Indoor Localization Scheme Research Articles

An INS/UWB fusion localization scheme for wireless sensor network

Abstract As the demand for indoor services relying on location information grows, achieving precise indoor positioning is becoming an urgent issue to address. The ultra-wide band (UWB) accuracy can reach centimeter level, but is susceptible to non-line-of-sight (NLOS) effects. The inertial navigation system (INS) is not affected by the environment, but the error will diverge over time. Therefore, this paper focuses on the UWB/INS combined positioning system, which can combine the advantages of both. We propose a loosely-coupled INS/UWB indoor localization scheme based on factor graph optimization (FGO). Firstly, an agglomerative hierarchical clustering based NLOS mitigation method is applied to preprocess the UWB raw measurement data. The position estimates under UWB are then obtained using an improved extended Kalman filter to mitigate NLOS errors and provide more accurate location data for combined navigation. Then, we build the IMU preintegration factor and the zero-bias factor. Finally, FGO is used to fuse the information to get the final location. Designed experiments demonstrate the superiority of the algorithm. The simulation and experimental results show that the proposed scheme outperforms the comparison algorithms.

A Novel Multimodal Feature-Level Fusion Scheme for High-Accurate Indoor Localization

A Novel Multimodal Feature-Level Fusion Scheme for High-Accurate Indoor Localization

Intelligent Geomagnetic Indoor Positioning System

In the past, several firefighters have died in disaster relief operations. Although the firefighters were fully equipped, the scene of the disaster was smoky and disorienting, making the firefighters unable to identify their location. The commander wanted to direct the firefighters outside but could not confirm the correct location of the firefighters, causing delays in rescue. GPS cannot support indoor positioning or preset indoor positioning facilities at the moment of fire extinguishing. However, geomagnetism is everywhere, and it can be used to identify one’s location. Unfortunately, due to the uncertainty of the magnetic field strength, indoor geomagnetism is affected by the building environment, and the existing magnetic positioning methods have difficulty obtaining a location. To solve this problem, we propose a new incremental indoor localization scheme based on the difference in geomagnetic intensity. The proposed method achieves indoor localization in 2D environments successfully. The novelty of our geomagnetic indoor positioning system is that it can perform indoor positioning without adding any indoor positioning facilities, and the accuracy can reach 0.8~1.5 m. This article aims to verify that the geomagnetic turbulence filtering algorithm can filter out abnormal geomagnetic intensity, that the incremental algorithm can estimate the position of human motion, and that geomagnetism can be used for indoor positioning without any preset infrastructure. The contribution of this paper is that we have developed a practical system that can be used without any infrastructure and can be used for indoor positioning with meter-level accuracy. The geomagnetic indoor positioning system can be integrated with a wireless network and applied to disaster relief.

D-Fi: Domain adversarial neural network based CSI fingerprint indoor localization

Deep learning based channel state information (CSI) fingerprint indoor localization schemes need to collect massive labeled data samples for training, and the parameters of the deep neural network are used as the fingerprints. However, the indoor environment may change, and the previously constructed fingerprint may not be valid for the changed environment. In order to adapt to the changed environment, it requires to recollect massive amount of labeled data samples and perform the training again, which is labor-intensive and time-consuming. In order to overcome this drawback, in this paper, we propose one novel domain adversarial neural network (DANN) based CSI Fingerprint Indoor Localization (D-Fi) scheme, which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment. Specifically, the previous environment and changed environment are treated as the source domain and the target domain, respectively. The DANN consists of the classification path and the domain-adversarial path, which share the same feature extractor. In the offline phase, the labeled CSI samples are collected as source domain samples to train the neural network of the classification path, while in the online phase, for the changed environment, only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domain-adversarial path to update parameters of the feature extractor. In this case, the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment. Experiment results show that for the changed localization environment, the proposed D-Fi scheme significantly outperforms the existing convolutional neural network (CNN) based scheme.

Denoising Autoencoder를 활용한 RSSI Fingerprinting 기반 실내 위치 인식 기법

Received Signal Strength Indication (RSSI)-based Fingerprinting scheme for indoor localization can be greatly affected by noise and signal distortion caused by walls, obstacles, and frequency interference in the room. Therefore, in this paper, to improve the performance of fingerprinting-based indoor localization, we propose a localization method employing a deep learning-based denoising algorithm, Denoising Autoencoder (DAE). DAE

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.

PaCNN-LSTM: A Localization Scheme Based on Improved Contrastive Learning and Parallel Fusion Neural Network

The deep learning technique plays an important role in Wi-Fi localization systems as it could mine deep features of measurement data. The main challenges are to combat the signal fluctuation resulting in decrease of sample discrimination, and to leverage the broadest information of sample measurements during the training phase, since they are directly related to the location accuracy and robustness. Hence, to address the above issues, this paper proposes an indoor Wi-Fi localization scheme which mainly contains two modules. Firstly, an improved contrastive learning is introduced to handle the sample signal measurements to increase the discrimination. It is from the perspective of learning and encoding, and avoids the drawbacks brought by traditional processing methods. Then, we build a parallel fusion network named as PaCNN-LSTM based on convolutional neural network (CNN) and long short-term memory network (LSTM). Compared with existing networks, PaCNN-LSTM connects neural networks in parallel rather than serial, which improves the generalization performance of model when extracting the spatial and temporal features of signal measurements. In addition, it also considers the large amount of middle layer information that is always ignored. By adding a Flatten layer after the pooling layer, the available information of samples has been broadened. Extensive experimental results show that the localization performance of the proposed scheme is outperformed than others, where the location accuracy is improved by about 22%.

Multi-Floor Indoor Localization Scheme Using a Seq2Seq-Based Floor Detection and Particle Filter With Clustering

Multi-Floor Indoor Localization Scheme Using a Seq2Seq-Based Floor Detection and Particle Filter With Clustering

Novel Robust Indoor Device-Free Moving-Object Localization and Tracking Using Machine Learning With Kalman Filter and Smoother

A novel robust device-free moving-object localization and tracking approach incorporating a machine-learning classifier with both Kalman filter and Kalman smoother is proposed in this work. The received signal-strength indicator (RSSI) maps for an arbitrary indoor geometry are simulated by the prevalent Feko channel-propagation emulator. Such instantaneous RSSIs of WiFi signals collected by different receivers indoors are acquired as a feature vector. The indoor geometry is first partitioned into a number of equisized zones, and then the underlying localization problem can be converted to the multiclassification problem. The RSSI feature vectors are collected as the training data for the gradient boosting decision-tree classifier. We propose a novel online tracking and offline localization approach using the advanced machine-learning technique regularized by Kalman filter and Kalman smoother, respectively. Simulation results demonstrate that our proposed new localization and tracking approach leads to the highest localization-accuracy of 73.1% over four test trajectories as the room is partitioned into 16 equisized zones. Moreover, our proposed new method also reaches the smallest average Euclidean-distance error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.33{\kern0.99594pt}\text{m}$</tex-math></inline-formula> . Our proposed novel indoor localization and tracking scheme would be very promising and convenient as it can well serve for devices free indoor surveillance in the future.

CrowdLoc: Robust image indoor localization with edge-assisted crowdsensing

Image indoor localization, which is advantageous in infrastructure-less deployment and high positioning accuracy among various indoor localization schemes, is usually deployed in a lightweight end (shooting picture) and heavyweight server (searching image for location in the database) manner. Such a scheme is suffered from two major drawbacks: (1) The uploading and searching of the shooting pictures cause a severe burden to the server. (2) The image map will become defected as time elapse, such as missing features in initialization maps and continuous changes in the scene. To keep the image map up-to-date, a labor-intensive site survey is required with dedicated devices. The frequent updating will further exhaust the limited resources in the server. These two drawbacks have greatly hindered the image-based indoor localization from large-scale deployment. To this end, we study how to use crowdsourcing data to mitigate map defects and optimize location computation and map storage through edge architecture. In this paper, we propose CrowdLoc, an edge-assisted image localization architecture with lightweight multi-view localization on clients and crowdsourcing map-cache on the edge. Our method is mainly innovative in three parts. Firstly, CrowdLoc provides a lightweight multi-view localization on the client-side and the low-confidence localization can be offloaded to the edge server to use the crowdsourcing map for more accurate calculation. Secondly, CrowdLoc uses a map defect perception algorithm to recognize the update of the scene during user localization and update the crowdsourcing map-cache automatically on the edge side. Thirdly, in order to improve cache utilization, we propose a cache elimination strategy that runs periodically on the edge side. It can classify cache data according to the frequency of cache hits on a regular basis, remove invalidation data, and persist long-term updated data to the client map to reduce clients’ computing offload. We evaluate the performance of our method based on the real data in the lobby of an experimental building with a map missing rate of 20% and a map update rate of 40%. The results show that the mean localization error of CrowdLoc is 1.54 meters, which is 59.4% lower than that of multi-view localization without crowdsourcing maps, and only 0.56 meters higher than localization in non-defective maps.

XGBLoc: XGBoost-Based Indoor Localization in Multi-Building Multi-Floor Environments.

Location-based indoor applications with high quality of services require a reliable, accurate, and low-cost position prediction for target device(s). The widespread availability of WiFi received signal strength indicator (RSSI) makes it a suitable candidate for indoor localization. However, traditional WiFi RSSI fingerprinting schemes perform poorly due to dynamic indoor mobile channel conditions including multipath fading, non-line-of-sight path loss, and so forth. Recently, machine learning (ML) or deep learning (DL)-based fingerprinting schemes are often used as an alternative, overcoming such issues. This paper presents an extreme gradient boosting-based ML indoor localization scheme, simply termed as XGBLoc, that accurately classifies (or detects) the positions of mobile devices in multi-floor multi-building indoor environments. XGBLoc not only effectively reduces the RSSI dataset dimensionality but trains itself using structured synthetic labels (also termed as relational labels), rather than conventional independent labels, that classify such complex and hierarchical indoor environments well. We numerically evaluate the proposed scheme on the publicly available datasets and prove its superiority over existing ML or DL-based schemes in terms of classification and regression performance.

Multiview Features Fusion and AdaBoost Based Indoor Localization on WiFi Platform

With the increasing demand for location-based service (LBS), WiFi-based localization has attracted considerable attentions due to the wide deployment and low cost of WiFi devices. However, most of existing approaches need to work within line-of-sight (LoS) range or require users to take dedicated devices. Thus, a device-free and passive indoor localization scheme is still desired. In this paper, a multi-view features fusion and AdaBoost-based indoor localization scheme by exploiting channel state information (CSI) of WiFi signals is proposed. To this end, we firstly formulate a shared consistent representation by using CSI measurements from all WiFi receivers, modeling the common properties among all views. Based on this, a features extraction is applied to the consistent representation for charactering channel properties. Meanwhile, these features extracted are optimized via a t-distributed stochastic neighbor embedding (t-SNE) algorithm for selecting the effective features, contributing to system performance. AdaBoost then builds a non-linear mapping between the CSI features and locations, realizing the purpose of localizing a user relying solely on WiFi signals with a high level of granularity, without any active engagement from the users. We implement a prototype on commodity WiFi devices and conduct comprehensive experiments in indoor scenarios. Based on real-world CSI measurements, the results confirm that the proposed scheme can achieve average localization errors of 0.8 m and 1.1 m in two indoor scenarios.

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks.

Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.

Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning

Due to recent technological developments such as online navigation, augmented reality (AR), virtual reality (VR), and digital twins, and the high demand from users for various location-based services (LBS), research on location estimation techniques is being actively conducted. As a result, there is an increasing demand for effective localization technologies that can be used in places where the use of Global Positioning System (GPS) is limited, especially in indoor spaces with very large areas. In this paper, a new structure for an indoor localization system in which wireless fingerprinting and visual-based positioning are hierarchically combined—the so-called Fi-Vi system—is proposed. This scheme consists of two steps: fingerprint-based localization (FBL) followed by visual-based localization (VBL). In the first positioning step (i.e., the FBL stage), the entire area of a significantly broad range for localization is divided into multiple regions, the size and the number of which depend on the target accuracy of this step. Moreover, a machine-learning (ML) or deep-learning (DL) model trained on a Wi-Fi fingerprint radio map selects suitable candidate regions among these multiple regions. In the second positioning step (i.e., the VBL stage), the final location is precisely estimated through visual-based positioning based on the received information regarding the candidate regions. The FBL stage uses a sparse radio map (SRM) for fingerprinting, which can be constructed with relatively little effort and cost compared to radio maps used in conventional fingerprinting methods. As a result, it can be easily combined with existing visual-based positioning methods with almost negligible implementation complexity. Because of the hierarchical structure and SRM, the proposed scheme shows a significant performance improvement in terms of computational load and time required for indoor localization compared to the use of the existing visual-based indoor positioning method alone. In addition, it provides high accuracy and robustness even in a dynamically changing indoor wireless environment where conventional wireless fingerprinting methods show significant performance degradation. Finally, the performance analysis of the proposed scheme was performed using the UJIIndoorLoc dataset. Experiments and theoretical analysis have shown that when the estimation accuracy of the candidate region for the test dataset was achieved at about 99% through the FBL stage, the average computational amount of the VBL stage for the final position estimation was only about 16% of that in cases where the visual-based positioning method was used alone.

Privacy-preserving indoor localization based on inner product encryption in a cloud environment

How to utilize the resource-rich cloud to provide scalable indoor localization service to solve the problem of limited user equipment resources while preventing privacy leakage has become a hot topic. A novel privacy-preserving indoor localization scheme based on Inner Product Encryption in a cloud environment is proposed to address this issue. By using the computability of Inner Product Encryption on ciphertext without exposing plaintext information, an honest-but-curious cloud server estimates the users location without obtaining any private information. The least-squares estimation algorithm of the Time Difference location system is decomposed into the basic form of the inner product of two sets of vectors. One is the anchor information encrypted with Inner Product Encryption during the offline phase, the other is the users distance difference information encrypted and sent to the cloud server during the online phase. To hide the users real location, the users real distance difference information and several decoy information are encrypted and included in a request to achieve k-Anonymity. The location estimation algorithm operates on the cloud to locate the user from the ciphertext. In addition, three optimization methods applied to cloud server location estimation algorithm were designed to further improve the performance. The experimental results and theoretical analysis show that the proposed scheme consumes less time, and has a lower communication overhead, while maintaining positioning accuracy. At the same time, the scheme protects the users locational and data privacy of the indoor positioning service provider when migrating the indoor positioning services to the cloud environment.

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.

Practical Privacy-Preserving Indoor Localization Based on Secure Two-Party Computation

We present a privacy-preserving indoor localization scheme based on received signal strength measurements, e.g., from WiFi access points. Our scheme preserves the privacy of both the client's location and the service provider's database by using secure two-party computation instantiated with known cryptographic primitives, namely, Paillier encryption and garbled circuits. We describe a number of optimizations that reduce the computation and communication overheads of the scheme and provide theoretical evaluations of these overheads. We also demonstrate the feasibility of the scheme by developing a proof-of-concept implementation for Android smartphones and commodity servers. This implementation allows us to validate the practical performance of our scheme and to show that it is feasible for practical use in certain types of indoor localization applications.

Corrections and Comments on “High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning”

This paper presents critical amendments to the subject paper. The names of the authors are included here, who should have been credited in the subject paper for their contribution in designing the hardware, facilitating the system development, and constructing the testbed.

Indoor 3D Localization Scheme Based on BLE Signal Fingerprinting and 1D Convolutional Neural Network

Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

High-Precision Multipath-Based Indoor Localization Scheme With User Privacy Protection for Dynamic NLoS Environments

High-precision indoor localization systems (ILSs) are critical for applications such as human smartphone navigation, autonomous robotics and automated warehouse and factory design. This paper presents a novel fingerprinting-based ILS, which features a decimeter-level localization accuracy, the ability to function in a constantly changing non line-of-sight (NLoS) environment, and user privacy protection without the need for heavy computations. The proposed ILS is able to maintain its localization accuracy in a constantly changing environment and to camouflage the user’s location by leveraging multipath propagation. The method was successfully tested both by experimental verification using the ultra-wideband communication standard and a ray-tracing simulation. An average localization error of 6 cm is demonstrated for a stationary or slow-moving receiver. An average error of 30 cm is demonstrated for a receiver that is moving at a fast walking pace. The obtained localization accuracy is comparable to the accuracy of the state-of-the-art localization algorithms. At the same time, the proposed approach solves two practical challenges faced by ILSs: robustness to changing environments with moving objects and the high computation requirements of user privacy protection. The high degree of user privacy was evaluated using a set of corresponding metrics.