$$E^3$$ : Efficient Error Estimation for Fingerprint-Based Indoor Localization System

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.

It has always been important to know where we are in the world. In the past we used maps and compasses to find our way but today we are using GPS devices when travelling by foot, car or bike. The developments in location-based techniques and the growing usefulness of ubiquitous computing and context-dependent information have resulted in an increase business interest in location-based applications and services. Currently, there are many applications requiring precise location or real-time tracking of humans, animals or other things inside buildings. The current global positioning system is unreliable for interior spaces. Therefore, demand for indoor localization services is becoming a key requirement.This paper makes an analysis of recent developments in wireless indoor localization methods and systems, to have a better understanding of the state of the art and to inspire new scientific initiatives in this promising area.In order to achieve the purpose of the paper, the existing wireless localization systems and location methods are reviewed, and also comparing the related techniques and systems with future trends.

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.

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.

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.

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.

The paper answers a fundamental question: can the accuracy of Wi-Fi based localization be significantly increased by fusing information from alternate sources like LTE signals and magnetometers, collected through software defined radios and smart-devices? Further, it aims to eliminate dependency of well established localization techniques on only Wi-Fi access point (AP) positions, and instead, proposes a diversity-leveraging architecture called as the Wireless Locator (Wi-LO) framework. Wi-LO is a client-server paradigm for indoor localization that achieves precision by pattern-matching the collected signal samples with a priori references for each transmitter type, thus giving a stand-alone decision for these diverse sensing modes. The novelty of the paper is fusing these decision outcomes and resolving mis-matches (for e.g., Wi-Fi and LTE suggest different locations) in a seamless manner, and also identifying the best source to use at each location based on its spatial resolution. Wi-Lo is rigorously evaluated on a test-bed, with the proposed scheme of combining Wi-Fi, LTE and magnetometer performing better localization than the classical Wi-Fi-only approach in both urban (+8%) and rural (+25%) scenarios.

Wireless Indoor Localization Using Passive RFID Tags

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.

The cutting-edge wireless technologies offer huge array of services, from ultra-high-speed data communications to internet of things. But the current existing infrastructure cannot handle these use cases. Consequently, it is becoming a trend to apply computational intelligence algorithms such as machine learning (ML), deep learning (DL), reinforcement learning (RL) and artificial intelligence (AI) to wireless network infrastructures. And one of these applications is on wireless indoor localization. Wireless indoor localization takes advantage of wireless access points (WAPs) received signal strength indicators (RSSI) values to pinpoint the location of a user, similar to concept of GPS but indoors. The goal of this paper is to develop predictive models that can be used to predict the location of a user using RSSI readings that his smartphone receives. In this study, four ML algorithms are used which are support vector machines, random forest, Naïve-Bayes classifier and neural networks. The accuracy of each model are 97.83%, 97.67%, 98.50% and 97.33% respectively. Also, a recursive feature elimination algorithm is also used to determine the predictor that has the least impact amongst all other features and it is found out in the study that WAP2 is contributes the least influence when the predictive models are developed.

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.

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)

Wireless indoor localization is a significant challenge because of the noise generated by building structures, electromagnetic fields, and distances between connected nodes inside a building. This study compares two main localization methods: fingerprints (real and synthetic) and ranging schemes based on the Received Signal Strength Indicator (RSSI) of the ZigBee network. We followed four steps for the fingerprinting scheme. First, we obtained real data from the transceivers. Second, we computed the path-loss exponent for each device. Third, we produced a synthetic fingerprint dataset. Finally, we used three localization methods: k-nearest neighbor (KNN), multilayer perceptron (MLP), and long short-term memory recurrent neural network (LSTM-RNN). We assessed the performance of the localization methods by measuring their accuracy, precision, error-to-active area ratio, and installation difficulties. The results show that the real fingerprint scheme has the best performance, but it requires more installation time. While the synthetic fingerprint generation is based on the path-loss method that mixes the advantage of both fingerprint and path-loss. Furthermore, we compared the proposed estimated path-loss with that of a state-of-the-art method and demonstrated that the proposed method exhibits superior performance. These results suggest that the proposed method is more precise and suitable for real datasets.

While wireless local area network-based indoor localization is attractive, the problems concerning how to capture the signal-propagating character in the complex dynamic environment and how to accommodate the receiver gain difference of different mobile devices are challenging. In this article, we solve these problems by modeling them as common mode noise and develop a localization algorithm based on a novel differential radio map approach. We propose a differential operation to improve the performance of the radio map module, where the location is estimated according to the difference of received signal strength (RSS) instead of RSS itself. The particle filter algorithm is adopted to realize the target localization and tracking task. Furthermore, to calculate the particle weight at arbitrary locations, we propose a local linearization technique to realize continuous interpolation of the radio map. The indoor experiment results demonstrate the effectiveness and robustness of our approach.

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.