High-precision Indoor Positioning Research Articles

A high precision indoor positioning system of BLE AOA based on ISSS algorithm

This paper investigates the Bluetooth direction finding technique and combines it with the improved signal subtraction subspace (ISSS) algorithm for precise indoor positioning. First, the phase offset caused by the electronic switch is solved by the phase compensation algorithm. Then, the ISSS algorithm is proposed by combining the spatial smoothing techniques algorithm, the density-based spatial clustering of applications with noise (DBSCON) techniques and the signal subtraction subspace (SSS), which reduces the interference of coherent signals and caused by moving between people in the room. Finally, different placement methods for receivers in different spatial shapes are proposed. In this paper, the positioning accuracy of the algorithm is tested using Silicon Labs' Bluetooth device. The results show that the average positioning errors are 0.92 m for single receivers and 0.30 m for dual receivers in a room size of 3 m * 3 m. Therefore, the method has a promising application in achieving accurate indoor positioning.

The IMU/UWB/odometer fusion positioning algorithm based on EKF

High-precision indoor positioning is the basis of factory intelligent management. However, the positioning accuracy will decrease because of the complex environment. This study proposes a multi-sensor fusion framework to fuse the data of Ultra Wide Band (UWB), inertial measurement unit (IMU), and odometer. First fuse the data from UWB and IMU by using EKF to obtain attitude, velocity, and position. And then fuse the speed and output with the odometer output using complementary filtering to increase accuracy. At the same time, the algorithm can output higher frequency positioning data. We evaluate the performance of the algorithm on mobile robots. Experimental data is from a 6-axis IMU and 5 UWB radio sensor devices. And the result shows that the position RMSE of our algorithm is 3.29 centimeters and our comprehensive localization algorithm can increase localization accuracy in complex environments compared with only UWB algorithm.

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.

Methodology for Large-Scale Camera Positioning to Enable Intelligent Self-Configuration.

The development of a self-configuring method for efficiently locating moving targets indoors could enable extraordinary advances in the control of industrial automatic production equipment. Being interactively connected, cameras that constitute a network represent a promising visual system for wireless positioning, with the ultimate goal of replacing or enhancing conventional sensors. Developing a highly efficient algorithm for collaborating cameras in the network is of particular interest. This paper presents an intelligent positioning system, which is capable of integrating visual information, obtained by large quantities of cameras, through self-configuration. The use of the extended Kalman filter predicts the position, velocity, acceleration and jerk (the third derivative of position) in the moving target. As a result, the camera-network-based visual positioning system is capable of locating a moving target with high precision: relative errors for positional parameters are all smaller than 10%; relative errors for linear velocities (vx, vy) are also kept to an acceptable level, i.e., lower than 20%. This presents the outstanding potential of this visual positioning system to assist in the industry of automation, including wireless intelligent control, high-precision indoor positioning, and navigation.

Toward 5G NR High-Precision Indoor Positioning via Channel Frequency Response: A New Paradigm and Dataset Generation Method

Location-based services (LBSs) provide necessary infrastructure for daily life, from bicycle sharing to nursing care. In contrast to traditional positioning methods such as Wi-Fi, Bluetooth, and ultra-wideband (UWB), fifth-generation (5G) networking is defined as a paradigm of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrated sensing and communication</i> (ISAC). With its advantages of wide-range coverage and indoor-outdoor integration, 5G is promising for high-precision positioning in indoor and urban canyon environments. However, 5G location studies face great obstacles due to the lack of commercialized 5G ISAC base stations that support positioning functions as well as publicly available datasets. In this paper, we first propose a dataset generation method, the Multilevel Feature Synthesis Method (Multilevel-FSM), to obtain positioning features. In particular, the features of a multiple-input multiple-output (MIMO) channel are flattened into a single image to increase the information density and improve feature expression, and data augmentation is performed to provide stronger robustness to noise. Subsequently, we devise a specially designed deep learning positioning method, Multipath Res-Inception (MPRI), trained on the proposed dataset to enhance positioning accuracy. Finally, the results of extensive experiments conducted in two typical 5G scenarios (indoors and urban canyon) show that Multilevel-FSM and MPRI outperform state-of-the-art works in accuracy, time overhead and robustness to noise.

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.

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

With the development of modern society, the demand for indoor positioning technology is higher and higher. The existing indoor positioning technology is difficult to really solve the problem of accuracy and achieve high-performance indoor positioning system design. Based on iBeacon equipment, this paper proposes a method to optimize received signal strength indication indoor positioning algorithm by using the Gaussian filtering method so as to reduce the adverse impact of multipath fading in indoor environment. In order to further improve the accuracy of indoor positioning algorithm, the stack automatic encoder in the deep neural network algorithm is introduced. Through the deep learning method, the high-dimensional information of the fingerprint database collected by the system can be extracted and the adverse impact of data noise on the database is also reduced. Through the simulation test of the system, it can be seen that the error of received signal strength indication indoor positioning algorithm based on extended Gaussian filter is small. Compared with the traditional iBeacon algorithm, the improved algorithm can achieve better data classification. The maximum error of the whole system is 1.02 M. Comprehensive analysis shows that the proposed indoor positioning system has a certain practical value and can be applied to the indoor positioning needs in a certain range of environment.

Multivariable Fingerprints With Random Forest Variable Selection for Indoor Positioning System

Indoor positioning technology with fingerprints based on 5th Generation (5G) mobile communication system has attracted extensive attention. Due to instability caused by non-line-of-sight and multipath propagation, received signal strength indicator (RSSI) values at different points may be similar. To address the problem and meet the requirement of high-precision indoor positioning, multivariable fingerprints with random forest variable selection (RFVS) is proposed in this paper. Ten 5G variables related to distance-power relationship are chosen to establish the multivariable fingerprinting database. In order to utilize correlation among the multivariable fingerprints, RFVS is used to sort variable importance and combinations. Five machine learning algorithms are used to calculate user equipment’s positions in three experimental scenarios for test. Results show that multi-layer perception (MLP) achieves best performance among the five positioning algorithms in all test scenarios. Combined with RFVS, MLP shows 31.42%, 39.56% and 30.54% accuracy improvement compared with that of RSSI only used in three test scenarios respectively. Besides, the best result of MLP achieves 1.198 m positioning error for 80% test samples in one of three experimental scenarios.

An Indoor Positioning Method Based on UWB and Visual Fusion.

Continuous positioning and tracking of multi-pedestrian targets is a common concern for large indoor space security, emergency evacuation, location services, and other application areas. Among the sensors used for positioning, the ultra-wide band (UWB) is a critical way to achieve high-precision indoor positioning. However, due to the existence of indoor Non-Line-of-Sight (NLOS) error, a single positioning system can no longer meet the requirement for positioning accuracy. This research aimed to design a high-precision and stable fusion positioning system which is based on the UWB and vision. The method uses the Hungarian algorithm to match the identity of the UWB and vision localization results, and, after successful matching, the fusion localization is performed by the federated Kalman filtering algorithm. In addition, due to the presence of colored noise in indoor positioning data, this paper also proposes a Kalman filtering algorithm based on principal component analysis (PCA). The advantage of this new filtering algorithm is that it does not have to establish the dynamics model of the distribution hypothesis and requires less calculation. The PCA algorithm is firstly used to minimize the correlation of the observables, thus providing a more reasonable Kalman gain by energy estimation and the denoised data, which are substituted into Kalman prediction equations. Experimental results show that the average accuracy of the UWB and visual fusion method is 25.3% higher than that of the UWB. The proposed method can effectively suppress the influence of NLOS error on the positioning accuracy because of the high stability and continuity of visual positioning. Furthermore, compared with the traditional Kalman filtering, the mean square error of the new filtering algorithm is reduced by 31.8%. After using the PCA-Kalman filtering, the colored noise is reduced and the Kalman gain becomes more reasonable, facilitating accurate estimation of the state by the filter.

Research and Prospect on Key Technologies of Indoor Positioning Based on Visible Light Communication

Indoor positioning has become a research hotspot because of its important application value in industrial production and daily life. Traditional wireless positioning technologies such as Wi Fi and Bluetooth are difficult to achieve high-precision indoor positioning due to electromagnetic interference and multipath effect. The modulated white LED can not only meet the needs of lighting, but also transmit the location information to achieve high-precision indoor positioning. This paper first introduces several modulation methods commonly used in visible light positioning system, compares the characteristics of different modulation methods, and proposes a modulation method suitable for visible light positioning; Then, two demodulation methods of the visible light positioning system are introduced and discussed; After that, several visible light location algorithms are introduced, and the performance of each algorithm is analyzed in detail; Finally, the problems in visible light positioning are discussed and prospected.

High Precision Indoor Positioning System Based on UWB/MINS Integration in NLOS Condition

High Precision Indoor Positioning System Based on UWB/MINS Integration in NLOS Condition

A Beidou + UWB + inertial Navigation Integrated Indoor and Outdoor Multi-Source Information Fusion Positioning System

The article analyses the high-precision indoor and outdoor positioning system and its positioning method based on Beidou/UWB. And analyse the principles and characteristics of UWB technology. The article proposes a sequential filtering fusion algorithm for the ship’s integrated navigation system. The basic idea of the algorithm is to use the first-order Markov process to establish the noise model of ship motion, and use the state expansion method to transform the state equation into a basic equation that conforms to the standard Kalman filter. Theoretical research and experimental simulation prove that the filter introduced in the article is matched with the system and has high filtering performance and accuracy.

Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array.

By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.

LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints.

Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.

Research on the UWB High Precision Indoor Positioning Method with the Heterogeneous Information Constraints

There are many obstacles in the UWB indoor positioning, such as installation location limitation of base stations, non-line-of-sight and so forward. In this paper, the high-precision indoor positioning model was discussed, and then the UWB indoor positioning method was given based on the heterogeneous data constraints, such as PDR, map and vision. Three indoor positioning models, the kinematic adaptive robust EKF UWB model based on the gain matrix, the UWB/PDR/Map coupled model, and the UWB/Vision fusion model were built and assessed, respectively. Afterward, the precision and the potential application scenarios of the three models were discussed via the practical tests. The test results showed that, with our method, the overall positioning accuracy reached around ¡À0.2 m under the conditions of the full or partial UWB signal coverage, available or interrupted line-of-sight, or undergoing other situational challenges such as the sparse texture and the continuous variation of the light strength.

Design of three-dimensional visualization indoor navigation system in smart city construction

In recent years, smart city has attracted more and more attention because of its high intelligence and informatization. In the future, smart community, smart parking lot and smart exhibition hall will enter people's life. In these smart services, indoor navigation and positioning technology is the basic technology. High precision indoor and outdoor seamless positioning technology plays a vital role in people's future life, is the science and technology source power to promote public innovation and entrepreneurship, and is an important part of supporting the national strategic needs. At present, indoor navigation is still facing challenges such as high cost, poor adaptability and poor endurance. In this paper, combined with threedimensional( 3D) visualization technology, indoor navigation is designed and applied. The results show that the indoor navigation system combines indoor positioning and artificial intelligence to achieve multichannel positioning and reduce power consumption, which can be widely used in the construction of smart city and bring convenience to people's life.

A Hybrid TDOA-Fingerprinting-Based Localization System for LTE Network

To the best of our knowledge, effective indoor positioning for the long term evolution (LTE) has not been addressed in the literature. In this paper, the uplink sounding reference signal (SRS), which contains timing and received signal strength (RSS) information, is exploited to achieve this task. Accordingly, an LTE based localization system using time-difference-of-arrival (TDOA) and fingerprint of RSS that operates in two steps is devised to meet the requirement of high-precision indoor positioning. In the first step, peak value detection is applied to the SRSs from spatially separated sensors to estimate the TDOAs, from which a coarse target location is computed with the use of least squares. Based on deep neural network, fingerprinting in a subarea containing the coarse solution is then performed to obtain the final position estimate. Due to the instability in the RSS values caused by non-line-of-sight and multipath propagation, we propose a novel extraction method to select reliable RSSs as input to the network. Experimental results show that our two-step localization system can provide an accuracy of sub-meter level.

The design of an indoor high-precision multi-source wireless positioning system in B5G environment

To meet the growing demand for indoor wireless positioning in B5G environment, a high-precision indoor positioning system is designed in this paper. The designed system adopts the fusion of multi-source positioning techniques, to chieve the reasonable cost and reliability. First, Considering the B5G environment, a two-steps fusion algorithm is proposed for the designed system, in which the UWB and ZigBee techniques, the trilateration and the maximum likelihood algorithms are fused in turn through the error comparison to obtain the high-precision estimated locations. Then, the proposed positioning system is verified in a plane space of 10 m×10 m by simulations, and the reference nodes of UWB and ZigBee are designed respectively. The received signal strength (RSS) ranging is used, based on which the distance measurements of both UWB and ZigBee are established. The simulation results show that the designed system using the multi-source fusion algorithm has better performance than other algorithms the terms of location accuracy, and can satisfy the requirements of high-precision indoor positioning.

A Holistic Approach to Guarantee the Reliability of Positioning Based on Carrier Phase for Indoor Pseudolite

The integrity monitoring algorithm based on pseudorange observations has been widely used outdoors and plays an important role in ensuring the reliability of positioning. However, pseudorange observations are greatly affected by the error sources such as multipath, clock drift, and noise in indoor pseudolite system, thus the pseudorange observations cannot be applied to high-precision indoor positioning. In general, double differenced (DD) carrier phase observations are used to obtain a high-precision indoor positioning result. What’s more, the carrier phase-based integrity monitoring (CRAIM) algorithm is applied to identify and exclude potential faults of the pseudolites. In this article, a holistic method is proposed to ensure the accuracy and reliability of positioning results. Firstly, if the reference pseudolite operates normally, extended Kalman filter is used for parameter estimation on the premise that the number of common pseudolites meets positioning requirements. Secondly, the innovation sequence in the Kalman filter is applied to construct test statistics and the corresponding threshold is determined from the Chi distribution with a given probability of false alert. The pseudolitehorizontal protection level (HPL) is calculated by the threshold and a prior probability of missed detection. Finally, compared the test statistics with the threshold to exclude the faultypseudolite for the reliability of positioning. The experiment results show that the proposed method improves the accuracy and stability of the results through faults detection and exclusion. This method ensures accuracies at the centimeter level for dynamic experiments and millimeter levels for static ones.

Indoor Positioning System With Cellular Network Assistance Based on Received Signal Strength Indication of Beacon

With the development of the mobile Internet, the demand for indoor location based services(ILBS) is increasing. Applications and devices in indoor environments can determine their position based on Received Signal Strength Indication(RSSI) of bluetooth Beacons. However, because of multipath effects, shadow fading and the blockage of obstacles, RSSI values are unstable for positioning. The propagation model are various in different environments, which have caused great difficulty in high-precision indoor positioning. In this paper, a novel positioning algorithm based on cellular network and bluetooth Beacon's RSSI is proposed for ILBS. The whole algorithm is divided into two parts: Offline Preparation and Online Positioning. During Offline Preparation, Beacon node is cellular networked in regular quadrilateral; a universal ranging model is derived from traditional ranging model for high-precision positioning. During Online Positioning, missing RSSI values are replaced firstly; next, Outliers Removed Median-Kalman Filter is used to process RSSI, which makes the signal smoother to range; then, the cell in which the mobile terminal is located is determined according to the strongest four RSSI values; after that, using filtered RSSI, distances without height influence could be calculated; finally, Weighted Multi-Point Positioning Algorithm With Cellular Network Assistance is used to calculate the real-time location of the mobile terminal. Experiment results show that our algorithm achieves average accuracy in 0.3m-0.5m and limits the maximum error within 0.8 m.