High-precision Indoor Research Articles

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.

Multi-Floor Indoor Localization Based on Multi-Modal Sensors.

High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost cameras, it is possible to combine the above three methods to achieve more accurate, complete, and reliable location results. However, the existing SLAM rapidly advances, so hybrid visual and wireless approaches take advantage of this, in a straightforward manner, without exploring their interactions. In this paper, a high-precision multi-floor indoor positioning method, based on vision, wireless signal characteristics, and lidar is proposed. In the joint scheme, we, first, use the positioning data output in lidar SLAM as the theoretical reference position for visual images; then, use a WiFi signal to estimate the rough area, with likelihood probability; and, finally, use the visual image to fine-tune the floor-estimation and location results. Based on the numerical results, we show that the proposed joint localization scheme can achieve 0.62 m of 3D localization accuracy, on average, and a 1.24-m MSE for two-dimensional tracking trajectories, with an estimation accuracy for the floor equal to 89.22%. Meanwhile, the localization process takes less than 0.25 s, which is of great importance for practical implementation.

A Novel Carrier Loop Based on Coarse-to-Fine Weighted Adaptive Kalman Filter for Weak Communication-Positioning Integrated Signal.

We propose a communication-navigation integrated signal (CPIS), which is superimposed on the communication signal with power that does not affect the communication service, and realizes high-precision indoor positioning in a mobile communication network. Due to the occlusion of indoor obstacles and the power limitation of the positioning signal, existing carrier loop algorithms have large tracking errors in weak signal environments, which limits the positioning performance of the receiver in a complex environment. The carrier loop based on Kalman filtering (KF) has a good performance in respect of weak signals. However, the carrier frequency error of acquisition under weak signals is large, and the KF loop cannot converge quickly. Moreover, the KF algorithm based on fixed noise covariance increases or diverges in filtering error in complex environments. In this paper, a coarse-to-fine weighted adaptive Kalman filter (WAKF)-based carrier loop algorithm is proposed to solve the above problems of the receiver. In the coarse tracking stage, acquisition error reduction and bit synchronization are realized, and then a carrier loop based on Sage–Husa adaptive filtering is entered. Considering the shortcomings of the filter divergence caused by the negative covariance matrix of Sage–Husa in the filter update process, the weighted factor is given and UD decomposition is introduced to suppress the filtering divergence and improve the filtering accuracy. The simulation and actual environment test results show that the tracking sensitivity of the proposed algorithm is better than that based on the Sage–Husa adaptive filtering algorithm. In addition, compared with the weighted Sage–Husa AKF algorithm, the coarse-to-fine WAKF-based carrier loop algorithm converges faster.

An Ultra-Low Power, Small Size and High Precision Indoor Localization System Based on MEMS Ultrasonic Transducer Chips.

The development of Internet of Things (IoT) requires demanding accurate and low-power indoor localization. In this article, a high-precision 3-D ultrasonic indoor localization system with ultralow power is proposed. A new piezoelectric micromachined ultrasonic transducer (PMUT) chip with a slotted membrane is designed and fabricated as a receiver, breaking the dilemma of low resonant frequency and wide field of view required for indoor localization. The system works based on the time difference of arrival (TDoA), and an improved quantum genetic algorithm (QGA) is used to estimate the location. The results show that the system achieves centimeter-level positioning precision, which is among the best solutions nowadays. Due to the high performance and small size endowed by the PMUT, the receiver footprint reaches as small as 0.25 cm2 and power consumption could reach as low as 0.1 mW, which are far better than that of current indoor localization systems.

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.

A Robust Localization System Fusion Vision-CNN Relocalization and Progressive Scan Matching for Indoor Mobile Robots

Map-based, high-precision dynamic pose tracking and rapid relocalization in the case of unknown poses are very important for indoor navigation robots. This paper aims to propose a robust and high-precision indoor robot positioning algorithm that combines vision and laser sensor information. This algorithm mainly includes two parts: initialization and real-time pose tracking. The initialization component is mainly to solve the problem of the uncertainty of a robot’s initial pose and loss of pose tracking. First, the laser information is added to the posenetLSTM neural network that only considers image information as a geometric constraint, and the loss function is redesigned thereby improving global positioning accuracy. Second, on the basis of visual rough positioning, the branch and bound method is used to quickly search the high-precision pose of the robot. In the real-time tracking component, small-scale correlation sampling is performed on the high-resolution environment grid map, and the robot’s pose is dynamically tracked in real time. When the score of the tracking pose is lower than a certain threshold, the method of nonlinear graph optimization is used to perform the pose optimization. In order to prove the robustness, high precision, and real-time performance of the algorithm, this article first builds a simulation environment in Gazebo for evaluation, and then verifies the relevant performance of the algorithm through the Mir robot platform. Both simulations and experiments show that the introduction of laser information into the neural network can greatly improve the accuracy of vision relocalization and the system can quickly perform high-precision repositioning when the camera is not severely blocked. At the same time, compared with the pose tracking performance of the adaptive Monte Carlo localization (AMCL) algorithm, the proposed algorithm has also improved in accuracy and in real-time performance.

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.

A high precision indoor positioning method based on UKF

Aiming at the nonlinear filter problem in Ultra Wide Band (UWB) navigation and position, a high-order Unscented Kalman Filter (UKF) position method is proposed. On the one hand, the position and velocity are used as state variables to establish a nonlinear filtering model based on UWB position system. On the other hand, based on the fifth order cubature transform (CT), the analytical solution of the high-order unscented Kalman filter is obtained by introducing a free parameter δ. To verify the effectiveness of the proposed method, the Time of Arrival (TOA) location method, the least square method and fifth order CKF method are introduced as comparison methods. The simulation and experimental results show that the proposed high-order UKF method has good positioning accuracy in both static and dynamic UWB positioning methods.

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 based on robust LM visual inertial odometer and pseudosatellite

High-precision indoor positioning based on robust LM visual inertial odometer and pseudosatellite

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.

WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning.

High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.

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.

High-Precision Indoor Robot Dynamic Obstacle Detection with Laser and Camera

Indoor robot is a very common robot, and its main navigation method is laser SLAM. In the process of detecting dynamic obstacles with laser SLAM, the clustering effect is not obvious, the accuracy is low, and the real-time performance is poor. To solve this problem, a sensor fusion method of lidar and monocular camera is proposed, which improves the accuracy of detecting the position of moving obstacles. When processing lidar data, first preprocess the data, then use the BDSCAN algorithm to cluster the point cloud, and finally use the Kalman filter to predict and calculate the location of the moving obstacle. When processing the data of the monocular camera, the YOLOv3 algorithm is used to quickly detect the target and return the position of the monocular camera. After obtaining the camera and laser data, according to the installation position of the radar and camera, map the position calculated by the radar to the coordinates of the camera. If the coordinate difference after mapping is small, it can be considered that the two sensors have detected the same target. The result is returned to the display interface. Using this method, the efficiency and accuracy of indoor robots in detecting moving obstacles have been greatly improved.

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.

High precision indoor positioning algorithm of single LED lamp based on A-Bayes

With the rapid development of information technology, the application field of city positioning technology is more and more extensive. Traditional indoor positioning technology has shortcomings in positioning accuracy, equipment cost, security and so on. Visible light indoor positioning technology can effectively solve the above problems. This paper presents an Adaptive boosting- Bayes (A-Bayes) localization algorithm. In this algorithm, three tilted photodetectors and one horizontal PD are used as receivers to measure the received optical power of each PD and set up a fingerprint database. The Adaboost strong classifier is used to divide the test space into two regions: the center and the edge. In the online positioning stage, for the measured received optical power, the region to which it belongs is first determined, and then the Bayesian matching algorithm is used to match the regions to complete the positioning. The experimental results show that in the test space of, and the overall average positioning error is 9.27 cm. Compared with the traditional indoor positioning algorithm, the positioning error of the proposed algorithm is smaller; Compared with the traditional multi lamp location algorithm, the algorithm proposed in this paper avoids the interference between multi light source symbols, and the equipment is simpler; Compared with the traditional fingerprint matching algorithm, the proposed algorithm has faster positioning speed.

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.