Wireless Indoor Positioning System Research Articles

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

A Non-Line-of-Sight Wireless Indoor Localization System Using Custom-Designed Radio Relay and Uniform Circular Array

Indoor localization using WiFi signals, though getting studied for many years, is not adopted extensively in real indoor environments yet. One possible impediment is that many prior systems perform worse in non-line-of-sight (NLoS) indoor scenarios than in line-of-sight (LoS) scenarios or even fail to work. Nevertheless, realizing localization in both LoS and NLoS scenarios is indispensable for a viable system working in indoor environments. Since existing state-of-the-art solutions mainly aim to localize a target in LoS indoor scenarios, we present a system that uses multipath channel parameters to achieve localization with a single AP in indoor NLoS scenarios. The proposed introduces deployable and simple custom-designed radio relays to assist localization, which can receive signals from the target and forward them to the AP in NLoS scenarios. Field trials conducted in a real NLoS scenario show that our system achieves a median 3-D localization accuracy of 86 cm with a uniform circular array (UCA) in most NLoS conditions, i.e., the direct path from a target to AP is not completely blocked. More importantly, even if in a challenging NLoS condition, i.e., the direct path does not exist, our system can still achieve a median 2-D localization accuracy of 85 cm.

Optimizing Anchor Node Deployment for Fingerprint Localization With Low-Cost and Coarse-Grained Communication Chips

A part of off-the-shelf wireless communication chips can only provide very coarse-grained information of the distance range between the transmitter and receiver. Exploiting such chips for precise wireless indoor positioning (WIP) becomes very valuable since their costs are comparatively low. For the WIP systems built on the coarse-grained communication chips, this article discusses the optimal anchor node deployment problem for fingerprint localization. The problem is very challenging since it implies three particularly important requirements of adaptive localization precision, unique fingerprint, and a minimum number of anchor nodes. To minimize the required number of anchor nodes while satisfying the three requirements, we propose two anchor node deployment algorithms, namely, ANDAs and WANDA, both of which are developed for mediate and large-area deployment, respectively. We first prove the effectiveness of the chip in maintaining robust fingerprints and formulate the deployment optimization problem as a minimum attribute reduction problem. Then, to deal with the high computational complexity involved, ANDA is proposed by significantly reducing the search space of the optimization problem. To practically address the combination explosion problem for large-area deployment, the idea of area partitioning is adopted. Investigating anchor nodes at different grid vertices in subareas that may have different sharing degrees, WANDA is proposed to achieve an overall anchor node deployment optimization. Simulation and experimental results demonstrate the validity and the superiority of ANDA and WANDA over counterparts. For large-area deployment, compared to ANDA, WANDA can further decrease the total amount of anchor nodes by 17%.

Machine learning in indoor visible light positioning systems: A review

Developing a wireless indoor positioning system with high accuracy, reliability, and reasonable cost has been the focus of many researchers. Recent studies have shown that visible-light-based positioning (VLP) systems have better positioning accuracy than radio-frequency-based systems. A notable highlight of those research articles is their combination of VLP and machine learning (ML) to improve the positioning performance in both two-dimensional and three-dimensional spaces. In this paper, in addition to describing VLP systems and well-known positioning algorithms, we analyze, evaluate, and summarize the ML techniques that have been applied recently. We break these into four categories: supervised learning, unsupervised learning, reinforcement, and deep learning. We also provide deep discussion of articles published during the past five years in terms of their proposed algorithm, space (2D/3D), experimental method (simulation/experiment), positioning accuracy, type of collected data, type of optical receiver, and number of transmitters.

Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System

In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations.

Applications of Wireless Indoor Positioning Systems and Technologies in Underground Mining: a Review

Applications of Wireless Indoor Positioning Systems and Technologies in Underground Mining: a Review

Performance Improvement Design of Bluetooth Low Energy-Based Wireless Indoor Positioning Systems

The systematic design of wireless indoor positioning systems can offer another essential approach to achieving the required performance objectives aside from using suitable location determination algorithms. This manuscript investigates Bluetooth Low Energy- (BLE-) based wireless indoor positioning systems and how adjusting the system design parameters can affect their location determination performance. Without placing emphasis on sophisticated location determination algorithms, this work provides guidelines for how a system designer can control the balance among multiple positioning performance metrics. For example, a balance between the number of installed reference nodes and the accuracy performance can be chosen to control deployment costs, such as the installation expense, infrastructure expense, and installation time. To demonstrate our baseline study, we compare three different designs of BLE wireless indoor positioning system that utilize location determination algorithms based on proximity, trilateration, and scene analysis. These designs are also compared over two different building sizes, which are medium and large. The design model and performance analysis data were based on our actual implementation of the hardware and software system for a BLE wireless indoor positioning system. Specifically, the received signal strength indication data were collected from our prototype reference nodes. The findings from our study indicated that a proximity-based system can only provide fair location accuracy performance (average error distance of 5 m to 7 m) making it unsuitable for applications that require high accuracy. For medium location accuracy performance (average error distance of 3 m to 5 m), the trilateration-based system can achieve the highest efficiency in terms of number of installed reference nodes over the accuracy. The trilateration-based system can reduce the number of installed reference nodes by 154% to achieve the same level of accuracy as the scene analysis-based system. For good location accuracy performance (average error distance ≤ 3 m), the scene analysis-based system yields the highest scalability performance in terms of installed reference nodes. The scene analysis-based system can reduce the number of reference nodes by 40% and 113% to achieve the same accuracy performance when compared with trilateration and proximity-based systems, respectively. Finally, the validation results from the actual installation of Bluetooth-based indoor positioning systems confirmed that our proposed framework can help the system designers to achieve the required performance goal.

A Particle Filter-Based Reinforcement Learning Approach for Reliable Wireless Indoor Positioning

Positioning is envisioned as an essential enabler of future fifth generation (5G) mobile networks due to the massive number of use cases that would benefit from knowing users’ positions. In this work, we propose a particle filter-based reinforcement learning (PFRL) approach for the robust wireless indoor positioning system. Our algorithm integrates information of indoor zone prediction, inertial measurement units, wireless radio-based ranging, and floor plan into an particle filter. The zone prediction method is designed with an ensemble learning algorithm by integrating individual discriminative learning methods and Hidden Markov Models. Further, we integrate the particle filter approach with a reinforcement learning-based resampling method to provide robustness against localization failure problems such as the kidnapping robot problem. The PFRL approach is validated on a two-tier architecture, in which distributed machine learning tasks are hosted at client and edge layer. Experiment results show that our system outperforms traditional terminal-based approaches in both stability and accuracy.

Indoor positioning and navigation system for autonomous vehicles based on RFID technology

The localization and navigation systems play a very significant role in today’s world. They are commonly used in different fields of industry as well as in our daily life. Location of objects is crucial in logistics and transport to provide the real-time monitoring and management of process chain. There are numerous methods which allows to follow people and objects within defined area. One of them is Global Positioning System (GPS), which provides geolocation of objects on the earth as long as an unobstructed line of sight to four or more satellites is assured. As a consequence of this limitation, GPS cannot be applied for indoor environments. Recently, a couple of Indoor Positioning Systems (IPS) based on different technologies have been developed. Despite the drawbacks and limitations, these systems have been successfully applied for different fields of industry. Notwithstanding the above, reliable, accurate, cost-effective and simple indoor positioning methods are still in the area of scientists interest. Present paper provides a general overview of existing indoor positioning systems as well as authors concept of 2D wireless indoor positioning and navigation system dedicated for autonomous vehicles based on Radio Frequency Identification (RFID) technology.

FineLoc: A Fine-Grained Self-Calibrating Wireless Indoor Localization System

Self-calibrating wireless indoor localization systems construct the radio map even the indoor floor plan automatically, which avoids the labor-intensive site survey process; however, existing systems utilizing the feature of Wi-Fi signals can only provide coarse-grained indoor maps, which hinders improvement of localization accuracy. In this paper, we present FineLoc, a fine-grained self-calibrating localization system based on the freely-deployed Bluetooth low energy (BLE) nodes and crowdsourced data, which can profile more detailed layout information of the indoor space. We first reveal that existing systems can only generate inaccurate floor plans owning to the coarse-grained Wi-Fi reference information. Then, we utilize the increasingly popular BLE beacon nodes as the source of reference information, with which a series of dead-reckoning optimization and new schemes particularly for finer-grained indoor map construction are presented. We implement a prototype FineLoc system, which is deployed in around $11,000\;\mathrm{m}^2$11,000m2 areas. Our experimental results with the prototype show that FineLoc can achieve 80 percent localization errors within $1.6\;\mathrm{m}$1.6m, $1.4\;\mathrm{m}$1.4m, and $1.1\;\mathrm{m}$1.1m in the library, classroom building, and office building, respectively, with an average density of deployed BLE nodes less than $2.6/100\;\mathrm{m}^2$2.6/100m2.

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings.

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

Dynamic Wireless Indoor Localization Incorporating With an Autonomous Mobile Robot Based on an Adaptive Signal Model Fingerprinting Approach

Indoor localization based on received signal strength (RSS) will result in a decreased precision after the environment changes. In this paper, we develop an adaptive wireless indoor localization system (ILS) for dynamic environments. The system consists of the following two components: an automated database updating process and a new fingerprinting algorithm called adaptive signal model fingerprinting (ASMF). In the ILS, a self-locating mobile robot is set up to continuously collect RSS measurement data within the localization space for autonomously updating the fingerprint database. ASMF is designed to reduce the time consumption and the amount of RSS data needed for updating the database. The fingerprint of the signal in ASMF is constructed by the position of the beacons and three signal models, which can be duly corrected based on the regression and optimization algorithm. Finally, we propose experiments for positioning targets in the static and dynamic environments and compare the results of the ASMF algorithm with traditional trilateration and k-nearest-neighbor fingerprinting algorithms. The experimental results demonstrate that the ASMF-based ILS provides much better performance in both static and dynamic environments; furthermore, the positioning accuracy can be actually maintained by the autonomous updated ASMF database.

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.

A high output magneto-mechano-triboelectric generator enabled by accelerated water-soluble nano-bullets for powering a wireless indoor positioning system

A high-performance magneto-mechano-triboelectric nanogenerator (MMTEG) is demonstrated for powering a wireless indoor positioning system.

Electronically steerable antenna array for indoor positioning system

This paper presents a 2.4 GHz electronically steerable antenna array (ESAA) using a phase shifter based on the 4×4 Butler matrix which is suitable for the wireless indoor positioning system (WIPS). The low cost and simplicity are advantages of the proposed ESAA. The main beam of the antenna can be steered at four positions in the elevation plane, from −37°, −12°, 12° to + 36° with the narrow beamwidth in order to increase localization accuracy and wide-angle coverage of 240° in the azimuth plane. The ESAA is demonstrated as a base station antenna in a single-anchor indoor localization system experimentation. Two indoor localization algorithms, including fingerprinting and direction-of-arrive (DoA), are implemented and discussed in this paper. The indoor measurement setups and localization results using the proposed ESAA are proven in this paper to have better results than other similar approaches.

Robust System Design Using BILP for Wireless Indoor Positioning Systems

In wireless indoor positioning system designs, reference node (RN) failures during the online phase cause received signal strength values to be unavailable. This leads to accuracy performance degradation and a lack of system reliability in smart office systems. Moreover, the major design concern in the reliability of indoor positioning systems under the faulty RNs during the online phase has not been yet investigated in previous works. To address these gaps, we propose a novel mathematical formulation using a Binary Integer Linear Programming (BILP) approach that employs the Simulated Annealing (SA) solution technique. The proposed robust system design aims to put in place a suitable number of RNs and to determine their optimum locations, which may be located on a single floor or on multiple floors. In particular, the proposed system design provisions to support robust operation both during a normal situation and when there are some RN failures. Experimental results and comparative performance evaluation revealed that the proposed robust system design outperformed other system designs and was able to achieve the highest location accuracy performance in both fault-free and RN-failure scenarios. Specifically, when nine of the RNs in a three-story building failed, the proposed system design achieved 84.6%, 54.7%, and 32.9% more accurate performance than the Uniform, the MSMR, and the PhI-Uni, respectively.

Accuracy Analysis of a Wireless Indoor Positioning System Using Geodetic Methods

Abstract Ubisense RTLS is one of the Indoor positioning systems using an Ultra Wide Band. AOA and TDOA methods are used as a principle of positioning. The accuracy of positioning depends primarily on the accuracy of determined angles and distance differences. The paper presents the results of accuracy research which includes a theoretical accuracy prediction and a practical test. Theoretical accuracy was calculated for two variants of system components geometry, assuming the parameters declared by the system manufacturer. Total station measurements were taken as a reference during the practical test. The results of the analysis are presented in a graphical form. A sample implementation (MagMaster) developed by Globema is presented in the final part of the paper.

Indoor infrared optical wireless localization system with background light power estimation capability.

The indoor user localization function is in high demand for high-speed wireless communications, navigations and smart-home applications. The optical wireless technology has been used to localize end users in indoor environments. However, its accuracy is typically very limited, due to the ambient light, which is relatively strong. In this paper, a novel high-localization-accuracy optical wireless based indoor localization system, based on the use of the mechanism that estimates background light intensity, is proposed. Both theoretical studies and demonstration experiments are carried out. Experimental results show that the accuracy of the proposed optical wireless indoor localization system is independent on the localization light strength, and that an average localization error as small as 2.5 cm is attained, which is 80% better than the accuracy of previously reported optical wireless indoor localization systems.

TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips.

This paper aims at proposing a new wireless indoor localization system (ILS), called TrackCC, based on a commercial type of low-power system-on-chip (SoC), nRF24LE1. This type of chip has only l output power levels and acute fluctuation for a received minimum power level in operation, which give rise to many practical challenges for designing localization algorithms. In order to address these challenges, we exploit the Markov theory to construct a -sized state transition matrix to remove the fluctuation, and then propose a priority-based pattern matching algorithm to search for the most similar match in the signal map to estimate the real position of unknown nodes. The experimental results show that, compared to two existing wireless ILSs, LANDMARC and SAIL, which have meter level positioning accuracy, the proposed TrackCC can achieve the decimeter level accuracy on average in both line-of-sight (LOS) and non-line-of-sight (NLOS) senarios.