Device-free Localization System Research Articles

Room-scale Location Trace Tracking via Continuous Acoustic Waves

The increasing prevalence of smart devices spurs the development of emerging indoor localization technologies for supporting diverse personalized applications at home. Given marked drawbacks of popular chirp signal-based approaches, we aim at developing a novel device-free localization system via the continuous wave of the inaudible frequency. To achieve this goal, solutions are developed for fine-grained analyses, able to precisely locate moving human traces in the room-scale environment. In particular, a smart speaker is controlled to emit continuous waves at inaudible 20kHz , with a co-located microphone array to record their Doppler reflections for localization. We first develop solutions to remove potential noises and then propose a novel idea by slicing signals into a set of narrowband signals, each of which is likely to include at most one body segment’s reflection. Different from previous studies, which take original signals themselves as the baseband, our solutions employ the Doppler frequency of a narrowband signal to estimate the velocity first and apply it to get the accurate baseband frequency, which permits a precise phase measurement after I-Q (i.e., in-phase and quadrature) decomposition. A signal model is then developed, able to formulate the phase with body segment’s velocity, range, and angle. We next develop novel solutions to estimate the motion state in each narrowband signal, cluster the motion states for different body segments corresponding to the same person, and locate the moving traces while mitigating multi-path effects. Our system is implemented with commodity devices in room environments for performance evaluation. The experimental results exhibit that our system can conduct effective localization for up to three persons in a room, with the average errors of 7.49 cm for a single person, with 24.06 cm for two persons, with 51.15 cm for three persons.

Exploring wireless device-free localization technique to assist home-based neuro-rehabilitation.

Home-based movement neuro-rehabilitation is quite necessary when the patient goes back home from hospital. Due to lack of supervision from doctors, rehabilitation at home is often forgotten. As an alternate to doctor-supervision, in this research, we explore the wireless device-free localization technique to assist the rehabilitation procedure. The localization technique can judge whether the patient is near the rehabilitation equipment and even obtain the movement trajectory. The most challenging problem in the wireless device-free localization system is that the received-signal-strength (RSS) of the electromagnetic-wave is unpredictable, which increases the localization error. How to select the informative RSS is pretty important. This research proposes a new criterion (i.e., fluctuation-level) to select the informative RSS. Experimental results show the effectiveness of the proposed fluctuation-level in reducing the localization error.

Push the Limit of Millimeter-wave Radar Localization

Existing device-free localization systems have achieved centimeter-level accuracy and show their potential in a wide range of applications. However, today’s radio-based solutions fail to locate the target in millimeter-level due to their limited bandwidth and sampling rate, which constrains their applications in high-accuracy demand scenarios. We find an opportunity to break the bottleneck of existing radio-based localization systems by reconstructing the accurate signal spectral peak from the discrete samples, without changing either the bandwidth or the sampling rate of the radio hardware. This study proposes milliLoc , a millimeter-level radio-based localization system. We first derive a spectral peak reconstruction algorithm to reduce the ranging error from the previous centimeter-level to millimeter-level. Then, we improve the AoA measurement accuracy by leveraging the signal amplitude information. To ensure the practicality of milliLoc , we further extend our system to handle multi-target situations. We fully implement milliLoc on a commercial mmWave radar. Experiments show that milliLoc achieves a median ranging accuracy of 5.5 mm and decreases the AoA measurement error by 31.2% compared with the baseline. Our system fulfills the accuracy requirements of most application scenarios and can be easily integrated with other existing solutions, shedding light on high-accuracy location-based applications.

MA-RTI: Design and Evaluation of a Real-World Multipath-Assisted Device-Free Localization System.

Device-free localization (DFL) systems exploit changes in the radio frequency channel by measuring, for example, the channel impulse response (CIR), to detect and localize obstacles within a target area. However, due to a lack of well-defined interfaces, missing modularization, as well as complex system configuration, it is difficult to deploy DFL systems outside of laboratory setups. This paper focused on the system view and the challenges that come with setting up a DFL system in an indoor environment. We propose MA-RTI, a modular DFL system that is easy to set up, and which utilizes a multipath-assisted (MA) radio-tomographic imaging (RTI) algorithm. To achieve a modular DFL system, we proposed and implemented an architectural model for DFL systems. For minimizing the configuration overhead, we applied a 3D spatial model, that helps in placing the sensors and calculating the required calibration parameters. Therefore, we configured the system solely with idle measurements and a 3D spatial model. We deployed such a DFL system and evaluated it in a real-world office environment with four sensor nodes. The radio technology was ultra-wideband (UWB) and the corresponding signal measurements were CIRs. The DFL system operated with CIRs that provided a sub-nanosecond time-domain resolution. After pre-processing, the update rate was approximately 46 Hz and it provided a localization accuracy of 1.0 m in 50% of all cases and 1.8 m in 80% of all cases. MA fingerprinting approaches lead to higher localization accuracy, but require a labor-intensive training phase.

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

Device-Free Localization of Multiple Targets in Cluttered Environments

Device-free localization (DFL) enables several new applications in various sectors including smart cities, intelligent transportation, and public safety. DFL relies on a network of sensor radars that transmit, receive, and process reflected signals propagating in a monitored environment. The accuracy of DFL degrades in cluttered environments, due to the presence of undesired objects that reflect the signal. Indeed, the multiple reflections of the signal overlap at the receiver and make the inference of targets’ positions challenging. This article presents a theoretical foundation of DFL in cluttered environments by deriving the fundamental limits on DFL accuracy. In particular, we propose a system model that takes into account multiple reflections, nonline-of-sight conditions, and the presence of multiple targets. Building on such a model, we derive the Cramér-Rao bound on the inference accuracy of targets’ positions by applying equivalent Fisher information analysis. The proposed bound provides guidelines for the design and analysis of DFL systems operating in cluttered environments. Then, the article presents a case study compliant with the 5G New Radio numerology and channel modeling. Results show how the minimum achievable error is affected by multiple reflections and multiple targets and to which extent the employment of a signal with larger bandwidth and a network with a higher number of receivers can lower the achievable error toward submeter accuracy.

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars.

Indoor device-free localization (DFL) systems are used in various Internet-of-Things applications based on human behavior recognition. However, the usage of camera-based intuitive DFL approaches is limited in dark environments and disaster situations. Moreover, camera-based DFL schemes exhibit certain privacy issues. Therefore, DFL schemes with radars are increasingly being investigated owing to their efficient functioning in dark environments and their ability to prevent privacy issues. This study proposes a deep learning-based DFL scheme for simultaneous estimation of indoor location and posture using 24-GHz frequency-modulated continuous-wave (FMCW) radars. The proposed scheme uses a parallel 1D convolutional neural network structure with a regression and a classification model for localization and posture estimation, respectively. The two-dimensional location information of the target is estimated for localization, and four different postures, namely standing, sitting, lying, and absence, are estimated simultaneously. We experimentally evaluated the proposed scheme and compared its performance with that of conventional schemes under identical conditions. The results indicate that the average localization error of the proposed scheme is 0.23 m, whereas that of the conventional scheme is approximately 0.65 m. The average posture estimation error of the proposed scheme is approximately 1.7%, whereas that of the conventional correlation, CSP, and SVM schemes are 54.8%, 42%, and 10%, respectively.

Electromagnetic Models for Passive Detection and Localization of Multiple Bodies

The paper proposes a multi-body electromagnetic (EM) model for the quantitative evaluation of the influence of multiple human bodies in the surroundings of a radio link. Modeling of human-induced fading is the key element for the development of real-time Device-Free (or passive) Localization (DFL) and body occupancy tracking systems based on the processing of the Received Signal Strength (RSS) data recorded by radio-frequency devices. The proposed physical-statistical model, is able to relate the RSS measurements to the position, size, orientation, and random movements of people located in the link area. This novel EM model is thus instrumental for crowd sensing, occupancy estimation and people counting applications for indoor and outdoor scenarios. The paper presents the complete framework for the generic N-body scenario where the proposed EM model is based on the knife-edge approach that is generalized here for multiple targets. The EM-equivalent size of each target is then optimized to reproduce the body-induced alterations of the free space radio propagation. The predicted results are then compared against the full EM simulations obtained with a commercially available simulator. Finally, experiments are carried out to confirm the validity the proposed model using IEEE 802.15.4-compliant industrial radio devices.

A Device-Free Localization and Size Prediction System for Road Vehicle Surveillance via UWB Networks

Device-free localization (DFL) is a technique of locating targets via wireless sensor networks (WSNs) without carrying any electronic devices. This article introduces a device-free vehicle localization system that can determine the position and 2-D size of a dynamic vehicle in a monitoring region via ultra wide band (UWB) networks. Specifically, our system involves an obstructed recognition model for detecting the state of links, a state-input radio tomography imaging (SI-RTI) algorithm for reconstructing a high-resolution image, a contour threshold method for predicting the size of a vehicle target, and a gradient-advance-retreat (GAR) optimization algorithm for improving accuracy. Field experiments show that our system can estimate the position and size of a moving vehicle with a location root-mean-square error (RMSE) of less than 1.1 m and a root-mean-square overlap degree of more than 75% between the prediction and the real target.

Performance Comparison of Various Smoothing Methods For Detection of Device-Free Target

In the device-free localization systems, it is major issue how to detect a device-free target, which does not carry any assistant electronical devices. When a target crosses the communication links, the target causes the fluctuation of received signal strength (RSS) values at the receiver side. However, the performance of the detection scheme based on RSS tends to be degraded because of noises in indoor environments. In this paper, we present various smoothing methods to mitigate the effect of noises for enhanced performance of target detection. To compare the performance of various smoothing methods, we performed experiments with IEEE 802.15.4 ZigBee devices. According to the experimental results, it is shown that the Gaussian kernel smoothing, the weighted moving average smoothing, and the exponential moving average smoothing methods can provide more suitable performance than other methods in terms of mean square error, while the exponential moving average smoothing provides more suitable performance in terms of detection accuracy and detection temporal accuracy.

Cooperative Networked PIR Detection System for Indoor Human Localization.

Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary PIR sensor detects only the presence of a human motion in its field of view (FOV) without any other information about the actual location, utilizing the information of overlapping FOV of multiple sensors can be useful for localization. In this study, a PIR detector and sensing signal processing algorithms were designed based on the characteristics of the PIR sensor. We applied the designed PIR detector as a sensor node to create a non-wearable cooperative indoor human localization system. To improve the system performance, signal processing algorithms and refinement schemes (i.e., the Kalman filter, a Transferable Belief Model, and a TBM-based hybrid approach (TBM + Kalman filter)) were applied and compared. Experimental results indicated system stability and improved positioning accuracy, thus providing an indoor cooperative localization framework for PIR sensor networks.

Enhanced Geometric Filtering Method Based Device-Free Localization With UWB Wireless Network

The measurement of received signal strength (RSS) and channel state information (CSI) has been widely used in the research of device-free localization (DFL). However, DFL systems based on these systems usually require labour-intensive training to understand the impact of the target on the surrounding wireless signals to build the target location-related model or image. In addition, the fact that the RSS is susceptible to multipath interference and is not directly accessible to CSI directly limits its actual deployment. In this paper, we explore the feasibility of DFL based on time-of-flight measurements of ultra-wideband signals, and propose a fusion method for simultaneous localization of multiple targets based on the evaluation results. Specifically, we mesh the feasible region of the target and utilize the geometric filtering method to re-evaluate the filtered wireless links in combination with the real-time adaptive relaxation target model. Consequently, we introduce the clustering method and combine the category matching strategy to ensure that the algorithm has robust real-time multi-target perception ability. Extensive experiments conducted in the lobby, laboratory and office scenarios demonstrate that the proposed method plays an active role in more than 82% and can control the mean error within 0.6 m.

Multipath-Enhanced Device-Free Localization in Wideband Wireless Networks

State-of-the-art device free localization systems infer presence and location of users based on received signal strength measurements of line-of-sight links in wireless networks. In this letter, we propose to enhance device free localization systems by exploiting multipath propagation between the individual network nodes. Particularly, indoors, wireless propagation channels are characterized by multipath propagation, i.e., received signals comprise multipath components due to reflection and scattering. Given prior information about the surrounding environment, e.g., a floor plan, the individual propagation paths of multipath components can be derived geometrically. Inherently, these propagation paths differ spatially from the line-of-sight propagation path and can be considered as additional links in the wireless network. This extended network determines the novel multipath-enhanced device free localization system. Using theoretical performance bounds on the localization error, we show that including multipath components into device-free localization systems improves the overall localization performance and extends the coverage area significantly.

A robust indoor localization method with calibration strategy based on joint distribution adaptation

Device-free localization (DFL) systems have aroused extensive attention because it is more convenient than device-enabled localization systems, and fingerprint-based localization method is usually used in DFL systems. Although fine-grained information can be provided by the channel state information (CSI), but changes in the environment over time can cause the CSI become different. Therefore, the real-time CSI data can’t match with the data in the fingerprint map established beforehand very well, which can lead to the inaccuracy of the positioning result. This paper presents a DFL system, which adopts transfer learning method to update the fingerprint map and employs the Light Gradient Boosting Machine (LightGBM) algorithm to train the fingerprint map. Wavelet transform is used in this paper to filter the noise in the raw CSI data and the CSI data on a portion of the fingerprint points are collected to update the established fingerprint map by joint distribution adaptation in the update stage. After classifying the CSI data of the testing point by LightGBM, the position coordinate is achieved by the confidence regression method. By using LightGBM, the proposed system can achieve the average distance error of 0.48m, outperforming the result by using eXtreme Gradient Boosting (XGBoost) and Gradient Boost Decision Tree (GBDT). According to the result of the four-week experiment, the average distance error of this system can be decreased by 21% compared with not using the calibration method.

MAMPI-UWB-Multipath-Assisted Device-Free Localization with Magnitude and Phase Information with UWB Transceivers.

In this paper, we propose a multipath-assisted device-free localization (DFL) system that includes magnitude and phase information (MAMPI). The DFL system employs ultra-wideband (UWB) channel impulse response (CIR) measurements, enabling the extraction of several multipath components (MPCs) and thereby benefits from multipath propagation. We propose a radio propagation model that calculates the effect on the received signal based on the position of a person within a target area. Additionally, we propose a validated error model for the measurements and explain the creation of different feature vectors and extraction of the MPCs from Decawave DW1000 CIR measurements. We evaluate the system via simulations of the position error probability and a measurement setup in an indoor scenario. We compare the performance of MAMPI to a conventional DFL system based on four sensor nodes that measures radio signal strength values. The combination of the magnitude and phase differences for the feature vectors results in a position error probability that is comparable to a conventional system but requires only two sensor nodes.

TWPalo: Through-the-wall passive localization of moving human with Wi-Fi

Recently, we have witnessed a booming growth of Wi-Fi-based indoor localization systems that can locate the user without requiring the user to hold or wear any device. To foster a broad range of real-world based applications via these systems, the localization under the through-the-wall scenario (TTW) needs to be addressed, which is of great challenge. Based on the in-depth understanding of human induced reflection conveyed by Wi-Fi channel state information (CSI), we propose TWPalo, a through-the-wall device-free localization system. To achieve this goal, a three-dimensional joint parameter estimation algorithm is proposed to estimate the time of flight (ToF), angle of arrival (AoA), and Doppler frequency shift (DFS) of the propagation path. Then, by iterating joint parameter estimation, channel reconstruction, and cancellation, an innovative algorithm is proposed to separate the CSI of each propagation path and obtain the parameters of subtle human induced reflection (HIR), which could be overwhelmed by strong propagation paths, under the TTW scenario. At last, TWPalo translates the obtained AoA and ToF of subtle HIR into the locations of the human behind the wall. Extensive experimental evaluation validates the great performance of TWPalo in terms of parameter estimation and localization, under typical indoor TTW scenarios of glass and concrete wall.

MFPL: Multi-Frequency Phase Difference Combination Based Device-Free Localization

With the popularity of indoor wireless network, device-free indoor localization has attracted more and more attention. Unlike device-based localization where the target is required to carry an active transmitter, their frequent signal scanning consumes a large amount of energy, which is inconvenient for devices with limited energy. In this work, we propose the MFPL, device-free localization (DFL) system based on WiFi distance measurement. First, we combine multi-subcarrier characteristic of Channel State Information (CSI) with classical Fresnel reflection model to get the linear relationship between the change of the length of reflection path and the subcarrier phase difference. Then we calculate the Fresnel phase difference between subcarrier pairs with different spacing from CSI amplitude time series. Finally, we get the change of the length of the reflection path caused by target moving to achieve distance measurement and localization. Using a combination of subcarriers with different spacing to achieve distance measurement effectively broadens the maximum unambiguous distance of the system. To solve the complex non-linear problem of the intersection of two elliptic equations, we introduce Newton's method to transform the non-linear problem into a linear one. The effectiveness of our approach is verified using commodity WiFi infrastructures. The experimental results show our method achieves a median error of 0.87 m in actual indoor environment.

Improved Distance Metrics for Histogram-Based Device-Free Localization

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram distance-based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the “long-term” and “short-term” histograms. The existing HD-DFL methods use either Kullback–Leibler or the subsequent improvement, kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. The results from practical implementation in two different environments show that some distance metrics perform considerably better than the kernel distance when used for existing DFL techniques, such as radio tomographic imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.

Device-Free Localization Systems Utilizing Wireless RSSI: A Comparative Practical Investigation

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric to localize targets with no device attached to them have been reported in the literature for almost a decade. Approaches using RSSI can be split into three main categories. Link-based approaches utilize weighted summation or probabilistic methods to infer location. Location-based approaches create a fingerprint map of an area. Radio Tomographic Imaging treat DFL as an imaging problem solved with a linear inverse. In this paper, we implement and investigate the performance of all three major RSSI approaches in two test environments. We demonstrate how different environments and walking trajectories can have significant effects on the localization accuracy. The experimental results lead us to the conclusion that without implementing and testing within the same environment for the same target trajectories, the performance of various classes of DFL systems cannot be reliably evaluated. Relying on the stated accuracy from the literature for comparison is a flawed premise.

MSDFL: a robust minimal hardware low-cost device-free WLAN localization system

The research on fingerprint-based device-free (DF) indoor localization has attracted great interest due to the ubiquitous of radio frequency signals and its accuracy. Most such approaches typically require a large number of transmitters and receivers to achieve acceptable accuracy, and there is another problem that the accuracy is degraded due to the expiration of the fingerprint database over time. In this paper, we present an accurate fingerprint-based device-free localization system using only a single stream over time, termed MSDFL. By analyzing the CSI fingerprint patterns and comparing the matrix similarity between CSI fingerprints and testing CSI samples matrix, the proposed MS algorithm is able to provide accurate DF localization with only a single stream. To cope with the noisy CSI samples and remove the subcarriers greatly affected by multipath, a novel CSI pre-processing scheme is applied on CSI data to reduce the noise and extract the most contributing subcarriers. In addition, to overcome the decrease in positioning accuracy caused by the expiration of the fingerprint database, a rigorously designed update scheme using artificial neural network is utilized for the renewal of fingerprinting database, which further enhance the performance of our system over time. Experimental results are presented to confirm that MSDFL can effectively improve location accuracy, compared with the other existing methods in representative indoor environment.