An Enhanced Indoor Localization System Using Crowdsourced Multi-Source Measurements

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

With the rapid growth of the demand for indoor location-based services (LBS), Wi-Fi received signal strength (RSS) fingerprints database has attracted significant attention because it is easy to obtain. The fingerprints algorithm based on convolution neural network (CNN) is often used to improve indoor localization accuracy. However, the number of reference points used for position estimation has significant effects on the positioning accuracy. Meanwhile, it is always selected arbitraily without any guiding standards. As a result, a novel location estimation method based on Jenks natural breaks algorithm (JNBA), which can adaptively choose more reasonable reference points, is proposed in this paper. The output of CNN is processed by JNBA, which can select the number of reference points according to different environments. Then, the location is estimated by weighted K-nearest neighbors (WKNN). Experimental results show that the proposed method has higher positioning accuracy without sacrificing more time cost than the existing indoor localization methods based on CNN.

This paper presents results for an approach for indoor location estimation that integrates received signal strength (RSS) data from both WiFi and GSM networks. Previous work has focused on relatively small indoor environments. In many potential applications, getting approximate location information, such as in which room the mobile user is, is adequate. A hierarchical clustering method is used to partition the RSS space. To choose the best transmitters in a partition, we assess the amount of RSS variance that is attributable to different base stations (BSs) or access points (APs) by transforming the RSS tuples into principal components (PCs). This allows us to retain most of the useful information of detectable transmitters in fewer dimensions. In our experiments, we collected WiFi and cellular RSS on the 2nd and 3rd-floor electronic engineering (EE) building in Queen Mary campus. The experiment results show that the proposed method can provide a good accuracy of room prediction, especially when we integrate WiFi RSS with GSM RSS together to do the positioning.

The fingerprint location method based on the WiFi received signal strength (RSS), is one of the indoor positioning method owning the highest value of application currently. It uses distribution characteristic of WiFi RSS in the space to estimate user's location. But due to the unstable RSS measurements of WiFi signal in a fixed location, in online phase, the uncertain fluctuation of RSS measurements will cause matching error of reference point (RP), and lead to the positioning error. To deal with this problem, a matching positioning method combining spatial multi-points fingerprint is presented in this paper. Firstly, assisted by odometer information, the space uniforming calibration of fingerprint is carried out in radial direction of road. And then, intensity and variation characteristics in space of consecutive multi-points RSS are matched jointly in online process, so as to enhance the positioning accuracy and stability. Through experiments, it is proved that the method in this paper is effective and, under reasonable parameters, positioning performance is good.

Compared with the Wi-Fi Received Signal Strength (RSS) commonly-used for the indoor localization, the Channel State Information (CSI) can be used for precise ranging to achieve the high Wi-Fi indoor localization accuracy since it includes the fine-grained physical-layer information such as the amplitude and phase of each subcarrier during the signal transmission. Due to the lack of the theoretical analysis of the localization error bound in the existing CSI ranging-based localization methods, it is not easy to compare the ideal performance of different localization methods. Therefore, this paper proposes a CSI ranging-based Wi-Fi indoor localization error bound analysis method, which is based on the indoor signal propagation model to derive out the CSI ranging-based localization error bound by considering the relationship between the localization accuracy and the path loss, shadow fading, multipath effect and asynchronous effect. Besides, through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, as well as discusses the impact of different experimental parameters on the localization performance.

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

The paper focuses on the performance analysis of the timing acquisition scheme based on received signal strength (RSS) measurements for ultra-wideband (UWB) communication systems. To our knowledge, few paper discusses the performance of the timing acquisition scheme based on RSS measurements for UWB in details. The timing acquisition scheme based on RSS measurements has many advantages, such as low cost, high speed and robustness. However, low transmission power limitation and complicated indoor multipath propagation environments make it difficult to measure accurate RSS at the receiver and affect the accuracy of the time delay offset estimation for the local template signals. In addition, the time variation of the indoor channel state can form a multiple-to-one mapping relation between the RSS measurements and the distance, which largely limits the method application. In the paper, we analyze the accuracy of time delay offset estimation for this method by deriving the Cramer-Rao lower bound (CRLB). The number results show that the accuracy of the timing acquisition method based on RSS measurements suffers from both the UWB signal transmission speed and the varying distance between the transmitter and receiver. It is noted that though the accuracy of the method can hardly fulfil the fine timing acquisition requirement for UWB systems, it will largely reduce the coarse timing acquisition time and improve the performance for UWB timing acquisition systems if combined with other timing acquisition ones

In this paper, we propose a surface correlation based fingerprinting method in sparse Wi-Fi environment. The proposed method can estimate location even with very few Wi-Fi APs by using accumulated spatial RSS pattern. We verify performance of the proposed method through several tests in a building with complex paths. Many studies have developed localization technologies that use measurements such as RSS (Received Signal Strength) or RTT (Round Trip Time) for Wi-Fi signal. All RF have signal noise and distortion problems as well as multipath problem in indoor environments. As the distance from the RF signal source increases, the discrimination of the RSS decreases due to RF signal noise and distortion. Therefore, ambiguity of RSS increases as the distance increases, the performance deteriorates. The conventional RF based localization method have tried to maintain dense RF environment to guarantee stable and accurate performance. However, in order to maintain the dense RF environment, it is becoming a barrier to commercialization of localization technology due to high cost of constructing RF infrastructure and maintenance difficulty. In this paper, we propose a Surface Correlation (SC) based fingerprinting technique to maintain high positioning performance even with few Wi-Fi APs. The surface is the accumulated RSS pattern data during the movement of a pedestrian, which means the spatial RSS pattern. Conventional fingerprinting methods using kNN (k-Nearest Neighbors) and WkNN (Weighted kNN) are very vulnerable to signal noise and distortion because they utilize instantaneous RSS data. The performance of Particle Filter (PF) based localization technology is also degraded in areas where Wi-Fi AP is sparsely distributed. However, the proposed method is very robust against signal noise and distortion because it compares the spatial RSS pattern with the reference RSS pattern on the fingerprinting database. This increases localization accuracy and availability for Wi-Fi coverage. For localization, the proposed method conduct correlation process for the spatial RSS pattern of a pedestrian with reference RSS pattern of database. So, the reference RSS pattern should be extracted from a reference trajectory that matches a pedestrian`s walking path. The proposed method utilize a database of ‘link-node’ structure to dynamically generate a reference trajectory in indoor environment with complicated path. It generate candidate reference trajectories using only MAC address and turn event of Pedestrian Dead-Reckoning (PDR). The proposed method fix a reference trajectory between these candidate trajectories through correlation process of measured RSS pattern with each reference RSS pattern. A reference point on the fixed reference having most similar RSS pattern to a pedestrian`s is considered as a current location. In order to improve performance at a fork where RSS ambiguity increases, the proposed method estimates preferentially for a reference trajectory matching with PDR turn event. The turn event can be wrong due to various motion of a pedestrian. The proposed method selects a correct reference trajectory by continuously performing correlation process on candidate reference trajectories. In order to verify the performance of the proposed surface correlation based fingerprinting method, we conducted several tests in a three-story building. Before the tests, we conducted a survey of Wi-Fi environment to construct fingerprinting database in the building. As a result of the survey, 170 APs are distributed in the building. We reduced the number of APs by 20% from 170 to 17, and analyzed performance of the proposed and conventional localization methods. The test results show that the performance of the conventional localization methods degrade as the number of applied APs decreases. The proposed fingerprinting method shows a 3 meter localization error even with very few Wi-Fi APs, and unlike the conventional fingerprinting method, the localization error is bounded to be within 5m due to the use of spatial RSS pattern. In this paper, we presented the fingerprinting method using the surface correlation in sparse Wi-Fi environment. The proposed fingerprinting method accumulates Wi-Fi RSS during movement of a pedestrian. The proposed method compares the spatial RSS pattern, which is accumulated RSS data, with reference RSS patterns of fingerprinting database. By comparing the RSS patterns, the proposed method greatly improves accuracy as well as stability. In addition, it can be effectively applied in a sparse Wi-Fi environment by improving the availability of ambiguous areas of Wi-Fi coverage. Through these results, we expect to be able to provide accurate and reliable location information even in indoor weak RF environment.

High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m2 area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future.

With the WiFi becoming more and more popular over the last few years, the study on the technology of indoor positioning based on WiFi is given more and more attention. Being specific to the problem that not all the clustered reference points(RPs) of the traditional clustering algorithm have geometric proximity in indoor positioning, this paper puts forward the affinity propagation presentation(AP) which bases on the combination of geometry and received signal strength (RSS). Being different from the traditional clustering algorithm, this kind of algorithm can endow the RPs with geometrical and endow the RSS with corresponding weight according to the positioning conditions, it can use the features of the geometry and RSS to restrain the sort of RPs, and that can make the sort of RPs more reasonable. After that it will use k-Nearest Neighbor Algorithm(KNN) to have accurate positioning, which not only can reduce the calculation, but also improve the localization accuracy. Simulation shows that, this kind of algorithm possesses the advantage of fast location and higher localization accuracy.

The increasing demand of indoor Location Based Services (LBS) has made Indoor Positioning System (IPS) a hot research topic. Recently, various machine learning techniques have been developed to tackle the problems of IPS. The WLAN fingerprinting technique is one of the most cost effective choices for IPS due to the use of existing Wi-Fi networks in commercial and public buildings. However, the variation of Received Signal Strength (RSS) at any specific location due to the indoor fading issue leads to serious challenges in the accuracy of the distance estimates. In this paper, we propose a new technique by using Probabilistic Neural Network (PNN) multi-classifier based on Multiple Service Set Identifiers (MSSIDs) that are configured on the same access point. A spatial voting scenario for three PNN-classifiers is proposed as a tool to determine the location of the user. Using a spatial voting based technique is specifically designed to tackle the negative impact of the multi-path propagation on the performance of IPS. The proposed system is performed inside the College of Engineering and Applied Sciences (CEAS) at Western Michigan University. The performance of the proposed system compared with some of the common methods such as K-Nearest Neighbors (K-NN) and multi-class support vector machine (SVM). According to the experimental results, the spatial voting of the three PNN-dassifiers can significantly mitigate the adverse effects of RSS variation. The performance of the multi-classifier exhibits superior performance to that of the other single classifiers in all Area of Interest (AOI). Over 3000 m2 AOI, the proposed system achieves localization accuracy < 0.73m and with precision 90 % of the distance error less than 2m.

Indoor positioning systems answer the need for ubiquitous localisation systems. Frequently, indoor positioning relies on machine learning models developed based on the training data composed of WiFi received signal strength (RSS) vectors observed in different indoor locations. However, this requires expensive collection of RSS vectors in precisely measured locations. In this study, we propose a semi-supervised method, which can reduce the volume of expensive labelled training data and exploit the availability of unlabelled signal strength measurements. The method relies, inter alia, on the measures of similarity among nearest neighbours of unlabelled vectors. Tests performed with a number of testbed areas confirm that the method improves the accuracy of random forest models used to estimate indoor location of mobile terminals.

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a Centralized indooR localizatioN method using Pseudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users' devices and improves the shared CRNP model by aggregating users' updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users' privacy leakage.

This paper suggests a mapless indoor localization using wifi received signal strength (RSS) of a smartphone, collected by multiple people. A new trajectory learning algorithm by combining a dynamic time warping and a machine learning technique is proposed in order to generate an alternative map. Moreover, we combine particle filter and Gaussian process (GP) for the position estimation, because it can use the alternative map as the probabilistic function (the prior), and can use probabilistic relationship (the likelihood) between wifi RSSs and location. Field experimental results confirm the usefulness of our algorithm when the map is not available and robustness against outliers, in that the accuracy of the proposed localization is similar to that using the true map information.