A Survey of 3D Indoor Localization Systems and Technologies

A survey of active and passive indoor localisation systems

This paper tackles indoor receiver localization as a regression task using visible light positioning (VLP). This technology has gained prominence as an alternative to conventional indoor positioning. Traditional systems such as WiFi and ZigBee face notable drawbacks: electromagnetic interference, added infrastructure, limited accuracy, and high deployment costs. In general, classical positioning approaches—lateration, triangulation, proximity, and least squares—typically infer location from measurements like received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). However, in real-world VLP scenarios, these methods face difficulties since they depend on ideal signals, which means unaffected by physical obstacles and perfectly aligned between transmitters and receiver. Our work proposes the application of the extreme learning machine (ELM) algorithm, its regularized version (R-ELM) and the Levenberg-Marquardt algorithm (LMA), to predict the receiver’s position based simply on the light signals emitted by light-emitting diodes (LEDs) and captured by a photodiode (PD). To evaluate the overall performance of these models, two experimental datasets were used: one in a 2D environment with 7344 RSS samples and another novel in a 3D environment with 27000 training records and 18000 testing samples by analyzing power spectral densities. To assess performance and analyze parameter optimization of ELM, R-ELM, and LMA, metrics such as root mean square error (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMSE</i>), correlation coefficient (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i>), training and testing times were used. By considering the state of the art, the LMA model proved to be the most accurate in the 2D environment (RMSE less than 10 cm), although its training time was excessive (over one hour), incompatible with for real-time applications (time variant channels). In contrast, the R-ELM achieved an accuracy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMSE</i> = 12.4806 cm and completed its training in 31.2 ms (real-time learning), positioning it as a much more efficient alternative compared to LMA. For the 3D dataset, the R-ELM also showed the best overall performance, with an average RMSE close to 22.4784 cm and a training time of 1.0250 s. Incorporating both the dominant frequency and power as inputs into the ELM enhanced the model’s accuracy compared to using each input separately. Lastly, RMSE was evaluated as a function of the distance from the center of the room of each data set, concluding that the R-ELM is more stable in areas with weak signals or missing data, which is particularly valuable in real environments where the signal is not always homogeneous. Finally, the results demonstrate that it is possible to achieve a precise and fast positioning system based on VLP using the R-ELM, as this model generally achieves higher accuracy and computational efficiency.

Global Navigation Satellite System (GNSS) has been widely used in people’s life. Generally, GNSS signals suffer from severe attenuation in urban canyon environments. the 5th generation wireless systems (5G) and GNSS hybrid positioning technique is a promising localization technique because 5G base stations are able to help GNSS relieve the problem of lacking visible satellites in urban environments. However, 5G-GNSS hybrid positioning faces the time synchronization problem. The conventional methods based on time of arrival (TOA) or time difference of arrival (TDOA) require that the time synchronization accuracy between two base stations should at least be 30-nanosecond level to make sure the range error is below 10 m, which is difficult to achieve in real applications. This work proposes a 5G-GNSS hybrid positioning scheme basing on AOA-TOA measurements. We combine the angle of arrival (AOA) estimates from 5G base stations and TOA measurements from GNSS satellites to perform hybrid localization. Moving averaging operation is then performed on the raw position estimates to further reduce the effect of noise. The principle of the proposed algorithm is explained detailedly. Numerical results demonstrate that the proposed method can be free from the influence of the time synchronization error, thus it improves the accuracy and robustness of hybrid positioning system in comparison with conventional methods.

Global navigation satellite system (GNSS) has been widely used in many fields due to their low cost and high positioning accuracy. Because of the open frequency of navigation signals, the low power of navigation signals, and the growing reliance of many modern wireless systems on satellite-based navigation, GNSS performance may be easily affected by interference signals. Monitoring and troubleshooting of interference sources are important means to guarantee the normal use of satellite navigation applications and are an important part of GNSS operation in complex electromagnetic environments; however, traditional angle of arrival (AOA) algorithms cannot efficiently operate with coherent signals, so a decoherence-based orientation scheme is proposed to optimize the AOA algorithm. Furthermore, a joint AOA and time difference of arrival (TDOA) interference localization algorithm is proposed for problems such as the lack of accuracy in a single interference source localization algorithm. Numerical simulation results show that decoherence-based AOA localization can be well applied to various interference signals, and the accuracy of the joint AOA and TDOA interference localization algorithm is higher than that of single-method interference localization. In addition, the physical verification further verifies the usability and reliability of the GNSS interference source positioning algorithm proposed in this paper.

In this paper, the indoor visible light positioning algorithm based on the technique Time Difference of Arrival (TDOA) using Hilbert transform is presented. The proposed indoor positioning system (IPS) is designed with the single light emitting diode (LED) ceiling lamp transmitter and the target device with multiple receiver photodiodes (PD). The mathematical model of the proposed system is derived that constitutes the different approaches for estimating the coordinates of the target device. As the phase difference is the key parameter for estimating the location in the TDOA approach, we have employed the Hilbert transform to calculate the phase difference for different PDs. The mean localization error achieved for the indoor environment of 5 m × 5 m × 3 m is in the range of 10−4 m. The mm-level accurate IPS system is designed with relatively lower complexity than the other positioning techniques, which are Received Signal Strength (RSS), Angle of Arrival (AOA), and Time of Arrival (TOA). This is due to the single LED transmitter requirement for the positioning of target device, whereas the other techniques necessitate multiple transmitters. The estimation is done with the different transmitter and receiver positions and the performances are compared. Based on the analysis, the optimum positions for the single LED ceiling lamp are provided.

In recent years, indoor and outdoor positioning technology for the blind has attracted much more attention. The outdoor positioning technology has relatively matured. However, three-dimensional indoor positioning technology is facing many problems. Due to low positioning accuracy as well as slow positioning speed, there is no ideal implementation system at present. Light Fidelity (LiFi) technology is a new wireless transmission technology that uses the visible light spectrum, such as the light emitted by a light bulb, to transmit data. It transmits signals through the changing light of LED lights. When LED lights are properly laid out in the building, not only the function of lighting but also that of transmitting information are met at the same time. It is a good way to solve indoor positioning problems. This article studies an indoor positioning glasses for blind based on the LiFi technology. Different from other indoor positioning technologies such as ultrasound and wireless fidelity (WiFi), the design of indoor positioning glasses for the blind will propose new ideas of three-dimensional indoor positioning problems for the blind.

Due to the limitation of Global Navigation Satellite System (GNSS), it cannot work properly without direct line of sight to GNSS satellites such as in urban and indoor environments. Visual processing such as Visual Odometry (VO) and Structure-From-Motion (SFM) is usually used for the indoor navigation or localization. SFM was introduced in 1990 and had been successfully used for reconstruction of unordered images, and in the process of reconstruction, the SFM would estimate the position of the camera for each image at the same time. This paper introduces three most common SFM tools and suggests a positioning method for small UAVs equipped with vision cameras in indoor environment. Each stage of the SFM algorithms is studied to analysis the impact on the final positioning accuracy. The output from different feature detection, feature matching, initial pair setting, and the reconstructions are compared with an indoor reference localization system based on infrared cameras. The objective of this paper is to suggest a positioning and navigation method for small UAVs equipped with vision cameras in indoor environment. In this research, we will study the impact of different stages of SFM algorithm on the final positioning accuracy. Several objects such as chairs or boxes would be placed randomly in a lab room to build a feature-based environment without any special landmarks. In our experiment, an indoor localization system based on infrared cameras will be used as the reference navigation result to evaluate the final performance of the suggested algorithm.

This study develops a hybrid positioning scheme that combines both differential time of arrival (DTOA) and angle of arrival (AOA) observations of the 1090-MHz automatic dependent surveillance-broadcast (ADS-B) signal and implements it on a wide area multilateration (WAM) test bed. The major issue when combining DTOA and AOA observations is synchronization. A synchronization design for DTOA and AOA observations is thus evaluated. The positioning algorithm that combines AOA and DTOA observations is a filter-based approach. Based on the measurement errors from actual ADS-B signals received from real flights, simulation is used to evaluate the positioning performance of the proposed 1090-MHz ADS-B WAM with the integration of AOA/DTOA approach under two scenarios, namely DTOA alone and AOA combined with DTOA.

Understanding the indoor whereabouts of individuals and objects is important, especially for those who fall within the 71% of visually impaired individuals with a school education, students in 450 special education units and many other areas and aspects in Sri Lanka. Researchers have declared that, there isn’t any particularly good localization system, and the performance should be evaluated considering the approach and application. The most well-known indoor positioning (IP) technologies that have been historically deployed are Bluetooth, Wi-Fi, RFID (radio frequency identification), IR (Infrared), and UV (ultraviolet) whereas received signal strength (RSSI), fingerprinting, and triangulation methods have been used as common IP techniques. The combination of both IP technologies and techniques creates an IP system, and the integration of machine learning and IoT with the structured system essentially delivers an accurate and more advanced system. This paper contains a detailed, analytical review of a developed indoor positioning system derived from the existing indoor localization techniques, localization technologies, localization systems, algorithms, and performance matrixes. This also provides a comprehensive comparison between numerous existing systems to justify the proposed solution. This project has been developed to achieve better accuracy through low-cost deployment as an effective system to fill the gap in the scarcity of positioning systems in the world. This paper presents a descriptive introduction and problem definition, a critical discussion of results, machine learning models, benefits of the project, and future works. As later justified, ESP32 microcontroller and BLE beacons are utilized with RSSI fingerprinting method to develop this IP system and, as a part of the project, two data visualization methods have been introduced here using NodeRED dashboard and LC display. Overall, this project was developed with an effective combination of RSSI fingerprinting, IoT protocols, machine learning, and data interpretation methods. KEYWORDS: Indoor positioning, RSSI, Fingerprinting, Machine Learning, MQTT, ESP32, BLE beacons, NodeRED

The ASEAN Community is comprised of three pillars, namely the ASEAN Political-Security Community, ASEAN Economic Community and ASEAN Socio-Cultural Community. Last year, in December 2015, Laos P.D.R and others 9 ASEAN Countries became The ASEAN Economic Community or AEC. For ASEAN Socio-Cultural Community will be achieved soon. As known, 10 ASEAN Countries have many difference cultures such as Laos P.D.R has more than 49 races and they are different for social and cultures. To learn about all Lao cultures, the museum is a good choice. The smart museum is the museum which can be provided a good information and convenient for the visitors. To provide good information for the visitors, the tracking system is needed. Although there are many tracking systems for 2D indoor localization proposed in recent years, 3D indoor localization is still limited. Therefore, this paper proposed the intelligent system for 3D localization supported visitor tracking in Laos's museum using RFID Technology. The system deployed fingerprint technique and pattern matching algorithm to solve the problem of indoor propagation and to get the high accuracy. According to the result, the average error of the estimated target location is approximately 60 cm which is less than the fingerprint separated distance. In addition, this system can be identified whether the visitors are adult or children by classifying the height of the target location as well leading to support the suitable information (speech, videos, text, etc.,) for them in the future work.

Modern infrastructure and a myriad of services rely on positioning and timing information provided by Global Navigation Satellite Systems (GNSS) and in particular the Global Positioning System (GPS). However, given their low received signal power levels, GNSS signals are vulnerable to Radio Frequency Interference (RFI), either from non-intentional or intentional (jamming), sources. Hence, GNSS itself has become a critical infrastructure which must be protected. Since RFI source is unknown a priori, passive localization systems consisting of spatially distributed Sensor Nodes (SNs) are needed to geo-locate the RFI. These systems typically use source Angle of Arrival (AOA), Time Difference of Arrival (TDOA) or a combination of AOA/TDOA measurements which are non-linear in nature, to estimate the RFI position. Also, dynamics associated with the RFI source(s) further complicates the geo-localization process. This paper explores and reports on the use of various Kalman Filters in combining AOA and TDOA measurements for efficient geo-localization and tracking of dynamic and stationary RFI sources based on real measurements from one such geo-localization system. We report on and contrast the geo-localization accuracies and computational complexities of the Extended, Unscented and Single Propagation Unscented Kalman Filters along with the traditional snap-shot approach.

Global navigation satellite systems (GNSS) have been widely used in our daily lives. Meanwhile, GNSS are facing a severe threat of spoofing attacks, which can mislead users by false position, velocity and time (PVT) solutions, resulting in serious consequences. Especially, with the development of vehicle navigation, autonomous driving and unmanned aerial vehicles (UAV), increasing spoofing attacks have emerged towards vehicles, where anti-spoofing techniques are desperately needed. Among these techniques, spoofer localization is a necessary step to fundamentally address the spoofing attacks. Most of the conventional spoofer localization methods are based on time of arrival (TOA) or time difference of arrival (TDOA). These methods require measurements observed by multiple receivers at the same time, where time synchronization is indispensable. However, it is hard to apply these methods to the existing vehicles equipped with independent commercial receivers. To cope with this problem, in this paper we develop a flexible method for vehicles to locate a stationary spoofer based on Doppler and clock drift double difference (DCDD). DCDD is calculated using Doppler measurements and clock drift solutions of one vehicular receiver and another reference receiver. Generally, Doppler frequency of GNSS signals changes slowly with time, which releases the restriction for high-precision time synchronization. DCDD represents the Doppler frequency caused by the vehicle motion, from which the spoofer position can be estimated. In addition, with the elimination of irrelevant components in Doppler measurements, DCDD minimizes the user complexity required for localization, where at least two receivers and one vehicle is needed. Utilizing DCDD, an iterative weighted least squares (WLS) algorithm is used to solve the spoofer position, and a closed-form initial value is given to improve the convergence rate. We evaluate the localization performance by the derived Cramér-Rao lower bound (CRLB) in the presence of vehicle position and velocity errors. Simulations and field vehicle experiments are conducted to verify the feasibility and effective of the proposed method. The experimental results show that with an average vehicle velocity of 8 m/s, the horizontal localization error can be less than 40 m and 20 m, using DCDD observations of 4 epochs and 8 epochs respectively.

Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods.

Received Signal Strength Indicator (RSSI) is simplest among various indoor localization methods. The main drawback is requiring a training phase to build fingerprints database, which is time consuming and sensitive to changes of test environment. Time based approaches such as Time of Arrival (ToA) and Time Difference of Arrival (TDoA) achieve better performance with some hardware expenditure, while location accuracy is significantly affected by multipath interference. By employing Super-resolution techniques (SRTs), multipath components can be estimated to improve localization precision, but limited to OFDM-based Multiple Input Multiple output (MIMO) wireless communication systems. Considering for general radio signals with strong correlation, we propose an enhanced TDoA approach based on RSS-assisted cross-correlation method. Simulation and experimental results demonstrate that our method is effective to mitigate multipath interference, verified by IEEE 802.11b WLAN. Compared with SRTs, our method achieves comparable localization precision, and has superiority in computational complexity and anti-noise performance.

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.