Indoor Localization Technology Research Articles

3D Indoor Position Estimation Based on a UDU Factorization Extended Kalman Filter Structure Using Beacon Distance and Inertial Measurement Unit Data.

The development of the GPS (Global Positioning System) and related advances have made it possible to conceive of an outdoor positioning system with great accuracy; however, for indoor positioning, more efficient, reliable, and cost-effective technology is required. There are a variety of techniques utilized for indoor positioning, such as those that are Wi-Fi, Bluetooth, infrared, ultrasound, magnetic, and visual-marker-based. This work aims to design an accurate position estimation algorithm by combining raw distance data from ultrasonic sensors (Marvelmind Beacon) and acceleration data from an inertial measurement unit (IMU), utilizing the extended Kalman filter (EKF) with UDU factorization (expressed as the product of a triangular, a diagonal, and the transpose of the triangular matrix) approach. Initially, a position estimate is calculated through the use of a recursive least squares (RLS) method with a trilateration algorithm, utilizing raw distance data. This solution is then combined with acceleration data collected from the Marvelmind sensor, resulting in a position solution akin to that of the GPS. The data were initially collected via the ROS (Robot Operating System) platform and then via the Pixhawk development card, with tests conducted using a combination of four fixed and one moving Marvelmind sensors, as well as three fixed and one moving sensors. The designed algorithm is found to produce accurate results for position estimation, and is subsequently implemented on an embedded development card (Pixhawk). The tests showed that the designed algorithm gives accurate results with centimeter precision. Furthermore, test results have shown that the UDU-EKF structure integrated into the embedded system is faster than the classical EKF.

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.

5G Indoor Positioning Error Correction Based on 5G-PECNN.

With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is affected by noise, multipath propagation interference, installation errors, and other factors, leading to errors in 5G indoor positioning. This paper aims to address these issues by first constructing a 5G indoor positioning dataset and analyzing the characteristics of 5G positioning errors. Subsequently, we propose a 5G Positioning Error Correction Neural Network (5G-PECNN) based on neural networks. This network employs a multi-level fusion network structure designed to adapt to the error characteristics of 5G through adaptive gradient descent. Experimental validation demonstrates that the algorithm proposed in this paper achieves superior error correction within the error region, significantly outperforming traditional neural networks.

Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms

With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning.

HyTra: Hyperclass Transformer for WiFi Fingerprinting-based Indoor Localization

The emerging demand for a variety of novel Location-based Services (LBS) by consumers and industrial users is driven by the rapid and extensive proliferation of mobile smart devices. Sensors embedded in smart devices or machines provide wireless connectivity and Global Positioning System (GPS) capability, and are co-utilized to acquire location-linked data which are algorithmically transformed into reliable and accurate location estimates. GPS is a mature and reliable technology for outdoor localization but indoor localization in a complex multi-storey building environment remains challenging due to fluctuations in wireless signal strength arising from multipath fading. Location-linked data from wireless access points (WAPs) such as received signal strength (RSS) are acquired as numerical sequences. By conceptualizing a fixed order sequence of WAP measurements as a sentence where the RSS from each WAP are words, we may leverage on recent advances in artificial intelligence for natural language processing (NLP) to enhance localization accuracy and improve robustness against signal fluctuations. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer neural network which learns the relative positions of wireless access points (WAPs) through multiple learnable embeddings. We propose a second network, HyTra-HF, which improves upon HyTra by applying a hierarchical relationship between location classes. We test our proposed networks on public and private datasets varying in sizes. HyTra-HF outperforms existing deep learning solutions by obtaining 96.7\% accuracy for the floor classification task on the UJIIndoorloc dataset. HyTra-HF is amenable to deep model compression and achieves accuracy of 95.95\% with over ten-fold reduction in model size using Sparsity Aware Orthogonal (SAO) initialization and has the best-in-class accuracy for the sparse model.

Current Status and Future Trends of Meter-Level Indoor Positioning Technology: A Review

High-precision indoor positioning technology is regarded as one of the core components of artificial intelligence (AI) and Internet of Things (IoT) applications. Over the past decades, society has observed a burgeoning demand for indoor location-based services (iLBSs). Concurrently, ongoing technological innovations have been instrumental in establishing more accurate, particularly meter-level indoor positioning systems. In scenarios where the penetration of satellite signals indoors proves problematic, research efforts focused on high-precision intelligent indoor positioning technology have seen a substantial increase. Consequently, a stable assortment of location sources and their respective positioning methods have emerged, characterizing modern technological resilience. This academic composition serves to illuminate the current status of meter-level indoor positioning technologies. An in-depth overview is provided in this paper, segmenting these technologies into distinct types based on specific positioning principles such as geometric relationships, fingerprint matching, incremental estimation, and quantum navigation. The purpose and principles underlying each method are elucidated, followed by a rigorous examination and analysis of their respective technological strides. Subsequently, we encapsulate the unique attributes and strengths of high-precision indoor positioning technology in a concise summary. This thorough investigation aspires to be a catalyst in the progression and refinement of indoor positioning technologies. Lastly, we broach prospective trends, including diversification, intelligence, and popularization, and we speculate on a bright future ripe with opportunities for these technological innovations.

Visible Light Positioning as a Next-Generation Indoor Positioning Technology: A Tutorial

Visible Light Positioning as a Next-Generation Indoor Positioning Technology: A Tutorial

Retracted: Indoor and Outdoor Seamless Positioning Technology Based on Artificial Intelligence and Intelligent Switching Algorithm

Retracted: Indoor and Outdoor Seamless Positioning Technology Based on Artificial Intelligence and Intelligent Switching Algorithm

Studies on indoor positioning algorithms using BLE Beacons

Global positioning systems have become very popular, helping locate people and objects. Currently, with greater specificity, indoor location services are gaining more relevance. Despite the recent developments, the technology for indoor localization is still incipient, demanding better accuracy, lower costs, and greater practicality. Therefore, this paper presents a study of a technique for indoor localization with Bluetooth low-energy beacons. In addition, we propose and perform experiments to implement a low-cost, low-power, BLE-enabled device-based indoor positioning system. With such devices, we calculate the distance between the emitters and the receiver using known techniques and determine the user’s location. The results we obtained reduced the time and complexity of calibrating the offline phase of the systems. However, the accuracy of the localization is related to the accuracy of the distance estimation.

Analysis of Daily Application of Indoor Positioning Technology Based on TDOA

As a matter of fact, under the background of the rapid development of the current communication field, positioning technology is an important and valuable research direction in recent years. At the same time, indoor positioning is a relatively new and popular research topic of positioning technology contemporarily. With this in mind, this paper proposes to apply TDOA (Time Difference of Arrival) positioning technology to the positioning of indoor personnel in actual scenarios. In addition, this study attempts to apply TDOA positioning technology to the practical problems of personnel search as well as rescue in disaster scenarios. According to the analysis, the feasibility of these schemes is systematically analyzed and discussed. Moreover, the current defects as well as prospects are demonstrated at the same time. Overall, these results shed light on guiding further exploration of positioning technology.

Research on Intelligent Indoor Positioning Technology based on 5G

5G, as the first mobile communication system that takes positioning service as one of the design goals, introduces a variety of new technologies to improve the communication performance of the system and the accuracy and applicability of wireless positioning. However, there are still some problems in 5G indoor positioning, such as low positioning accuracy and poor switching between indoor and outdoor positioning. In order to solve these problems, further research and improvement of positioning technology is needed. In summary, 5G, as the first mobile communication system that takes positioning service as one of its design goals, introduces a variety of new technologies to improve the accuracy and applicability of positioning. However, there are still some problems to be solved, such as improving indoor positioning accuracy and achieving continuous and seamless coverage for indoor and outdoor positioning. The future development trend is positioning integration and convergence, and the introduction of collaborative positioning techniques to further improve the performance of positioning. In this paper, by analyzing the current status of 5G indoor positioning, it leads to the fact that how to improve the indoor positioning accuracy and achieve continuous and seamless coverage of indoor and outdoor positioning are urgent problems in the field of positioning, for which, firstly, we analyze the positioning techniques under the ultra-dense networking for 5G, the AOA, TDOA positioning techniques, and uplink and downlink positioning techniques. Finally, it is pointed out that positioning integration and convergence is the trend of future 5G positioning development, and then the necessity and feasibility of introducing collaborative positioning technology is pointed out.

An ultra wideband (UWB) localization system for automatic guided vehicles (agvs) in smart warehouses

As the main component of indoor positioning technology, UWB technology plays an important role in promoting the development of indoor and outdoor seamless positioning technology. This paper presents a proposed localization system of a propagation channel measurement campaign performed in a smart warehouse environment whose dimension is 100 meters * 100 meters and provides an extensive model for this environment. Three different arrangements of basestations are simulated to analyze the effects of basestation constellations and virtual racks are set up to determine the approximate line-of-sight (LOS) probability. At the same time, by considering the influences of varying transmit power, using weighting and using two-way ranging, the distribution of errors and the reliability threshold are drawn and the required bandwidths are determined. Finally, according to the simulation results, the optimal layout and modulation parameters are recommended, and improvements in channel models and algorithms for future work are concluded.

An Ensemble Filter for Indoor Positioning Technology of Mobile Home Service with Agile iBeacon Deployment

In this article, we undertake a thorough investigation into the theory of indoor location, adopt an effective and quick positioning algorithm, and make use of a network of lowpower iBeacons. An iBeacon-based indoor positioning system (IPS) is presented to investigate how to utilize iBeacon for accurate location and whether it can successfully replace the current dominant positioning technology based on the analysis that was conducted. However, the first things that should be taken into account at this point in the design of house robots for indoor environments are how to quickly and precisely gather target node location information as well as how to regulate and plan a course. This article examined the more popular and often used indoor positioning techniques, provided a succinct summary of current indoor positioning technologies and regulators, and examined ultrasonic locating technology in depth. Based on this, a system for mobile home service robots was developed, and simulation tests were performed to assess the accuracy of node locating, node reception and arrival times, the best level of route planning, and navigation and path estimation errors in both absolute and comparative terms. Additionally, we go into detail about the difficulties in developing a practical IPS, the solutions that are already available, a thorough performance comparison, and some potential future IPS development trends.

A Hybrid Indoor Positioning System Based on Visible Light Communication and Bluetooth RSS Trilateration.

Indoor positioning has become an attractive research topic because of the drawbacks of the global navigation satellite system (GNSS), which cannot detect accurate locations within indoor areas. Radio-based positioning technologies are one major category of indoor positioning systems. Another major category consists of visible light communication-based solutions, as they have become a revolutionary technology for indoor positioning in recent years. The proposed study intends to make use of both technologies by creating a hybrid indoor positioning system that uses VLC and Bluetooth together. The system first collects the initial location information based on VLC proximity, then collects the strongest Bluetooth signals to determine the receiver's location using Bluetooth RSS (received signal strength) trilateration. This has been inspired by the fact that there have not been any studies that make use of both technologies with the same positioning algorithm, which can lead to pretty high accuracy of up to 0.03 m.

Enhanced Pedestrian Navigation on Smartphones With VLP-Assisted PDR Integration

Pedestrian dead-reckoning (PDR) is a potential indoor localization technology that utilizes the inertial measurement unit (IMU) to estimate location. However, one of its most significant drawbacks is the accumulation measurement error. This paper proposes a PDR system integrated with visible light positioning (VLP) that boasts low accumulated error and can achieve real-time and accurate indoor positioning using both the IMU and the camera sensor on a smartphone. A multi-frame fusion method for LED-ID decoding is introduced in the encoding and decoding process of the system, achieving 98.5% decoding accuracy with a 20-bit-long LED-ID at the height of 2.1 m. This method also accommodates the variation in the shutter speeds of cameras and heights of the LED. Meanwhile, absolute locations and step length could be further calibrated using a single light-emitting diode (LED), providing an average accuracy within 0.5 meters over a 108-meter walk.

Indoor and Outdoor Fusion Positioning Technology Based on BeiDou Satellite Navigation

In order to solve the inaccuracy and discontinuity of BeiDou differential indoor and outdoor navigation and positioning, this article highlights an indoor and outdoor fusion positioning method based on BeiDou differential and WIFI. This positioning method combines the micro-inertial navigation positioning technology with a single positioning method such as Bluetooth, WIFI fingerprint, ZigBee, ultra-wideband UWB, etc., to correct the BeiDou pseudo range equation to reduce the spatial pseudo range error. And it can integrate BeiDou differential pseudo range equation and linear filtering algorithm to achieve precise positioning of navigation. The research results show that the method realizes the positioning of the indoor and outdoor interactive area by fusing the satellite pseudo-range equation and the base station pseudo-range equation. And it realizes the seamless integration of indoor and outdoor precise positioning, which improves the positioning accuracy and reliability.

Adaptive map matching based on dynamic word embeddings for indoor positioning

Map matching has been widely used in various indoor localization technologies. However, conventional map matching technologies based on probabilistic models, such as particle filter (PF), still have a series of limitations, such as underutilization of map information, poor generalization, and relatively low precision. To improve the performance of PF-based map matching technique, this paper proposes MapDem, a novel map matching model fusing dynamic word embeddings and Variational Autoencoder (VAE) to improve matching performance significantly. The key to our approach is to extract map information using dynamic word embeddings to represent each reachable point on the map as word vectors with allowable oriented trajectory information. The same point has different representations on different trajectories so that MapDem can adaptively learn the contextual information of map for position estimation. Unlike traditional particle filters, MapDem focuses on the learning of particle sets distribution by a statistical model, Variational Autoencoder (VAE), followed by estimating position with combined current and previous sequence information. Extensive experiments have been conducted with 610 trajectories in three real-world scenarios. Numerical results demonstrate the adaptability of MapDem which works equally well in all three different scenarios, outperforming traditional particle filters by 18% on average.

TriLoc: Toward Accurate Indoor Localization With Assistance of Microwave Reflections

With the development of the Internet of Things (IoT), indoor localization technology plays a vital role in smart factories, smart cities, and other fields. When using the network infrastructures such as Wi-Fi, some conventional localization technologies usually need multiple access points (APs), which involve time and data synchronizations. As a result, those drawbacks hinder the deployment of those localization technologies. In this article, we propose TriLoc, a single AP-based indoor localization system with the assistance of microwave reflections, which has the minimum overheads compared to the existing schemes. The insight is that the reflection paths and the direct path experience the same phase errors, and thus, it can be used for locating the user device using only a single AP. In the system, we first estimate the angle of arrivals (AoA) and time of flight (ToF) of propagation paths and then use the multipath triangle that consists of one first-order reflection path and the direct path to locate the user device. Finally, we construct the prototype using software-defined radio and evaluate it in various indoor environments. The experimental results show that TriLoc can achieve submeter localization accuracy using only a single AP.

Indoor and Outdoor Seamless Positioning Technology Based on Artificial Intelligence and Intelligent Switching Algorithm

Navigation and positioning is a new growth point of wireless services. And seamless positioning is the current development trend of navigation and positioning services. In view of the problems that dynamic handover cannot be performed during site movement, STA only selects the optimal AP according to the indication of received signal strength, and the handover delay is too long. This paper proposes a research on indoor and outdoor seamless positioning technology based on artificial intelligence and intelligent switching algorithm. The purpose is to study the influence of reducing the positioning data with too large error on the final positioning result. The method of this paper is to propose an intelligent switching algorithm and then discuss the research status and development direction of indoor positioning technology and outdoor positioning technology, respectively, and point out the switching methods between them. The role of these methods is to analyze the current situation of indoor and outdoor navigation technology, especially the development route of seamless positioning technology. It determines the strategy of integrating heterogeneous positioning systems to build a seamless positioning system to maximize the use of existing positioning system resources. This paper studies the design and implementation of the seamless positioning system through the simulation experiment of the intelligent switching algorithm and finally tests the system through the experimental vehicle. The online experiment results show that the system can achieve high positioning accuracy in indoor and outdoor environments, especially at its junction, and the positioning errors in all directions are within 0.2 m.

Intelligent Indoor Localization Algorithm Based on Channel State Information

With the deepening application of wearable devices in the field of indoor positioning, the mining and application of human behavior patterns based on new technologies such as mobile terminals, channel technologies and intelligent algorithms have gradually become a research hotspot in the field of intelligent positioning. At this stage, outdoor positioning satellite technology has been gradually improved, but it cannot work effectively in indoor environment, and indoor positioning technology has not formed a set of standard and effective scheme at this stage, which leads to a scene of a hundred contentions for indoor positioning technology. This paper proposes an indoor intelligent localization algorithm based on channel state information, which first forms the measurements of antenna subarrays by the fixed antenna subset arrays located in different paths to achieve the phase estimation based on channel state; then determines the AoA of the direct path from the target to the AP using ToF information and eliminates the STO noise of channel state information by unfolding the best linear fit of CSI phase; again the AoA and ToF estimates from multiple measurements are plotted in two-dimensional space, and the direct path possibility estimation is achieved by identifying the estimates through five clusters of Gaussian-averaged clustering algorithm; finally the coordinates of the target to be located are obtained by coupling direct propagation path, estimation clustering and least-squares estimation using three APs as the receiver and the target to be located as the transmitter. The simulation results show that the intelligent localization algorithm proposed in this paper is able to lack the localization accuracy at the decimeter level in the indoor localization environment with three fixed APs, and the average error is better than the localization algorithms of support vector machine, deep learning and limit learning.