Indoor Positioning Method Research Articles

An Enhanced Indoor Positioning Method Based on RTT and RSS Measurements Under LOS/NLOS Environment

An Enhanced Indoor Positioning Method Based on RTT and RSS Measurements Under LOS/NLOS Environment

Optimum Weight Parameter of Weight Centroid Method for Indoor Positioning in Environment with Different Path Loss Exponent and Multipath Fading Effect

This paper proposes optimum weight parameter of weight centroid method for indoor positioning in environment with different path loss exponent and multipath fading effect. The indoor environment is defined as square with each side 10 m long. There is a transmitter installed in the center of each side. The distance error model is applied to calculate the distances between the location coordinates of user and the location coordinates of transmitters in environment with different path loss exponent and multipath fading. The total 100 location coordinates of user are estimated by using weight centroid method, varying weight parameter in the range of 1 to 10. The optimum weight parameter is defined as the case where mean of distance error of all positions is the least. The optimum weight parameter for environment with different path loss exponent and multipath fading effect is illustrated. The results show that the optimum weight parameter changes slightly with the path loss exponent and tends to change with the standard deviation of the multipath fading effect divided into 3 periods. The first period tends to increase slowly, the second period tends to increase rapidly, and the third period tends to fluctuate up and down. Moreover, the size of the indoor environment has very little impact on the optimum weight parameter. These results make it possible to select optimum weight parameter for case with path loss exponent and standard deviation of multipath fading effect that match or are close to the channel of indoor environment.

Near Relationship Enhanced Multisourced Data Fusion Method for Voice-Interactive Indoor Positioning

Near Relationship Enhanced Multisourced Data Fusion Method for Voice-Interactive Indoor Positioning

Research on Inertial Navigation and Environmental Correction Indoor Ultra-Wideband Ranging and Positioning Methods.

In contrast to outdoor environments, indoor positioning encounters signal propagation disruptions due to the presence of buildings, resulting in reduced accuracy and, at times, the inability to determine a location accurately. This research, leveraging the robust penetrative capabilities of Ultra-Wideband (UWB) signals in non-line-of-sight (NLOS) scenarios, introduces a methodology for refining ranging outcomes through a combination of inertial navigation and environmental adjustments to achieve high-precision spatial positioning. This approach systematically enhances the correction of signal propagation errors through walls. Initially, it digitalizes the spatial setting, preserving the error correction parameters. Subsequently, it employs inertial navigation to estimate spatial coordinates and delineate signal propagation pathways to achieve precise ranging results. It iteratively hones the positioning outcomes for enhanced precision. Empirical findings demonstrate that within NLOS conditions, compared to standalone UWB positioning and IMU/UWB fusion positioning using the ESKF algorithm, this positioning technique significantly enhances planar positioning accuracy while achieving a marginal elevation accuracy improvement, albeit with some residual deviations from actual values. Furthermore, this positioning methodology effectively rectifies results in NOLS settings, paving the way for a novel approach to optimize indoor positioning through UWB technology.

Towards interpretability in fingerprint based indoor positioning: May attention be with us

In a world increasingly pervaded by mobile and IoT devices, position-related information is gaining more and more importance. Highly accurate and standardized positioning techniques are not yet available for indoor scenarios, unlike for the outdoor case. The most commonly used method for indoor positioning is WiFi fingerprinting, which, despite its well-recognized advantages, still suffers from some notable limitations. Recently, approaches relying on deep learning showed promising results even though their lack of interpretability is still a significant drawback. In this paper, for the first time, we propose a domain-specific concept of interpretability, based on identifying the access points that are most relevant to a position estimate. The goal is to enhance the positioning process by gaining novel scientific knowledge and operational insights, without worsening the performance of the task. We show how it is possible to practically achieve both a local and a global notion of interpretability by means of a deep learning model equipped with an attention module, applied to a ranking based fingerprint representation. Since off-the-shelf application of attention does not guarantee to achieve a faithful nor plausible interpretation, we verified through a series of thoroughly designed quantitative and qualitative clustering based experiments the existence of a strong relationship between the obtained interpretations and the positioning domain. Finally, as by-product, we showed an example of how the new knowledge can be used in principle to improve positioning performance.

FedPos: A Federated Transfer Learning Framework for CSI-Based Wi-Fi Indoor Positioning

This article proposes FedPos, a federated transfer learning framework together with a novel position estimation method for Wi-Fi indoor positioning. Compared with traditional machine learning with privacy leakage problems and the cloud model trained through federated learning (FL) fails in personalization, the FedPos framework aggregates nonclassification layer parameters of models trained from different environments to build a robust and versatile encoder on the cloud server while preserving user privacy. The global cloud encoder can aggregate different classifiers and then construct personalized models for new users through fine-tuning. The proposed framework can be updated incrementally and is highly extensible. Specifically, we exploit channel state information (CSI) as the positioning feature and assess the transferability of a lightweight convolutional neural network (CNN) in unfamiliar environments. We evaluate the performance of our proposed framework and position estimation method in different indoor environments. Our experimental results indicate that the proposed framework can achieve a mean localization error of 42.18 cm in a 64-position living room. They also confirm that FedPos can achieve a 5.22% average localization performance boost and reduce the average model training time by about 34.78% when compared with normal training. By reusing part of the feature extractor layers that are trained from other environments, at least 65% of training data can be saved to achieve a localization performance that is similar to the base model. Overall, the proposed position estimation method can effectively improve localization accuracy as compared with seven other existing CSI-based methods.

The Indoor Positioning Method Time Difference of Arrival with Conic Curves Utilizing a Novel Networking RFID System

At present, the demand for accurate indoor positioning at a low cost is increasing. Based on the architecture of networking passive radio frequency identification (RFID) systems, research into passive location algorithms is important for finding a location solution with ultra-low cost, easy implementation, and no required maintenance. In this paper, TDACC (time difference of arrival with conic curves) based on signal propagation time is proposed, which breaks down the positioning problem into solving the intersection of an ellipse and a hyperbola. The results indicate that this method has a positioning error of 0 m in the absence of signal interference. When the time delay fluctuates to 1 ns and 2 ns, the average errors of TDACC are 0.19 m and 0.33 m, respectively. Different from other time-based localization methods, the proposed method only requires two distribution nodes without time synchronization, which reduces the system cost. These results will help to promote the deeper semantic communication level fusion of passive RFID. By improving the coordinate positioning in the semantic prior knowledge base, this method will lead to more efficient and accurate industry applications.

A Non-Line-of-Sight Mitigation Method for Indoor Ultra-Wideband Localization With Multiple Walls

Ultra-wideband (UWB) ranging techniques can provide accurate distance measurement in line-of-sight (LOS) conditions. However, various walls and obstacles in indoor non-line-of-sight (NLOS) environments, which obstruct the direct propagation of UWB signals, can generate significant ranging errors. Due to the complex through-wall UWB signal propagation, most conventional studies simplify the ranging error model by assuming that the incidence angle is zero or the relative permittivities for different walls are the same to improve the through-wall UWB localization performance. Considering walls are different in realistic settings, this paper presents a through-multiple-wall NLOS mitigation method for UWB indoor positioning. First, spatial geometric equilibrium equations of UWB through-wall propagation and a numerical method are developed for the precise modeling of UWB through-wall ranging errors. Then, calculated error maps are determined numerically without field measurements. Finally, the determined error maps are combined with a gray wolf optimization algorithm for localization. The proposed method is evaluated via field experiments with four rooms, three walls, and six penetration cases. The results demonstrate that the method can strongly mitigate the multi-wall NLOS effects on the performance of UWB positioning systems. This solution can reduce project costs and number of power supplies for UWB indoor positioning applications.

Hybrid indoor positioning for smart homes using WiFi and Bluetooth low energy technologies

In indoor positioning problems, GPS technology used in outdoor positioning needs to be improved due to the characteristic features of wireless signals. There currently needs to be a generally accepted standard method for indoor positioning. In this study, an ecosystem consisting of Beacon devices, Bluetooth intelligent devices, and Wi-Fi access points has been created to propose an effective indoor location determination method by using Wi-Fi and BLE technologies in a hybrid way. First, RSSI (Received Signal Strength Indicator) data were collected using the fingerprint method. Then, Kalman Filter and Savitzky Golay Filter are used in a hybrid manner to reduce the noise on the obtained signal data and make it more stable. In the first part, using the collected data from Wi-Fi and Beacon devices, the Non-linear least squares method (NLLS), including Levenberg-Marquardt (LM), is used for indoor tracking. In the second part, a fingerprinting-based approach is tested. K Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms estimate the area where the client is located. Each algorithm’s accuracy rate are calculated on different training and test data and presented.

Using iBeacon Components to Design and Fabricate Low-energy and Simple Indoor Positioning Method

Using iBeacon Components to Design and Fabricate Low-energy and Simple Indoor Positioning Method

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning.

In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces (Cmip) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model's overfitting problem.

A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning

We study the problem of indoor positioning, which is a fundamental service in managing and analyzing objects in indoor environments. Unpredictable signal interference sources increase the degeneration of the accuracy and robustness of existing solutions. Deep learning approaches have recently been widely studied to overcome these challenges and attain better performance. In this paper, we aim to develop efficient algorithms, such as the dual-encoder-condensed convolution (DECC) method, which can achieve high-precision positioning for indoor services. In particular, firstly, we develop a convolutional module to add the original channel state information to the location information. Secondly, to explore channel differences between different antennas, we adopt a dual-encoder stacking mechanism for parallel calculation. Thirdly, we develop two different convolution kernels to speed up convergence. Performance studies on the indoor scenario and the urban canyon scenario datasets demonstrate the efficiency and effectiveness of our new approach.

Research on indoor positioning and navigation method of AGV based on multi-sensor fusion

In comparison with current AGV indoor positioning and navigation methods, the combination of IMU+2D LiDAR is prone to signal loss and cumulative error in indoor, so we propose an AGV indoor navigation and positioning method with multi-sensor fusion of odometer, IMU, LiDAR, and UWB. The model is based on the traceless Kalman filter fusion algorithm, and the UWB positioning provides accurate initial coordinates for the traceless Kalman filter. To verify the feasibility of the method, simulations and experiments are done on MatlabR2020a and experimental platform. The results show that the UWB localization can reduce the accumulated errors brought by IMU, the traceless Kalman filter has good trajectory fitting, the method has good performance in system stability and localization accuracy, and achieves the expected goal.

A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting.

In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory–based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method.

A new monocular vision simultaneous localization and mapping process for high-precision positioning in structured indoor environments

High-precision positioning in areas with unobtrusive features is difficult to achieve with visual based indoor positioning solutions. Most current solutions offer low precision and are expensive and/or require a priori training. A ceiling distributed on the same plane is regularly arranged and widespread in structured indoor environments. We propose a new monocular vision simultaneous localization and mapping (NMV SLAM) method for high-precision indoor positioning. First, image morphology technology is adopted to extract the ceiling corner accurately. Second, the geometric solution based on the a priori ceiling map realizes global positioning and makes up for the deficiency of time-consuming nonlinear optimization. Experiments show that the root mean square error of position measured by the proposed NMV SLAM process is less than 5.43 mm in a room with an entire ceiling. Moreover, the proposed NMV SLAM processes an average of 30.61 frames in 1 s on an i5-9400F central processing unit.

A Method of Indoor Positioning by Signal Fitting and PDDA Algorithm Using BLE AOA Device

Though Global Navigation Satellite System (GNSS) has been pretty mature and widely used in our daily life, the positioning of the indoor environment cannot be realized due to the shielding of concretes or glasses by GNSS. Many positioning methods have been proposed to solve that problem, and Bluetooth Low Energy (BLE) is one of the most widely used wireless technologies. This paper takes BLE Angle of Arrival (AOA) as the research object and proposes a method based on signal fitting and Propagator Direct Data Acquisition (PDDA) angle estimation algorithm. This method can be applied to both linear antenna array and rectangular antenna array and has good positioning performance. The method proposed in this paper does not need to calculate the phase differences between antennas, making it unnecessary to calculate the intersection of cones during spatial positioning. The angle estimation is divided into two steps. Firstly, the frequency deviation of Constant Tone Extension (CTE) is calculated based on the IQ samples captured during the reference period, and the received signal matrix of all antennas is fitted based on frequency deviation. Secondly, the antenna received signal model is established based on hardware antenna arrangement, and the arrival angle is estimated by the PDDA algorithm. In order to verify the effectiveness of the proposed method, the plane and spatial positioning experiments are carried out using the BLE devices of Nordic Company, which shows a good positioning accuracy. In spatial positioning, the average positioning accuracy of BLE in channel 37, channel 38, and channel 39 is 0.59m, 0.74m, and 0.77m, respectively, which can meet the basic indoor positioning requirements and cover a large area by one receiver. Compared with the classical MUSIC algorithm, the PDDA algorithm has the same positioning accuracy but lower computational complexity, so it has certain advantages.

A 3D Indoor Positioning Method of Wireless Network with Single Base Station in Multipath Environment

The proliferation of indoor location-based services has increased the demand of indoor positioning technology. Severe multipath and coherence effects are the difference between signal propagation indoors and outdoors. Most existing indoor localization methods build their models in 2-dimensional space and try to avoid the influence of multipath. We propose a method to realize 3-dimensional indoor positioning with single base station by using multipath channel. The angles of multipath coherent signals are estimated by MIMO antenna and the delays are estimated by OFDM signal. To avoid the complicated calculation in joint estimation of angles and delays in 3-dimensional space, the angles and delays are estimated separately and matched by the proposed algorithm. The line-of-sight channel is differentiated by time delay, and the reflection paths for non-line-of-sight channels are established with angle information and indoor maps. Finally, combine the angle information of the reflection paths and the line-of-sight path to obtain the target position in 3-dimensional indoor space. We verified the method through simulation in an indoor space of 6 m × 8 m × 4.5 m . The positioning errors are submeter level in 95 % cases and less than 0.4m in 60 % cases. The proposed method requires only one base station and can be applied in most wireless networks. Compared with existing indoor localization methods, it has lower computational complexity and higher application potential.

Overview of WiFi fingerprinting‐based indoor positioning

Location-based services have attracted significant attention since the concept of Industry 4.0 was proposed and the ‘Internet +’ era began owing to its social and commercial value. Many scholars have attempted to introduce machine learning in indoor fingerprint positioning to improve indoor positioning accuracy, enhance system robustness, reduce costs, and improve the performance of indoor positioning methods. A comprehensive overview of indoor positioning technology, its methods, and classifications is provided. Furthermore, a detailed review of the application of WiFi fingerprinting and machine learning methods in indoor positioning is presented, and the advantages and disadvantages of these methods when applied to indoor positioning are analyzed. This study summarizes the difficulties and challenges encountered by indoor positioning and suggests development directions.

Indoor High-Precision 3D Positioning System Based on Visible-Light Communication Using Improved Whale Optimization Algorithm

Visible-light communication (VLC) is a promising method for indoor positioning. The received signal strength algorithm is the most widely used localization algorithm in visible-light positioning (VLP) systems. However, in a VLP system, the photodiode (PD) will have a small rotation angle during movement, which will result in a massive positioning error ignoring the angle. In this study, a three-dimensional (3D) indoor VLP system using an improved whale optimization algorithm (IWOA) is proposed to reduce the error caused by the PD rotation. Firstly, the model of the VLC system with the PD rotation angles is introduced. Secondly, a novel IWOA with an elite opposition-based learning strategy and Lévy flight strategy is proposed. The convergence speed and accuracy of the WOA are improved. Lastly, the IWOA algorithm is efficiently utilized to address the problem with the PD rotation in the indoor VLP system. Simulation results show that the average error of 3D positioning is 2.14 cm with no PD rotation. When the PD has a rotation angle, the average positioning error estimated by ignoring the rotation angle is 27.14 cm, while that estimated by considering the rotation angle is 7.85 cm. In the VLP system, the positioning error with the PD rotation angle is effectively reduced by the proposed algorithm, which can be applied in a variety of indoor location scenes.

Indoor Positioning Method Based on Infrared Vision and UWB Fusion

Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.