Device-free Indoor Localization Research Articles

Environment-aware Multi-person Tracking in Indoor Environments with MmWave Radars

Device-free indoor localization and tracking using commercial millimeter wave radars have attracted much interest lately due to their non-intrusive nature and high spatial resolution. However, it is challenging to achieve high tracking accuracy due to rich multipath reflection and occlusion in indoor environments. Static objects with non-negligible reflectance of mmWave signals interact with moving human subjects and generate time-varying multipath ghosts and shadow ghosts, which can be easily confused as real subjects. To characterize the complex interactions, we first develop a geometric model that estimates the location of multipath ghosts given the locations of humans and static reflectors. Based on this model, the locations of static reflectors that form a reflection map are automatically estimated from received radar signals as a single person traverses the environment along arbitrary trajectories. The reflection map allows for the elimination of multipath and shadow ghost interference as well as the augmentation of weakly reflected human subjects in occluded areas. The proposed environment-aware multi-person tracking system can generate reflection maps with a mean error of 15.5cm and a 90-percentile error of 30.3cm, and achieve multi-person tracking accuracy with a mean error of 8.6cm and a 90-percentile error of 17.5cm, in four representative indoor spaces with diverse subjects using a single mmWave radar.

Scene Recognition for Device-Free Indoor Localization

Device-free indoor localization methods based on Channel State Information (CSI) have become an increasingly important topic. In complex indoor environments, both the line-of-sight (LOS) and non-LOS areas coexist, and the optimal parameters of the localization models for these two areas are different. In order to address this problem, in this paper, the scene-recognition indoor localization method is proposed to identify LOS and NLOS areas. First, the scene recognition model is given by combining mutation particle swarm optimization (MPSO) with backpropagation (BP) neural network. Then, the feature transformation method based on discriminant correlation analysis (DCA) is presented, which can effectively explore the correlation of amplitude data and phase data and form high-resolution location fingerprints, and further improve the indoor localization accuracy. Experimental results show that compared with the PAIL, LCAF, LSTM, BLS, PCNB, ABPS and FapFi algorithms, the proposed algorithm has higher localization accuracy.

A Meta-Learning Approach for Device-Free Indoor Localization

Although fingerprint-based methods could achieve high location accuracy for device-free indoor localization, it requires cost-expensive massive labeling. In order to fully exploit the previously collected CSI fingerprints for various indoor localization tasks, this letter proposes a meta-learning approach for indoor localization, with which an excellent initialization solution for a new localization task is resorted and further training with a few labeled samples results in sound performance improvement over the state-of-the-art deep learning (DL) approach. By recognizing the unequal contribution of an individual training task to the target task, a novel weighted loss is introduced under the framework of meta-learning. Experiments show that the proposed meta-learning approach can gain improvement of 20% Root-Mean-Square-Error (RMSE) enhancement over the DL approach.

Device Free Indoor Localization in the 28 GHz band based on machine learning

By exploiting the received power change in a communication link produced by the presence of a human body in an otherwise empty room, this work evaluates indoor free device localization methods in the 28 GHz band using machine learning techniques. For this objective, a database is built using results from ray tracing simulations of a system comprised of 4 receivers and up to 2 transmitters, while a person is standing within the room. Transmitters are equipped with uniform linear arrays that switch their main beams sequentially at 21 angles, whereas the receivers operate with omnidirectional antennas. Statistical localization error reduction of at least 16% over a global-based classification technique can be obtained through the combination of two independent classifiers using one transmitter and a reduction of at least 19% for 2 transmitters. An additional improvement is achieved by combining each independent classifier with a regression algorithm. Results also suggest that the number of examples per class and size of the blocks (strips) in which the study area is partitioned play a role in the localization error.

Device-Free WLAN Based Indoor Localization Scheme With Spatially Concatenated CSI and Distributed Antennas

Various machine learning (ML) based localization schemes using channel state information (CSI) in wireless local area networks (WLANs) have been investigated recently. Adopting a proper feature selection technique is important to achieve further improvement in detection accuracy. As described herein, we propose a device-free indoor localization scheme using a lightweight ML model with compressed spatially concatenated CSI in WLAN systems with distributed antennas. In this scheme, feedback beam-forming weights (BFWs) are collected at a CSI capture terminal. Then, current and past BFWs are concatenated as accurate feature data to characterize the object behavior. Additionally, we propose the use of a frequency-domain sampling scheme for a low-complexity real-time target position detection with a small number of datasets. Using ray-trace based simulation analysis and experimentally obtained results from an indoor environment, we demonstrate that the proposed scheme using the concatenated CSI is effective not only for achieving more accurate real-time detection, but also for reducing the necessary complexity for both off-line training and on-line classification compared with other reference schemes.

Passive infrared sensor dataset and deep learning models for device-free indoor localization and tracking

Location estimation or localization is one of the key components in IoT applications such as remote health monitoring and smart homes. Amongst device-free localization technologies, passive infrared (PIR) sensors are one of the promising options due to their low cost, low energy consumption, and good accuracy. However, most of the existing systems are complexly designed and difficult to deploy in real life, in addition, there is no public dataset available for researchers to benchmark their proposed localization and tracking methods. In this paper, we propose a system and a dataset collected from our PIR system consisting of commercial-of-the-shelf (COTS) sensors without any modification. Our dataset includes profile data of 36 classes that have over 1,000 samples of different walking directions and test data consisting of multiple scenarios with a sequence length of over 2,000 timesteps. To evaluate our system and dataset, we implement various deep learning methods such as CNN, RNN, and CNN–RNN. Our results prove the applicability and feasibility of our system and illustrate the viability of deep learning methods for PIR-based localization and tracking. We also show that our dataset can be converted for coordinate estimation so that deep learning methods and particle filter approaches can be applied to estimate coordinates. As a result, the best performer achieves a distance error of 0.25 m.

Device-free indoor localization based on sparse coding with nonconvex regularization and adaptive relaxation localization criteria

Device-free indoor localization based on sparse coding with nonconvex regularization and adaptive relaxation localization criteria

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.

A Parallel AdaBoost Method for Device-Free Indoor Localization

A Parallel AdaBoost Method for Device-Free Indoor Localization

Device-Free Indoor Localization of CSI Based on Limited Penetrable Horizontal Visibility Graph

Most of the current indoor localization methods based on channel state information (CSI) utilize the amplitude and phase of each subcarrier individually as location fingerprints, but the correlation between adjacent subcarriers also contains essential location information. Therefore, this paper proposes a device-free indoor localization method based on CSI and limited penetrable horizontal visibility graph (LPHVG), which does not require the subject to carry a device and can be implemented with only a single access point (AP). Firstly, we model the frequency correlation between subcarriers by LPHVG algorithm and construct a CSI-based complex network. Secondly, the topology of the complex network is utilized to analyze the relationship between CSI adjacent subcarriers and extract network features, which are combined with statistical features as fingerprint information to characterize different locations. Finally, the method is combined with support vector regression (SVR) to realize indoor localization in different environments. The experimental results show that the proposed method can significantly improve the localization accuracy, and has an excellent performance in different indoor scenarios.

Novel Cascade Classifier Using Multiresolution Progressive Learning for Device-Free Indoor Localization

Indoor localization of human objects has many important applications nowadays. Here, we propose a new multiresolution device-free indoor localization scheme using wireless radio frequency (RF) fingerprints. In our proposed new device-free approach, all transceiver devices are fixed in an indoor environment, so human targets do not need to carry any transceiver device with them. First, the indoor geometry is divided into several zones, and then, the received signal strength indicators measured by the receiving antennas are input features to our designed innovative machine learning model to identify within which zone the target is. Our proposed machine learning model, i.e., a multiresolution random forest classifier, is composed of a cascade architecture, which integrates and distills learned results over various zoning resolutions. The proposed new multiresolution approach greatly outperforms the existing random forest classifier. The average Euclidean distance error resulting from our proposed new technique is 1.0376 m.

Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System

The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.

SWIM: Speed-Aware WiFi-Based Passive Indoor Localization for Mobile Ship Environment

Accurate and pervasive device-free indoor localization with meter-level resolution is critical for large cruise and passenger ships due to safety-critical rescue and evacuation requirements when accidents occur. However, existing localization techniques would severely suffer on ships because of their unique mobility characteristics. In this paper, we take the first attempt to build a ubiquitous passive localization system using WiFi fingerprints for the mobile ship environment. By conducting extensive experiments and measurements during several cruise trips, we identified a major influence factor on the fingerprints in the mobile environment: varying the ship speeds may significantly change the patterns of fingerprints at runtime. Since it may be too expensive to identify the fingerprints associated with different speeds, we propose an efficient localization method, namely SWIM, which calibrates the fingerprints from only a single-speed scenario to multiple-speed scenarios using a signal reconstruction analysis. SWIM is designed to learn the predictive fingerprint variation introduced by environmental speed changes and reconstruct the original fingerprints to adapt to the runtime speed scenarios. We have implemented and extensively evaluated SWIM on actual cruise ships. Experimental results demonstrate that SWIM improves localization accuracy from 63.2 to 82.9 percent, while reducing the overall system deployment cost by 87 percent.

A Survey on Device-free Indoor Localization and Tracking in the Multi-resident Environment

Indoor device-free localization and tracking can bring both convenience and privacy to users compared with traditional solutions such as camera-based surveillance and RFID tag-based tracking. Technologies such as Wi-Fi, wireless sensor, and infrared have been used to localize and track people living in care homes and office buildings. However, the presence of multiple residents introduces further challenges, such as the ambiguity in sensor measurements and target identity, to localization and tracking. In this article, we survey the latest development of device-free indoor localization and tracking in the multi-resident environment. We first present the fundamentals of device-free localization and tracking. Then, we discuss and compare thetechnologiesused in device-free indoor localization and tracking. After discussing the steps involved in multi-resident localization and tracking including target detection, target counting, target identification, localization, and tracking, thetechniquesrelated to each step are classified and discussed in detail along with the performance metrics. Finally, we identify the research gap and point out future research directions. To the best of our knowledge, this survey is the most comprehensive work that covers a wide spectrum of the research area of device-free indoor localization and tracking.

MoLoc: Unsupervised Fingerprint Roaming for Device-Free Indoor Localization in a Mobile Ship Environment

Device-free indoor localization may play a critical role in improving passengers' safety in large vessels, particularly for scenarios without equipped radios. However, due to dynamic internal and external influences from the sailing ship such as changing sailing speed, the existing localization systems suffer huge accuracy degradation in a mobile ship environment. The challenges are mainly due to rich and arbitrary ship motions and the resulting complicated impacts on the indoor wireless channels. To address the challenges, in this article, we first propose a ship motion descriptor to extract discriminative latent representation from complex ship motions by leveraging deep-learning techniques. Based on this representation, we then design a novel fingerprint roaming model, i.e., MoLoc, to automatically learn the predictive fingerprint variation pattern and transfer the online fingerprint measurement to adapt to dynamic ship motions in real time. Furthermore, an unsupervised learning strategy is proposed to train the fingerprint roaming model using unlabeled onboard collected data which do not incur any labor costs. We have implemented and extensively evaluated MoLoc on real-world cruise ships, where experimental results demonstrate that MoLoc improves localization accuracy from 63.2% to 92.8% compared to the state-of-the-art localization methods, including Pilot, LiFS, SpotFi, and AutoFi while achieving a mean error of 0.68 m.

Indoor static localization based on Fresnel zones model using COTS Wi-Fi

Compared with outdoor localization using Global Positioning System (GPS), indoor localization is more complex and difficult due to the shadowing, fading and reflecting of radio. Thus, pioneers struggled for accurate device-free indoor localization and many creative schemes were proposed. However, these schemes still have defects and deficiencies. Mode-based schemes usually extract features of radio signal as fingerprints to match the target positions and signal modes. The training of features is time-consuming and labor-intensive because the fingerprints need to update every time when indoor environment changes. Model-based schemes universally establish empirical model to locate target, but the models are mostly un-robust in multiple experimental scenes. In this paper, we attempt to localize target in indoor environment based on Fresnel zones model of radio communication that could determine which elliptic region the target located in according to the phase of Channel State Information (CSI) obtained from Commercial Off-The-Shelf (COTS) Wi-Fi device. In addition, we focus on the interference on radio signal in indoor environment and propose specific solutions to remove phase offset and measurement error caused by insufficient hardware or background fluctuation. Therefore, our proposed system is more accurate compared with existing works. Results show the average error of our scheme is 33.6 cm in 47.6 m2 meeting room and 31.2 cm in 40.7 m2 office room while state-of-the-art work (MFDL) is 75 cm in 72 m2.

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.

A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals.

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.

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.

WallSense: Device-Free Indoor Localization Using Wall-Mounted UHF RFID Tags.

To achieve device-free indoor localization without the active participation of the users, this paper presents WallSense, a device-free indoor localization system based on off-the-shelf Radio RFID (Radio-Frequency Identification) equipment. The system deploys two orthogonal tag arrays in adjoining walls and uses the RSSI and phase information measured by RFID readers to localize the target. By differentiating the backscattered signal between adjacent tag pairs, WallSense is able to eliminate most undesirable factors and extract information directly related to the location of the target. By applying Particle Swarm Optimization (PSO) with a novel weighted fitness function and combining the localization result of two orthogonal tag arrays, the system is able to localize the target with high accuracy. Experiments show that the system is able to localize human target with 0.24 m median error. Also, WallSense has low deployment overhead and do not require the users to carry any devices.