which incorporates the prior information into a three-layer hierarchical prior model. With this model, the joint sparsity of the sparse vectors can be induced, and their values can be estimated via the variational Bayesian inference (VBI). The numerical simulation results demonstrate the outstanding performance of the proposed method compared with the state-of-the-art CS-based multi-target DFL methods.

Enhanced CS-Based Device-Free Localization With RF Sensor Networks

With the inherent sparsity of spatial positioning, the compressive sensing (CS)-based device-free localization (DFL) methods have been proposed in recent studies, which can reduce the sampling requirements. However, model errors and grid errors in CS-based DFL methods are inevitable due to using the fixed dictionary and discretization, but these errors are ignored by most existing CS-based DFL works. To solve these issues, this letter proposes an enhanced CS-based DFL scheme, which first exploits the increment dictionary learning technique to overcome the problem of the dictionary mismatch and then utilizes the quadratic programming approach to mitigate grid errors. Experimental results verify the better performance of the proposed method in terms of localization accuracy compared with the state-of-the-art CS-based DFL algorithms.

Towards robust and efficient device-free localization using UWB sensor network

Device-free localization (DFL) is the method of using distributed wireless sensors to localize the target without carrying any devices. Existing DFL methods leverage the variation of narrowband received signal strength (NRSS) which is vulnerable to multipath fading, and thus results in considerable performance degradation in indoor environments. Moreover, the inefficient sensor deployment of traditional DFL involves huge human efforts, which is not suitable for emergency scenarios. In this paper, we utilize sensors transmitting ultra-wideband (UWB) signals to solve both problems. We proposed two RSS variation estimation methods based on the channel impulse response (CIR) measurements provided by UWB sensors, which turn out to be more robust to multipath than NRSS due to the fine multipath resolution of UWB signals. We also employ a higher operating frequency to enhance the shadowing loss for mitigating the multipath effect. Additionally, satisfactory sensor self-localization is achieved under the cooperative localization framework owing to the accurate ranging capability of UWB sensors. We conducted experiments in three different environments to explore the feasibility of our method. The results show that the proposed method gains much better localization performance and requires less human efforts than narrowband DFL.

E-HIPA: An Energy-Efficient Framework for High-Precision Multi-Target-Adaptive Device-Free Localization

Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an e nergy-efficient framework for high-precision multi-target-a daptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.

A Geometric Filter Algorithm for Robust Device-Free Localization in Wireless Networks

Device-free localization (DFL) is the method of determining an uncooperative target’s location by using the shadowing induced by the target on the radio links crossing through the wireless network’s deployment area. Most DFL schemes require high memory and computing resources for accurate tracking performance, and thus may not be suitable for resource-constrained applications. To address this issue, a geometric filter (GF) algorithm that achieves accurate tracking using only operations on simple geometric objects is proposed. It reduces the algorithm’s storage and computational resource requirements resulting in fast execution times. Experimental results show that the GF algorithm outperforms some grid-based and particle filter-based DFL methods with up to $65\%$ reduction in tracking root-mean-square error, and up to 300 times improvement in average execution time.

The Case for Efficient and Robust RF-Based Device-Free Localization

Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subject’s location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the occupied cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

An Enhanced Geometric Filter Algorithm With Channel Diversity for Device-Free Localization

Device-free localization (DFL) techniques estimate a person's location without requiring the person to carry a radio device or tag. In the received signal strength (RSS)-based DFL system, the changes caused by the person's presence in the RSS of links in a wireless network are used to infer his location. A DFL system's accuracy is degraded in cluttered environments due to multipath fading. In this paper, the absolute changes in the RSS of links from different frequency channels are combined into a single measurement vector to mitigate the multipath fading effects. The measurements are then processed by a multichannel geometric filter (MCGF) algorithm that uses link-specific thresholds based on a link's fade level and RSS variance on different frequency channels to detect the target-affected links. In addition, the MCGF algorithm weights probable target locations by the overall fade levels of the target-affected links. Through experiments in both open and cluttered environments, the use of channel diversity and link-specific thresholds is demonstrated to outperform single-channel GF and multichannel methods that are based on radio tomographic imaging in terms of tracking accuracy. The proposed algorithm also has a lower computational overhead compared with existing DFL methods, resulting in much faster execution times.

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.

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.

Breathfinding: A Wireless Network That Monitors and Locates Breathing in a Home

This paper explores using RSS measurements on the links between commercial wireless devices to locate where a breathing person is located and to estimate their breathing rate, in a home, while the person is sitting, lying down, standing, or sleeping. Prior RSS-based device-free localization methods required calibration measurements to be able to locate stationary people, or did not require calibration but only located people who moved. We collect RSS measurements multiple short (3-7 minute) tests and during a longer 66 minute test, and show the location of the breathing person can be estimated, to within about 2 m average error. We describe a detector that distinguishes between sample times during which a person is moving and sample times during which a person is breathing but otherwise motionless. This detector enables removal of motion interference, i.e., RSS changes due to movements other than a person's breathing, and more accurately estimate a person's breathing rate. Being able to locate and monitor a breathing person, without calibration, is important for applications in search and rescue, health care, and security.