Positioning Technology Research Articles

Study on terrain acquisition and processing technology of BDS-3 auxiliary mountain highway

The operation mode of UAV equipped with LiDAR can significantly reduce the efficiency and cost of highway terrain acquisition. At present, due to the complicated terrain and weak signal of mountain road, it is difficult to collect terrain data. In view of the above problems, this paper puts forward the technology of BDS assisted terrain acquisition and processing on complex mountain roads. This paper describes in detail the new signal unfuzzy acquisition and tracking algorithm and multi-antenna positioning and attitude determination technology and equipment of BDS-3 mountain highway, through millimeter wave Lidar altimetry technology, imitation ground flight, air-ground multi-source data combined with air three-direction method and other technologies. To realize efficient 3D collection and 3D reconstruction of massive original images of terrain of complex mountain traffic lines in the range of 100 km. Finally, a case study of 100 km mountain road is taken to verify the effectiveness of the method.

Improving PPP timing and time transfer based on signal distortion biases calibration

Abstract Precise point positioning (PPP) technology has been widely used for more than a decade in timing and time transfer. Pseudorange bias caused by signal distortion will reach up ±3 ns, which cannot be neglected in sub-nanosecond level PPP timing and time transfer. We propose to improve the performance of PPP timing and time transfer by considering signal distortion bias (SDB). In principle, SDB will affect PPP timing and time transfer by influencing the initial clock offset. Then, it is verified by theoretical and experimental analyses. The theoretical results show that the impact of SDB on PPP timing can reach up 1.8 ns in different regions. Also, the impact of SDB on time transfer can reach up 0.8 ns in different regions even if the baseline length and receiver types are the same. For experimental results, the accuracy of PPP timing and time transfer improves with SDB correction. For time transfer, the RMSs are reduced by 54.8%, 61.1% and 17.6% for GPS, BDS3 and Galileo in all links, respectively. The clock frequency consistency obtained from different systems has been improved. And the frequency stability of time link with Hydrogen clocks is improved by 53.0% at 10000s interval with SDB correction.

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Current Status and Key Technologies for New Underwater Communications, Positioning and Telemetry Technologies

Current Status and Key Technologies for New Underwater Communications, Positioning and Telemetry Technologies

An improved multiple-outlier-robust GNSS/INS EKF filer based on multiple statistical similarity measure

Abstract High-precision and highly reliable location information is the key support for autonomous driving applications. Global Navigation satellite system (GNSS)/Inertial navigation system (INS) integrated positioning is a typical high-precision positioning technology. However, satellite signals are easily affected by various interferences such as signal blocking and multipath problems, leading to performance degradation in complex urban environments. The traditional extended Kalman filtering (EKF) cannot handle observation outlier, and the system’s positioning accuracy sharply declines when the observations are affected by non-Gaussian heavy-tailed noise. Therefore, this paper presents an improved multiple-outlier-robust extended Kalman filter (I-MOREKF) for GNSS/INS Loosely Coupled Integration. A multiple statistical similarity measure (MSSM) is built to evaluate the similarity between two random vectors from dimension to dimension. Then, the I-MOREKF is proposed by maximizing a cost function based on the MSSM, and considering the robust estimation with IGG III model. The proposed model effectively removes multiple types of errors in complex environments. A vehicular test was carried out to validate the feasibility and performance of the proposed model. Simulated random gross errors are introduced in GNSS measurements, and the measurement noises are corrupted by heavy-tail noise. The results show that simulated gross errors can be successfully detected with I-MOREKF model, and the 3D position Root Meas Square Error is 0.0148 m for I-MOREKF, which are improved by 97.2%,13.5% and 52.9%, as compared with EKF, IGG Ⅲ and MOREKF.

Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model

Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model

Unmanned aerial vehicle landing localization technology based on visual dual-channel uniqueness coding

Unmanned aerial vehicle (UAV) mainly uses GPS (Global Positioning System) for positioning, but it is difficult to locate in areas where GPS signal is missing or being disturbed. Therefore, the relative positioning technology based on visual method is also widely used in UAV landing, but due to the influence of cross wind and the motion of the landing plane, the traditional visual landing sign and positioning method is prone to lose the positioning target at the end stage of the landing. Therefore, this article proposes a landing sign based on double-layer unique coding, which can realize continuous positioning in the horizontal direction and accurate positioning of large-scale changes in the vertical direction. Firstly, real-time image acquisition and image processing are carried out through the airborne camera. Based on the flight altitude and encoding extraction effect of the UAV, the red or blue channel component is extracted, and then the image is preprocessed, edge detection, line detection, coding grid extraction, etc. As long as any 5 × 5 small squares of the whole pattern can be extracted, the perspective-n-point algorithm is used to obtain the relative pose estimation between the UAV and the landing sign. Experiments show that the landing sign and recognition algorithm effectively improve the UAV landing reliability.

Indoor Positioning Technology Based on Pseudo Satellite

Indoor Positioning Technology Based on Pseudo Satellite

A Review on the High-Efficiency Detection and Precision Positioning Technology Application of Agricultural Robots

The advancement of agricultural technology has increasingly positioned robotic detection and localization techniques at the forefront, ensuring critical support for agricultural development through their accuracy and reliability. This paper provides an in-depth analysis of various methods used in detection and localization, including UWB, deep learning, SLAM, and multi-sensor fusion. In the domain of detection, the application of deep algorithms in assessing crop maturity and pest analysis is discussed. For localization, the accuracy of different methods in target positioning is examined. Additionally, the integration of convolutional neural networks and multi-sensor fusion with deep algorithms in agriculture is reviewed. The current methodologies effectively mitigate environmental interference, significantly enhancing the accuracy and reliability of agricultural robots. This study offers directional insights into the development of robotic detection and localization in agriculture, clarifying the future trajectory of this field and promoting the advancement of related technologies.

Nanosecond laser cleaning method for high-capacity fast storage and data multiplexing in ARM architecture

As the application of laser cleaning technology continues to expand, various fields have put forward higher requirements for the performance and quality of the operation, and at present, laser cleaning has problems such as low efficiency in handling repetitive tasks, and the system deployment and maintenance costs are high. This paper proposes a nanosecond laser cleaning method based on ARM architecture with high-capacity fast storage and data reuse, integrating high-capacity fast storage technique and data reuse strategy to optimize the laser cleaning process. In this paper, SDRAM is used to store and record the laser scanning path, Cache is integrated to weaken the data calling delay and improve the system response speed, and the scanning cycle is finely adjusted by combining positional modulation laser frequency technology to avoid the edge erosion caused by the motor deceleration and to ensure the homogeneity and accuracy of laser cleaning. The results indicate that, under the same output point conditions, the dynamic phase difference spiral algorithm in this paper outperforms the linear filling algorithm in terms of speed and data filling efficiency, reducing the burden of repetitive task calculations. While maintaining surface erosion uniformity during cleaning, excessive erosion is prevented, leading to a comprehensive enhancement in the overall efficiency and performance of laser cleaning operations.

Three-dimensional positioning and trajectory tracking of pigeons in large indoor spaces

Animal localization and trajectory tracking are of great value for the study of brain spatial cognition and navigation neural mechanisms. However, traditional optical lens video positioning techniques are limited in their scope due to factors such as camera perspective. For pigeons with excellent spatial cognition and navigation abilities, based on the beacon positioning technology, a three-dimensional (3D) trajectory positioning and tracking method suitable for large indoor spaces was proposed, and the corresponding positioning principle and hardware structure were provided. The results of in vitro and in vivo experiments showed that the system could achieve centimeter-level positioning and trajectory tracking of pigeons in a space of 360 cm × 200 cm × 245 cm. Compared with traditional optical lens video positioning techniques, this system has the advantages of large space, high precision, and high response speed. It not only helps to study the neural mechanisms of pigeon 3D spatial cognition and navigation, but also has high reference value for trajectory tracking of other animals.

A visible light positioning system for coal mine personnel based on convolutional recurrent neural network

Visible Light Positioning (VLP) technology exhibits promising potential for underground coal mine positioning. While numerous studies have explored VLP in indoor environments, attention to VLP systems for underground mines still needs to be improved. In this paper, based on the traditional indoor visible light communication channel model, the effects of unique features on VLP, such as irregular walls, inclined and rotating receiving ends, and coal dust particles in underground mines, are considered and modeled in detail. In order to improve the accuracy and reliability of personnel VLP in complex mine environments, this paper proposes a deep-learning-based VLP system for underground personnel in coal mines, which consists of two LEDs and four photodetectors (PDs), where the four PDs are mounted on miners' helmets, in conjunction with the actual mine environment. Due to environmental factors and noise, there are apparent spatio-temporal characteristics in the received signal strength of the PDs. Therefore, the system adopts a convolutional recurrent neural network (CRNN) for feature extraction. In the experimental phase, the method's average localization error was 11.24 cm, with 90% of the localization errors within 20.4 cm. The experimental results show that the system has high positioning accuracy and meets the requirements of personnel positioning in underground mines.

Research On Vision Based SLAM Technology For UAV

The application of Simultaneous Localization and Mapping (SLAM) technology in the field of UAVs is becoming more and more extensive and in-depth. This paper reviews its rapid development and breakthroughs in recent years, lists some problems that can still be improved, and prospects the development of SLAM technology in the future. The SLAM technology is a powerful and highly flexible technology, whose application in Unmanned Aerial Vehicles (UAVs) can improve their operation safety and autonomous work ability. This paper lists some current research results which show the application of SLAM technology based on vision in UAVs plays a key role in obstacle avoidance, positioning and path planning. At the same time, in order to further enhance the help of vision based SLAM technology to UAVs, many emerging technologies are introduced to integrate with it. This paper reviews the impact of integrating SLAM technology based on vision with GPS positioning technology, inertial measurement technology and other technologies on UAVs during operation as well. The relevant research in this paper will play a significant role in the application of vision based SLAM technology in UAVs and the integration with other technologies.

Research on Positioning and Navigation System of Greenhouse Mobile Robot Based on Multi-Sensor Fusion.

The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF). The system introduces a confidence optimization algorithm based on weakening non-line-of-sight (NLOS) for UWB positioning, obtaining calibrated UWB positioning results, which are then used as a baseline to correct the positioning errors generated by the IMU and ODOM. The extended Kalman filter (EKF) algorithm is employed to fuse multi-sensor data. To validate the feasibility of the system, experiments were conducted in a Chinese solar greenhouse. The results show that the proposed NLOS confidence optimization algorithm significantly improves UWB positioning accuracy by 60.05%. At a speed of 0.1 m/s, the root mean square error (RMSE) for lateral deviation is 0.038 m and for course deviation is 4.030°. This study provides a new approach for greenhouse positioning and navigation technology, achieving precise positioning and navigation in complex commercial greenhouse environments and narrow aisles, thereby laying a foundation for the intelligent development of greenhouses.

A new PPP one-way timing approach via BDS-3 PPP-B2b and short-message communication service

In the realm of time-frequency, precise point positioning (PPP) technology has been widely used, the current PPP time transfer or timing necessitates network communication for data transmission, which consequently leads to a reduction in the reliability of PPP timing. Based on the BDS-3 PPP service (PPP-B2b) and the global short message communication (GSMC) service, this study proposes a novel PPP one-way timing method that is not subject to distance limitations or the requirements of network communication. Users can directly obtain the difference with UTC(k). Then, the proposed PPP timing method was validated using four stations equipped with external high-performance atomic clocks, based on a dataset of eight days. According to the findings, the suggested PPP one-way timing technique might reach a timing accuracy of roughly 0.5 ns in environments without a network. In terms of frequency stability, the proposed PPP timing method exhibits a stability of approximately 1.4 × 10−13 at 960 s and a stability of about 3 × 10−14 at 15360 s. Further, even under GSMC communication delays of 1 min, 5 min, and 10 min, the proposed PPP one-way timing accuracy can still achieve better than 0.5 ns.

Indoor positioning systems based on visible light communication

Indoor positioning systems (IPS) are crucial in the ever-expanding landscape of wireless communication systems. These systems can generally be classified into four categories based on the underlying positioning technologies. One of them is based on visible light technology using Light Emitting Diodes (LEDs). In this paper, we first present a simple transmission model from the sender to the receiver in a visible light system. Subsequently, we construct a higher-precision, cost-effective model based on the technology proposed by Epsilon. Next, we introduce the most commonly used fingerprint recognition method and triangulation method in visible light indoor positioning. Traditional fingerprint recognition methods have issues such as significant errors. Recent studies have proposed various improvements based on different problems. To address the problem of high position errors, new research introduces an accurate Visible Light Positioning (VLP) method based on location fingerprinting and meta-heuristic. The method presented in this paper addresses the issue of position errors being affected in special cases. A typical IPS is an indoor positioning system utilizing dual-tone multi-frequency (DTMF) technology. The system design involves arranging LED lamps in a rectangular grid shape and using a basic positioning unit. The research discussed in this paper also explores the calculation of channel gains and simulation results of positioning errors. In the proposed positioning system, the coarse position is obtained through the ID acquisition process. When the mobile unit receives one or more LED signals, the ID acquisition process is used to obtain the nearest LEDs ID based on the highest Received Signal Strength Indication (RSSI). Simulation results indicate an average positioning error of 18 millimeters at a Signal-to-Noise Ratio (SNR) of 10 dB. Through the results of our research, we conclude that the wide applicability of VLC technology makes it a cornerstone in many fields. In our daily lives, optical communication positioning technology continues to improve, promising broader applications in the future.

Optimizing E-commerce Operational Efficiency and Customer Experience Through Data Analytics and Technological Innovation

With an emphasis on the e-commerce industry specifically, this paper examines the revolutionary role that data analytics and technological innovation play in enhancing the customer experience and streamlining e-commerce operations. It explores the ways in which cutting-edge technologies, including big data, augmented reality, virtual reality, and artificial intelligence, can be leveraged to precisely understand consumer preferences in order to provide engaging, effective, and personalized shopping experiences. The study emphasizes how these technologies are revolutionizing front-end consumer interactions with personalized product recommendations, immersive shopping experiences, and instant customer service, while also improving back-end operations like inventory management and supply chain optimization. Using Chewy.com as an example, this paper explores technology applied in virtual and augmented reality and artificial intelligence in pet e-commerce and demonstrates how their integration with technology indicates great benefits toward consumer needs, business growth, and competitive leverage within the dynamism of e-commerce. The aim of this paper is to aid e-commerce practitioners in capturing the most out of data and technology for business models, customer satisfaction, and market positioning in an ever-increasing digital market.

Research on Underground Positioning Technology for Buried PE Pipes Based on Acoustic Waveguide Resonance

Research on Underground Positioning Technology for Buried PE Pipes Based on Acoustic Waveguide Resonance

BDS-3 full-frequency precise point positioning: models and performance comparison

With the BDS-3 six-frequency integration, there are more choices to implement the precise point positioning (PPP) technology. To take full advantage of the multi-frequency combination, six typical BDS-3 six-frequency PPP models using uncombined observation (UC), five dual-frequency ionospheric-free (IF) combinations (IF2), four triple-frequency IF combinations (IF3), three four-frequency IF combinations (IF4), two five-frequency IF combinations (IF5), and a single six-frequency IF combination (IF6) were constructed and compared. The results indicate that the positioning accuracies of the six models are similar. The static positioning accuracies reach 4, 3–5, and 12–14 mm in the east, north, and up directions, respectively, while the kinematic positioning accuracies are 24–26, 16–17, and 42–43 mm, respectively. Regarding the convergence times, the UC model is slightly worse than the five IF combined models, except for some cases in the up direction. In the static mode, benefiting from the smallest noise amplification factor, the average convergence times of the IF6 model are the shortest, reaching 12.1, 5.5, and 13.4 min in the three directions, respectively, and are 7%, 15%, and 6% shorter than those of the UC model. In the kinematic mode, with the combination of more signals into a single IF combination, the noise level of the IF combined observables gradually decreases, but the convergence times gradually increase. The kinematic average convergence times of the IF2 model are 15.3, 3.6, and 18.0 min in the three directions, which are 6%, 22%, and 10% and 1%, 16%, and 20% shorter than those of the UC and IF6 models, respectively. In addition, the observation residuals, and the estimates of inter-frequency bias, tropospheric zenith wet delay, and receiver clock offsets from different models were compared and analyzed. The specific selection of the model should be based on actual situations.

Design and test of a novel converging groove-guided seed tube for precision seeding of maize

In precision seeding operations, the irregular shapes of maize seed particles and the suboptimal geometric structure of the seed tube contribute to the rolling and bouncing of seeds upon falling into the furrow, ultimately compromising seeding uniformity. To address this issue, a convergent groove-guided (CGG) seed tube was proposed based on the principle of brachistochrone curve considering friction (BCCF). The discrete element method (DEM) was employed to conduct a comprehensive phenomenological analysis and numerical investigation of particle guide characteristics. Bench tests and field experiments were designed to verify the simulation results. Through response surface analysis, the optimal seeding performance was achieved at a groove angle of 111.34°, tube incline angle of 1.56°, and ground speed of 7.2 km h−1, resulting in a lateral dispersion landing range of 11.10 mm, angular velocity of 17.38 rad s−1, seed landing speed variation coefficient of 2.29%, and plant spacing variation coefficient of 6.04%. Bench test results unveiled that, under a ground speed of 7.2 km h−1, the variation coefficient of plant spacing for CGG seed tubes was measured at 7.63%, with a deviation of 1.59% from the simulation results. At high ground speeds of 9.0–14.4 km h−1, the seed guiding performance was also better than traditional seed tubes. Consistent results were also verified through field experiments. Therefore, the CGG seed tube manifested superior seed guidance efficacy when juxtaposed with its conventional counterpart, and this design can provide technical support for the realization of seed-to-ground positioning technology.