Indoor Localization Method Research Articles

An ISAR-SAR Based Method for Indoor Localization Using Passive UHF RFID System With Mobile Robotic Platform

A novel RFID inverse synthetic aperture radar (ISAR)-synthetic aperture radar (SAR) localization method is proposed and demonstrated which exploits reference tags with known locations to estimate the trajectory of a mobile platform and locate target tags with unknown locations. A system that integrates a mobile robot with an integrated RFID reader and antenna is used to obtain phase measurements of both reference and target tags when the robot is moving along its trajectory. The ISAR algorithm estimates the antenna trajectory and a novel ISAR-SAR loop then adjusts the estimated trajectory. The estimated trajectory is combined with phase measurements of the target tags to determine their location. The performance is compared to SAR using a LiDAR measured antenna trajectory. Similar localization accuracy (15 cm MAE) is achieved using LiDAR-SAR or ISAR-SAR method with a straight-line trajectory and higher accuracy (6 cm) is achieved using ISAR-SAR method than LiDAR-SAR method (8 cm) with an L-shape trajectory.

Multi-Floor Indoor Localization Based on RBF Network With Initialization, Calibration, and Update

Received Signal Strength Indicator (RSSI) fingerprinting is known as the most concerned method for indoor localization as its high accuracy and low cost. Numerous RSSI based methods have shown their attractive performances, but the major drawback is the high dependency on the database. In this paper, we propose a multi-floor indoor localization method which includes floor detection and location estimation based on the radial basis function (RBF) network. To ensure the localization accuracy and stability, the network is constructed according to the probabilistic algorithm. Choosing Gaussian radial basis functions with appropriate widths, the network parameters can be initialized appropriately regardless of deficiency of RSSI data. By further conducting the supervised learning of RBF network, the network parameters will be effectively calibrated and updated, so as to achieve a better localization performance. In addition, a radio map quality evaluation criterion is proposed to conduct a comprehensive analysis and interpretation for the localization approach. Finally, experimental results of a publicly accessible dataset which includes multi-floors buildings verify that the performance of the proposed RBF network is superior to other commonly used methods.

CTSLoc: an indoor localization method based on CNN by using time-series RSSI

CTSLoc: an indoor localization method based on CNN by using time-series RSSI

Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions.

Time difference of arrival (TDOA) based indoor ultrasound localization systems are prone to multiple disruptions and demand reliable, and resilient position accuracy during operation. In this challenging context, a missing link to evaluate the performance of such systems is a simulation approach to test their robustness in the presence of disruptions. This approach cannot only replace experiments in early phases of development but could also be used to study susceptibility, robustness, response, and recovery in case of disruptions. The paper presents a simulation framework for a TDOA-based indoor ultrasound localization system and ways to introduce different types of disruptions. This framework can be used to test the performance of TDOA-based localization algorithms in the presence of disruptions. Resilience quantification results are presented for representative disruptions. Based on these quantities, it is found that localization with arc-tangent cost function is approximately 30% more resilient than the linear cost function. The simulation approach is shown to apply to resilience engineering and can be used to increase the efficiency and quality of indoor localization methods.

Fingerprint Feature Extraction for Indoor Localization.

This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.

Single LED positioning scheme based on angle sensors in robotics.

Indoor robotic localization is one of the most active areas in robotics research nowadays. Visible light positioning (VLP) is a promising indoor localization method, as it provides high positioning accuracy and allows for leveraging the existing lighting infrastructure. Apparently, accurate positioning performance is mostly shown by the VLP system with multiple LEDs, while such strict requirement of LED numbers is more likely to lead to VLP system failure in actual environments. In this paper, we propose a single-LED VLP system based on image sensor with the help of angle sensor estimation, which efficiently relaxes the assumption on the minimum number of simultaneously captured LEDs from several to one. Aiming at improving the robustness and accuracy of positioning in the process of continuous change of robot pose, two methods of visual-inertial message synchronization are proposed and used to obtain the well-matched positioning data packets. Various schemes of single-LED VLP system based on different sensor selections and message synchronization methods have been listed and compared in an actual environment. The effectiveness of the proposed single-LED VLP system based on odometer and image sensor as well as the robustness under LED shortage, handover situation and background non-signal light interference, are verified by real-world experiments. The experimental results show that our proposed system can provide an average accuracy of 2.47 cm and the average computational time in low-cost embedded platforms is around 0.184 s.

Comparison of Direct Intersection and Sonogram Methods for Acoustic Indoor Localization of Persons.

We discuss two methods to detect the presence and location of a person in an acoustically small-scale room and compare the performances for a simulated person in distances between 1 and 2 m. The first method is Direct Intersection, which determines a coordinate point based on the intersection of spheroids defined by observed distances of high-intensity reverberations. The second method, Sonogram analysis, overlays all channels’ room impulse responses to generate an intensity map for the observed environment. We demonstrate that the former method has lower computational complexity that almost halves the execution time in the best observed case, but about 7 times slower in the worst case compared to the Sonogram method while using 2.4 times less memory. Both approaches yield similar mean absolute localization errors between 0.3 and 0.9 m. The Direct Intersection method performs more precise in the best case, while the Sonogram method performs more robustly.

Indoor camera pose estimation via style‐transfer 3D models

AbstractMany vision‐based indoor localization methods require tedious and comprehensive pre‐mapping of built environments. This research proposes a mapping‐free approach to estimating indoor camera poses based on a 3D style‐transferred building information model (BIM) and photogrammetry technique. To address the cross‐domain gap between virtual 3D models and real‐life photographs, a CycleGAN model was developed to transform BIM renderings into photorealistic images. A photogrammetry‐based algorithm was developed to estimate camera pose using the visual and spatial information extracted from the style‐transferred BIM. The experiments demonstrated the efficacy of CycleGAN in bridging the cross‐domain gap, which significantly improved performance in terms of image retrieval and feature correspondence detection. With the 3D coordinates retrieved from BIM, the proposed method can achieve near real‐time camera pose estimation with an accuracy of 1.38 m and 10.1° in indoor environments.

Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning Based Approach

For indoor localization system, the integration of various fingerprints could intuitively improve localization accuracy since different fingerprints complement each other. In spite of the potential benefits, however, the limitation of applying various fingerprints in improving localization accuracy still remains unknown. Moreover, how to design efficient indoor localization methods through fully exploiting the features of different fingerprints is to be explored as well. In this work, we investigate the location error of a fingerprint-based indoor system with the application of hybrid fingerprints. It manifests that the location error is dependent on correlation coefficient of different types of fingerprints. For instance, the exploiting correlation between different types of fingerprints is shown to effectively reduce the location error, which gradually decreases with the number of adopted fingerprints. On this basis, we propose a hybrid received signal strength (RSS) and channel state information (CSI) localization algorithm (HRC), which is designed based on deep learning. The HRC fully exploits quick construction of fingerprint database with coarse-grained RSS and rich multipath information of fine-grained CSI. The RSS and CSI with high correlation are selected to construct fingerprint database, aiming to improve localization accuracy. Moreover, a deep auto-encoder is used to reduce the computation complexity and the deep neural network is trained for location estimation. Experimental results, which are obtained by designed indoor localization system, validate that the location error of HRC can be reduced by 77.3% and 20.3%, compared with the existing localization methods and HRC without RSS/CSI selection by correlation coefficient, respectively.

A High-Accuracy Indoor Localization System and Applications Based on Tightly Coupled UWB/INS/Floor Map Integration

High-accuracy localization systems for non-line-of-sight (NLOS) environments have been a popular focus of research on indoor positioning. In particular, the use of ultra-wideband (UWB) technology for ranging is emerging as an effective method of indoor localization due to its high accuracy. To improve the overall performance of UWB systems, combining UWB technology with an inertial navigation system (INS) is a commonly used indoor positioning approach. However, UWB measurements are affected by outliers under the NLOS propagation of radio signals. In addition, INS tend to suffer from error accumulation without constraints imposed by observations from other sensors. To address these problems, an adaptive robust extended Kalman filter algorithm based on tightly coupled UWB/INS/floor map integration is proposed, with which the noise in the UWB observations can be adaptively adjusted. The integrated system performs NLOS identification based on a map line segment matching algorithm; then, abnormal observations are abandoned, or their weights are reduced to prevent the error accumulation of the INS from being incorrectly constrained. Finally, an evaluation based on experimental data shows the significant enhancement achieved with the proposed algorithm based on the integration of UWB technology, an INS and a floor map, which can achieve a 2-D positioning accuracy that corresponds to a root mean square error of 0.27 m. Thus, substantial improvements can be achieved for long-term operation in NLOS environments using the proposed integration scheme.

A Visual and VAE Based Hierarchical Indoor Localization Method.

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

SIABR: A Structured Intra-Attention Bidirectional Recurrent Deep Learning Method for Ultra-Accurate Terahertz Indoor Localization

High-accuracy localization technology has gained increasing attention in gesture and motion control and many diverse applications. Due to multi-path fading and blockage effects in indoor propagation, 0.1m-level precise localization is still challenging. Promising for 6G wireless communications, the Terahertz (THz) spectrum provides multi-GHz ultra-broad bandwidth. Applying the THz spectrum to indoor localization, the channel state information (CSI) of THz signals, including angle of arrival (AoA), received power, and delay, has unprecedented resolution that can be explored for positioning. In this paper, a Structured Intra-Attention Bidirectional Recurrent (SIABR) deep learning method is proposed to solve the CSI-based three-dimensional (3D) THz indoor localization problem with significantly improved accuracy. As a two-level structure, the features of individual multi-path rays are first analyzed in the recurrent neural network with the attention mechanism at the lower level. Furthermore, the upper-level residual network (ResNet) of the constructed SIABR network extracts hidden information to output the geometric coordinates. Simulation results demonstrate that the 3D localization accuracy in the metric of mean distance error is within 0.25m. The developed SIABR network has very fast convergence and is robust against THz indoor line-of-sight blockage, multi-path fading, channel sparsity and CSI estimation error.

초음파센서를 이용한 UKF 기반 로봇의 실내 위치 평가 방법

This paper proposes a UKF-Based indoor localization method that evaluates the optimal position of a robot by fusing the position information from encoders and the distance information of the obstacle measured by ultrasonic sensors. UKF is a method of evaluating the robot’s position by transforming optimal sigma points extracted using the unscented transform and is advantageous for the localization of a nonlinear system. To solve the problem of the specular reflection effect of ultrasonic sensors, we propose a validation gate that evaluates the reliability of the ranges measured by sonar sensors, that can maximize the quality of the position evaluation. The experimental results showed that the method is stable and convergence of the position error regardless of the size of the initial position error and the length of the sampling time.

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting.

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

Improvements of an Optical Scanning System for Indoor Localization Based on Defuzzification Methods

Nowadays, artificial intelligence (AI) has revolutionized the industry and made it possible to develop systems that ensure safety in human-robot collaboration. Mobile and industrial robots need a point of reference in their orientation for avoiding obstacles. Device and sensor technologies, such as acoustic sensors, Global Positioning System (GPS), light detection and ranging (LiDAR), accelerometers, and digital cameras play an important role in providing high resolution and sensitivity to solve complex problems in the industry. However, their cost and the need of applying signal processing techniques to filter their signals may not offer the required reliability for critical applications, such as indoor localization methods for industrial robots. The optical scanning systems (OSS) are devices that can be used instead at a lower cost, compared to the costs of devices previously mentioned. Nevertheless, there is still the challenge of avoiding the filtering of the signal. In this paper, a method to determine the energy center from sensors of an OSS using AI techniques is introduced. It uses defuzzification algorithms to accomplish accuracy measurements for localization and navigation tasks. The advantage of this method is that it eliminates the need of using the filtering process. One of the main contributions of this work is that the results can be used to implement an accurate and efficient indoor localization system, which can be used by an autonomous vehicle, such as industrial robots. According to the statistical analysis carried out, the minimal difference between the real position and the position estimated by the OSS was 0.021°.

BERT-ADLOC: A secure crowdsourced indoor localization system based on BLE fingerprints

Crowdsourced indoor localization methods have grasped much attention in recent years as a method of reducing the cost of constructing the fingerprint database. In a crowdsourcing environment, however, the localization system is vulnerable to malicious attacks, which possibly lead to serious localization errors. In this paper, we conclude the potential attacks during fingerprint database updates and online inference phases and propose a secure indoor crowdsourced localization system, BERT-ADLOC, based on BLE fingerprints. Our system consists of two main parts: adversarial sample discriminator BERT-AD and indoor localization model BERT-LOC. Our proposed BERT-AD recognizes fake fingerprints during the database update phase, while BERT-LOC defends against attacks online, in which valid beacons are moved or malicious beacons are deployed. A tailored BERT model is introduced to extract deep hidden features through the self-attention mechanism. Our experiments show that BERT-ADLOC achieves a good localization performance against adversaries both in the fingerprint database update phase and online inference phase.

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction.

With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.

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.

Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization

The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.

Variational Bayesian Adaptive Unscented Kalman Filter for RSSI-based Indoor Localization

Most existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This paper investigates the variational Bayesian adaptive unscented Kalman filtering (VBAUKF) for received signal strength indication (RSSI) based indoor localization under inaccurate process and measurement noise covariance matrices. First, an inaccurate and slowly varying measurement noise covariance matrix can be estimated by choosing appropriate conjugate prior distribution for an indoor localization model with inaccurate process and measurement noise covariance matrices. By choosing inverse Wishart priors distribution, the state, predicted error and measurement noise covariance matrices are inferred on each time separately. Second, a parameter optimization algorithm is designed to minimize the localization error of VBAUKF until it less than the threshold set in advance. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed filtering method for indoor localization.