Indoor Localization Method Research Articles

Image‐Based Indoor Localization Using Smartphone Camera

With the increasing demand for location‐based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless‐based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image‐based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective‐n‐point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image‐based indoor localization methods do not require infrastructure and have a wide range of applications that include self‐driving, robot navigation, and augmented reality.

Wireless Localization Method Based on AHP‐WKNN and Amendatory AKF

In this paper, we propose an AHP‐WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K‐nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP‐WKNN method and the modified AKF can improve positioning accuracy effectively.

Smartphone based indoor localization using permanent magnets and artificial intelligence for pattern recognition

Smartphone-based indoor localization methods are frequently employed for position estimation of users inside enclosures like malls, conferences, and crowded venues. Existing solutions extensively use wireless technologies, like Wi-Fi, RFID, and magnetic sensing. However, these approaches depend on the presence of active beacons and suitable mapping surveys of the deployed areas, which render them highly sensitive to the local ambient field clutters. Thus, current localization systems often underperform. We embed small-volume and large-moment magnets in pre-known locations and arrange them in specific geometric forms. Each constellation of magnets creates a super-structure pattern of supervised magnetic signatures. These signatures constitute an unambiguous magnetic environment with respect to the moving sensor carrier. The localization algorithm learns the unique patterns of the scattered magnets during training and detects them from the ongoing streaming of data during localization. Our work innovates regarding two essential features: first, instead of relying on active magnetic transmitters, we deploy passive permanent magnets that do not require a power supply. Second, we perform localization based on smartphone motion rather than on static positioning of the magnetometer. Therefore, we present a novel and unique dynamic indoor localization method combined with artificial intelligence (AI) techniques for post-processing. Experimental results have demonstrated localization accuracy of 95% with a resolution of less than 1m.

An End-to-End BLE Indoor Localization Method Using LSTM

An End-to-End BLE Indoor Localization Method Using LSTM

An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

The specific objective of this study is to propose a low‐cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT‐aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT‐aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real‐world scenarios.

A Novel Weighted Localization Method in Wireless Sensor Networks Based on Hybrid RSS/AoA Measurements

A hybrid RSS/AOA indoor localization method based on error variance and measurement noise weighted least squares (ENWLS) is proposed. This method is based on three-dimensional wireless sensor networks, and achieves high-precision indoor positioning without increasing its complexity. We use the first-order Taylor approximation to approximate the linear weighted least square (WLS) error, and use the weighted least squares estimation to roughly estimate the location of the target, then determine the weight matrix by estimating the linear WLS error variance and the measured noise value on the sensor node. Simulation results show that our proposed method is better than other existing hybrid RSS/AOA localization methods.

Indoor location method based on UWB and inertial navigation fusion

Indoor location method based on UWB and inertial navigation fusion

Research on intelligent indoor location method based on Wi-Fi fingerprint

Research on intelligent indoor location method based on Wi-Fi fingerprint

A new Wi-Fi dynamic selection of nearest neighbor localization algorithm based on RSS characteristic value extraction by hybrid filtering

Fingerprinting localization based on Wi-Fi received signal strength (RSS) is the most widely used indoor localization method. It typically includes offline training and online matching phases. The selection of the RSS characteristic value is a key step. The weighted K nearest neighbor (WKNN) algorithm is the most commonly used position-determination algorithm. The mean value of the RSS data collected over a time interval is usually taken as its characteristic value. However, the RSS measurements contain Gaussian and non-Gaussian noise, which cannot be filtered out effectively by the mean value method. The traditional WKNN algorithm adopts a fixed . However, reference points far away from the test point (TP) may be selected as the nearest neighbors to participate in the position calculation, which may result in accuracy degradation. This paper proposes the weighted dynamic K nearest neighbor algorithm (WDKNN-HF), which utilizes a hybrid of particle filtering and Kalman filtering to extract the RSS characteristic value. In the online matching phase, a dynamic K matching algorithm based on Euclidean distances is developed to determine the coordinates of TPs. Two experiments are conducted in two different indoor scenes. Experimental results demonstrate that the proposed algorithm can obtain better positioning accuracy than existing algorithms, such as KNN, WKNN, enhanced-WKNN (EWKNN) and self-adaptive weighted K nearest neighbor (SAWKNN).

An alternative approach for robot localization inside pipes using RF spatial fadings

Accurate robot localization represents a challenge inside pipes due to the particular conditions that characterize this type of environment. Outdoor techniques (GPS in particular) do not work at all inside metal pipes, while traditional indoor localization methods based on camera or laser sensors do not perform well mainly due to a lack of external illumination and distinctive features along pipes. Moreover, humidity and slippery surfaces make wheel odometry unreliable. In this paper, we estimate the localization of a robot along a pipe with an alternative Radio Frequency (RF) approach. We first analyze wireless propagation in metallic pipes and propose a series of setups that allow us to obtain periodic RF spatial fadings (a sort of standing wave periodic pattern), together with the influence of the antenna position and orientation over these fadings. Subsequently, we propose a discrete RF odometry-like method, by means of counting the fadings while traversing them. The transversal fading analysis (number of antennas and cross-section position) makes it possible to increase the resolution of this method. Lastly, the model of the signal is used in a continuous approach serving as an RF map. The proposed localization methods outperform our previous contributions in terms of resolution, accuracy, reliability and robustness. Experimental results demonstrate the effectiveness of the RF-based strategy without the need for a previously known map of the scenario or any substantial modification of the existing infrastructure.

Indoor Parking Method Based on Cooperation of Intelligent Vehicle and Parking Lot

This paper designs an intelligent vehicle-parking lot cooperative indoor parking system architecture and a multi-vision sensor-based indoor vehicle detection and localization method to solve parking problems in large parking lots. The system consists of a parking lot sensor group, a parking lot server, and an intelligent vehicle terminal. The communication between the intelligent vehicle terminal and the parking lot is established through V2X technology. Sensors and parking lot servers are deployed in the parking lot to complete indoor vehicle positioning and parking space management. The intelligent vehicle terminal initiates parking/leaving requests and positioning requests to the parking lot. The parking lot server provides the parking lot real-time map, parking space information and localization information to the intelligent vehicle terminal, so as to complete the parking navigation or automatic parking in the indoor parking scene through the cooperation of the intelligent vehicle terminal and the parking lot. This paper verifies the effectiveness of the method and system developed in this paper through real parking scene experiments.

Pseudo Label-Driven Federated Learning-Based Decentralized Indoor Localization via Mobile Crowdsourcing

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 C entralized indoo R localizatio N method using P seudo-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.

Comparison of Indoor Localization Scheme for Multistory Environment

Outdoor localization nowadays is a part of our daily life. However, such useful localization method as the Global Positioning System (GPS) fails inside buildings. Popular alternative is to utilize the Indoor Positioning System (IPS) technologies that are accessible indoors. There is a wide range of IPS technologies that can be used within an indoor environment. Moreover, more attention has been given to IPS for multistory buildings recently. In indoor localization, different techniques and methods are used for distance and position estimation. This paper will focus on indoor localization technologies and the comparison of indoor localization methods focusing on multistory environment which will improve the localization accuracy through various models and techniques.

Research on Intelligent Indoor Location Method Based on WI-FI Fingerprint

In order to improve indoor location performance based on Wi-Fi fingerprint, the novel algorithm is put forward based on K-mean algorithm and fuzzy support vector machine. Firstly, the basic theories of Wi-Fi location technology, indoor location principle based on location fingerprint database are studied. Secondly, the basic theories of K-means algorithm, fuzzy support vector machine and nearest incremental algorithm are discussed, and the corresponding mathematical model is deduced. Third, the Wi-Fi fingerprint location algorithm procedure is designed. Finally, the simulation analysis is carried out, and results show that the proposed method has higher location performance.

Accurate Visible Light Positioning Using Multiple-Photodiode Receiver and Machine Learning

Visible light positioning (VLP) is a promising indoor localization method as it provides high positioning accuracy and allows for leveraging the existing lighting infrastructure. Photodiode (PD)-based receiver is a commonly used tag for VLP. However, a tag employing a single PD requires three or more luminaires to be visible. This article presents a VLP system that uses a custom-made tag utilizing multiple PDs. It applies received signal strength (RSS)-based fingerprinting using a weighted k-nearest neighbor (WkNN) algorithm for localization. Experimental results show that it is possible to localize using less than three luminaires with high accuracy. The Manhattan and Matusita distance metrics are found to provide lower localization accuracy than the Euclidean metric for the WkNN algorithm. The creation of a dense fingerprinting database through 2-D interpolation is presented as a method to reduce the cost of time and labor. The localization performance of the VLP system does not degrade noticeably with the fabricated database. The localization accuracy of the WkNN algorithm is shown to be better than that of a multilayer perceptron (MLP)-based regressor. The developed VLP system is also experimentally benchmarked against the HTC Vive showing comparable performance.

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor.

The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.

Error data analytics on RSS range-based localization

The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. For indoor localization, Received Signal Strength (RSS) is a convenient and low-cost measurement that has been adopted in many localization approaches. However, using RSS data for localization needs to solve a fundamental problem, that is, how accurate are these methods? The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data. In this proposed work, we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cramer-Rao Lower Bound (CRLB). Through mathematical techniques, the key factors that affect the accuracy of RSS-based localization methods are revealed, and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived. The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.

Landmark Graph-Based Indoor Localization

Indoor localization is important for a variety of applications, such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, the existing indoor localization methods that make use of spatial information are either computationally expensive or sensitive to the completeness of landmarks. In this article, we propose a novel, low-cost, high-accuracy indoor localization method based on a landmark graph. The experimental results show that the proposed method outperforms the state-of-the-art methods.

A 3D MAP AIDED DEEP LEARNING BASED INDOOR LOCALIZATION SYSTEM FOR SMART DEVICES

Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks.

Considering the variation of the received signal strength indicator (RSSI) in wireless networks, the objective of this study is to investigate and propose a method of indoor localization in order to improve the accuracy of localization that is compromised by RSSI variation. For this, quartile analysis is used for data pre-processing and the k-nearest neighbors (kNN) classifier is used for localization. In addition to the tests in a real environment, simulations were performed, varying many parameters related to the proposed method and the environment. In the real environment with reference points of 1.284 density per unit area (RPs/m2), the method presents zero-mean error in the localization in test points (TPs) coinciding with the RPs. In the simulated environment with a density of 0.327 RPs/m2, a mean error of 0.490 m for the localization of random TPs was achieved. These results are important contributions and allow us to conclude that the method is promising for locating objects in indoor environments.