Key Technologies for Indoor Positioning Systems

It is hard to obtain a general error model for the range-based wireless indoor positioning system due to the complicated hybrid line-of-sight/non-line-of-sight (LOS/NLOS) environment. The performance of the conventional Gaussian-based nonlinear filters is degraded in the indoor scenario. In this paper, we employ a dynamic Gaussian model (DGM) to describe the indoor ranging error. A general Gaussian approximated model is constructed first to fit the potential distribution. The instantaneous LOS or NLOS error at a typical time is considered as the drift from this general distribution dynamically. The relationship between the instantaneous error of the DGM and the estimation accuracy of nonlinear filters is analyzed. Based on our analysis, we propose a measurement adaptation method to further reduce the error according to the DGM. Then, the nonlinear filters based on the Gaussian model, which are simple and accurate, can be applied. A biased extended Kalman filter (EKF) and an adaptive Gaussian particle filter (PF) integrated with the measurement adaptation method are designed. The real indoor experiment demonstrates that the estimation accuracy of our algorithms is greatly improved without imposing complexity and that our algorithms are suitable for the dynamic indoor environment.

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.

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.

Wireless indoor localization has attracted extensive research recently due to its potential for large-scale deployment. However, the performances of different systems vary and it is difficult to compare these systems systematically in different indoor scenarios. In this work, we propose \(E^3\), a Gaussian process based error estimation approach for fingerprint-based wireless indoor localization systems. With an efficient error estimation algorithm, \(E^3\) is able to efficiently estimate the localization errors of the localization systems without requiring the expensive site evaluations. Our evaluation results show that the proposed approach efficiently estimates the performance of fingerprint-based indoor localization systems and can be used as an efficient tool to tune system parameters.

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.

Determining the direction-of-arrival (DOA) of any signal of interest has long been of great interest to the wireless localization research community for military and civilian applications. To efficiently facilitate the deployment of DOA systems, the accuracy of wireless localization is critical. Hence, this paper proposes a novel method to improve the prediction result of a wireless DOA localization system. By considering the signal variation existing in the complex environment, the actual location of the target can be determined including the maximum prediction error. Moreover, the scenario of the moving target is further investigated by incorporating the adaptive Kalman Filter algorithm to obtain the prediction route of the flying drone based on the accuracy assessment method. This proposed adaptive Kalman Filter is a high-efficiency algorithm that can filter out the noise in the multipath area and optimize the predicted data in real-time. The simulation result agrees well with the measured data and thus validates the proposed DOA system with the adaptive Kalman Filter algorithm. The measured DOA of the fixed radiation source obtained by a single base station and the moving route of a flying drone from a two-base station localization system are presented and compared with the calculated results. Results show that the prediction error in an outdoor region of is about 10–20 m, which demonstrate the usefulness of the proposed wireless DOA system deployment in practical applications.

In this paper, we propose an extended recursive Cramer-Rao lower bound (ER-CRLB) method as a fundamental tool to analyze the performance of wireless indoor localization systems. According to the non-parametric estimation method, the Fisher information matrix of the ER-CRLB is divided into two parts: the state matrix and the auxiliary matrix, which builds a general framework to consider all the possible factors that may influence the estimation performance. Based on this idea, ER-CRLB can fully model the estimation process in the complicated indoor environment, e.g., the sequential position state propagation, target-anchor geometry effect, the NLOS identification, and the related prior information, which are demonstrated in the comprehensive simulations.

The quality of Received radio signal strength (RSS) is essential for obtaining higher accuracy in the fingerprint-based positioning system. However, due to the variation of the RSS which is caused by multi-path propagation or moving objects, such a precise positioning system is difficult to achieve. To address the above issues, a new calibration method is presented. The method consists of two parts: offline training part and online positioning part. A high accurate model is obtained in the offline training part by a neural network, and the unknown position is determined in the online part using the model which is trained in the previous part. In the offline part, a Gaussian fitting is first applied to the distribution of the obtained RSS data, and mixture Gaussian calibration method is adopted when more than one significant peak is occurred in the Gaussian model. In the online part, k-times measurement is adopted to enhance the quality of the RSS data. A set of experiments in an indoor positioning scenario is conducted. The experimental results show that in the offline part, the proposed mixture Gaussian calibration method can significantly improve the accuracy from 3.2m to 2.5m for 80% test points, and in the online part, the proposed k-times measurement narrows down the localization error from 4.3m to 2.4 for 80% test points. For that, no hardware modifications are required.

A patient tracking system is a promising tool for managing patient flow and improving efficiency in the operating room. Wireless location systems, using infrared or radio frequency transmitters, can automatically timestamp key events, thereby decreasing the need for manual data input. In this study, we measured the accuracy and precision of automatically documented timestamps compared with manual recording. Each patient scheduled for urgent surgery was given an active radio frequency/infrared transmitter. The prototype software tracked the patient throughout the perioperative process, automatically documenting the timestamps. Both automatic and traditional data entry were compared with the reference data. The absolute value of median error was 64% smaller (P < 0.01), and the average quartile deviation of error was 69% smaller in automatic documentation. The average delay between an activity and the documentation was 80 seconds in automatic documentation and 735 seconds in manual documentation. Both the accuracy and the precision were better in automatic documentation and the data were immediately available. Automatic documentation with the Indoor Positioning System can help in managing patient flow and in increasing transparency with faster availability and better accuracy of data.

As technology gets cheaper and wireless networks grow, more efforts have been put into developing smart buildings and homes. A key characteristic of these smart buildings is to provide indoor navigation and localization services to its occupants. As this demand increases, simple, scalable solutions are sought after. Current localization technologies, such as GPS, do not work in indoor environments, nor does it provide the level of accuracy required for an effective indoor navigation solution. This paper explores the feasibility of a wireless indoor location system based on Bluetooth Low Energy beacons (BLE). Five popular BLE beacon devices are compared in terms of energy consumption and proximity accuracy. Experiments are conducted in three different rooms and three filtering techniques are also examined, to improve the performance of each beacon. According to experimental results, a proper selection of the filtering technique can help to improve their proximity estimation.

In the past decades, the research on wireless localization systems has always stayed at the level of “empirical study.” The basic paradigm is as follows: the researcher is inspired by physical phenomena, then finds out technological breakthroughs to improve system performance, and finally conducts experiments for verification. Although this method may show superior performance under certain assumptions, it cannot reveal the nature of wireless localization technology. Consequently, some fundamental theoretical questions have not been substantively answered. For example, what is the upper bound of wireless localization performance under ideal conditions? Is there a general method, independent of the deployment environment and equipment heterogeneity, that can optimize the wireless localization system fundamentally? Our research answers these questions to a certain extent. Specifically, we propose a systematic theoretical analysis framework, based on which we determine the theoretical bound of localization performance. The proposed framework can help us understand the nature of wireless localization technology and guide us to optimize a practical localization system. In this chapter, we start with the basic signal propagation model, based on which we provide theoretical analysis on the accuracy, reliability, and scalability of the localization system.KeywordsWireless localizationMobile crowd sensingFingerprintsTheoretical analysesReceived signal strength (RSS)Channel state information (CSI)

Radiofrequency positioning problem within indoor space becomes significant with respect to the specific area for which the radiolocation is required. The area of interest may be an apartment, shop or storage area of any shape and size. The specificity of the space defines various requirements on the radiolocation system and the need for software that would facilitate the planning of such a system is obvious. Using well-known empirical and deterministic models for propagation of electromagnetic waves in enclosed spaces and precise analysis of specific closed areas, localization based on the RSS (Received Signal Strength) method should give good positioning results in any enclosed space. Software solution that was developed in this work allows you to determine the optimal position of the receiver on the walls of any space that is selected as an area in which implementation of a system of RSS indoor localization is required. The optimal position of the receiver provides precise and reliable localization within the indoor enclosure. The software can be used to optimize the indoor localization of people or objects, with the requirement that special radio frequency tags or radio frequency transmitters are mounted on them. The software supports the ability to import an arbitrary layout of indoor space over which optimization will be carried out from AutoCAD or a CAD tool.

Locating trapped targets using the signals of wireless devices such as mobile phones carried by targets is increasingly becoming the preferred scheme for disaster rescue. However, the accuracy of radio-localization technology currently adopted in the rescue is not high enough. To solve such problems, researchers proposed the approaches of Warwalking or Wardriving. However, both approaches are all limited by application scenarios. This paper proposes GuideLoc, a highly efficient aerial wireless localization system, which uses the target guiding technology based on region division. GuideLoc allows an unmanned aerial vehicle (UAV) to fly over a target device and provide position coordinates of the UAV as the target location information. During the process of positioning targets, based on the result of regional division, GuideLoc uses a number of fixed antennas to get the received signal strength indicator (RSSI) and angle of arrival (AOA) information of the target and estimates the target location using the information and correspondingly controls the UAV to fly towards the target. Averaging method is also applied for determining coordinates of the target. Experiment results show that GuideLoc can in short time achieve 2.7-meter positioning accuracy in average. Compared with other wireless localization methods using UAVs proposed in the literature, it shortened the flight distance by more than 50%. In addition, energy efficiency is also improved significantly.

The wireless indoor positioning system (IPS) has high demand due to wide applications such as asset and personal tracking, location analysis, and mapping. Global Positioning System (GPS) doesn't work within an indoor environment due to the non-line-of-sight (NLOS), to overcome this limitation we are using the most demanded technology Wi-Fi due to its high range, high data throughput with high availability and accessibility. The main problem is the positioning estimation accuracy in the Wi-Fi-based positioning system due to noise and interference in the environment. The received signal strength (RSSI) and signal quality (SQ) become low due to the multipath effects, reflections, signal loss, fluctuations, and other unknown factors. The objective of the proposed method is to reduce the fluctuations and the noise of the received signal and improve the signal strength and signal quality to increase the positioning estimation accuracy and distance estimation accuracy. RSSI smoothing technique of weighted moving average and feedback filter is used to make signal cleaner by removing the fluctuations and signal quality parameters SNR margin, frequency, and power noise to improve the signal quality. The trilateration technique is used to realize distance estimation and position estimation. To analyze the positioning estimation accuracy and distance estimation accuracy of the proposed method we implement the regression accuracy metrics based on mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). By doing this we achieved an accuracy below 0.5 meter.

Space-time radio interferometric positioning system (STRIPS) is a indoor localization system with low complexity that is robust to multipath effects. Its general idea follows a radio interferometric positioning system (RIPS), where two transmitters transmit sinusoidal signals at slightly different frequencies to create a slowly-varying envelope at a receiver. The receiver employs a cost-efficient envelope detector to extract the low-frequency differential signal, of which its phase contains a range information. Unlike the original RIPS, however, the STRIPS operates in the millimeter-wave (MMW) band, where the signal interference and multipath are less prominent than a typical radio-frequency (RF) band. In this paper, we implement the STRIPS on hardware and experimentally validate its performance. The experimental results show that the prototype we have built successfully achieves a submeter accuracy on average.