Position Algorithm Research Articles

Precise point positioning with mixed single- and dual-frequency GNSS observations from Android smartphones considering code-carrier inconsistency

The open access to raw Global Navigation Satellite System (GNSS) measurements from Android smartphones brings new possibilities to high-accuracy positioning with precise point positioning (PPP) technique. However, decimeter or even centimeter level positioning accuracy is still challengeable mainly due to the low completeness of dual-frequency observations and specific observation errors from a smartphone. Huawei P40 released in 2020 is the first module that can track multi-GNSS multi-frequency signals. Based on GNSS observations from P40 and a co-located geodetic-grade receiver, a method is proposed to extract and model the P40 code-carrier inconsistency errors. The P40 observation inconsistency presents a linear drift pattern with respect to time at each frequency and can even reach hundreds of meters. A correction model is then established which reduces the code-carrier differences to a level of few meters. Regarding to the P40 code-carrier inconsistency error signature, an uncombined precise point positioning (PPP) algorithm is developed with the inconsistency error treated with different schemes, including correction using established model, parameterization as a linear function, or estimated as an independent clock offset for carrier-phase observation. Meanwhile, to cope with the dual-frequency data deficiency of P40, the PPP algorithm is designed based on pure single-frequency processing which can handle mixed single- and dual-frequency observations. Positioning performances of three schemes are validated in both static and kinematic scenarios. With applying the code-carrier inconsistency schemes, P40 positioning precision reaches 0.2 m and 1.0 m for static and kinematic experiments, respectively, which is improved by an order of magnitude against the case without. Furthermore, precision can be even improved when P40 is equipped with an external geodetic-grade antenna. It reaches 5.8 and 18.2 cm in the static and kinematic tests, respectively, revealing improvements of 28.7% and 54.4% against the case without considering the inconsistencies, respectively. Such result is very encouraging and indicate a positioning accuracy level close to a geodetic receiver.

Research on positioning and mapping algorithm of sliding window optimization for substation monitoring robot

This paper proposed a positioning and mapping algorithm of sliding window optimization for substation monitoring robots to solve the problems of low precision location and poor robustness of the existing laser odometer in power inspection outdoor scene mapping. A tightly coupled simultaneous localization and mapping (SLAM) algorithm was proposed based on 16-wire LiDAR and inertial measurement unit (IMU). Firstly, the paper estimated the IMU and corrected the motion distortion of the laser point cloud by linear interpolation. Secondly, scene features were extracted by curvature and classified according to different feature properties. The local map was constructed in the sliding window using the inter-frame matching module. Finally, the joint optimization function was built using the distance and IMU data obtained by matching the frame with the local map. The paper used the KITTI and self-recorded datasets to conduct the experiments. The results show that the improved method’s accuracy outperforms the lightweight and ground-optimized LiDAR odometry and Mapping (Lego-LOAM) and LiDAR inertial odometry and mapping (LIO-Mapping) and draws a broad application prospect in power inspection field.

An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning.

Ambiguity resolution based on smartphone GNSS measurements can enable various potential applications that currently remain difficult due to ambiguity biases, especially under kinematic conditions. This study proposes an improved ambiguity resolution algorithm, which uses the search-and-shrink procedure coupled with the methods of the multi-epoch double-differenced residual test and the ambiguity majority tests for candidate vectors and ambiguities. By performing a static experiment with Xiaomi Mi 8, the AR efficiency of the proposed method is evaluated. Furthermore, a kinematic test with Google Pixel 5 verifies the effectiveness of the proposed method with improved positioning performance. In conclusion, centimeter-level smartphone positioning accuracy is achieved in both experiments, which is greatly improved compared with the float and traditional AR solutions.

Expectation Maximization Algorithm for GPS Positioning in Multipath Environments Based on Volterra Series

Expectation Maximization Algorithm for GPS Positioning in Multipath Environments Based on Volterra Series

Statistical analysis and suppression of vibration frequency bifurcation in diamond turning of Al 6061 mirror

In diamond turning of metal mirrors, the reflectivity heavily relies on the mirror surface roughness, and the vibration of machine tool is one of the crucial factors affecting the mirror surface roughness. Previous articles mostly focused on the vibration of the spindle, but in the process of spherical surface machining, the influence of Z-direction moving parts can not be ignored. In this work, by using the transfer matrix and prediction-correction method, a dynamics model is developed to determine the different order vibration frequencies for Z-direction moving parts. The vibration patterns are further revealed in terms of the state vector of each order component with consideration of the external forces, e.g. dynamic cutting forces and linear motor thrust harmonics. As the machining parameters increase, the vibration tends to become instability, and the bifurcation of vibration frequency appears. The sensitivity analysis results firstly show that the cutting depth and workpiece surface slope are the most important factors influencing the vibration frequency bifurcation phenomenon. The probability density distribution of the vibration frequency bifurcation is firstly established as a function of the workpiece shape by stochastic big data simulations, and an adaptive optimization algorithm of interpolation point position is established. Finally, the correctness of the model is verified by experiments, and the relative error is kept below 10%. Through the new interpolation point planning algorithm, the vibration is reduced by more than 50%. Our findings are of significant importance for improving the spherical/aspherical surface reflectivity of metal mirrors.

An Underwater Target Tracking Algorithm Based on Extended Kalman Filter

The technology of ocean monitoring is more advanced while the continuous development of industrial Internet. Unmanned underwater vehicle (UUV) is one of major ways for underwater environment monitoring, which makes high-precision positioning, and tracking of it is one of the key problems and needs to be solved urgently. An underwater acoustic positioning and tracking algorithm based on multiple beacons is proposed to reduce the positioning error of underwater acoustic positioning system caused by uncertain sound speed. The system consists of multiple GPS intelligent buoys floated on the sea surface and acoustic signal generator installed on the UUV. The effective sound speeds between the UUV and different buoys are considered to be unequal and estimated as the state parameters, together with the kinematic parameters of the UUV. Based on the kinematic equations of the UUV, the tracking model is obtained under the framework of the extended Kalman filter. Simulation results show that the proposed algorithm can correct the sound speed and improve the stability and accuracy of underwater acoustic positioning system.

An Iterative 5G Positioning and Synchronization Algorithm in NLOS Environments With Multi-Bounce Paths

5G positioning is a very promising area that presents many opportunities and challenges. Many existing techniques rely on multiple anchor nodes and line-of-sight (LOS) paths, or single reference node and single-bounce non-LOS (NLOS) paths. However, in dense multipath environments, identifying the LOS or single-bounce assumptions is challenging. The multi-bounce paths will make the positioning accuracy deteriorate significantly. We propose a robust 5G positioning algorithm in NLOS multipath environments. The corresponding positioning problem is formulated as an iterative and weighted least squares problem, and different weights are utilized to mitigate the effects of multi-bounce paths. Numerical simulations are carried out to evaluate the performance of the proposed algorithm. Compared with the benchmark positioning algorithms only using the single-bounce paths, similar positioning accuracy is achieved for the proposed algorithm.

Research on dynamic path planning method of electric inspection robot based on fuzzy neural network

In order to better address the issue of the coverage rate about power inspection robot to the working area, solving the difficulties in the navigation modeling and path planning of the intelligent control program is the focus of this paper. This paper first analyzes a multi-information fusion SLAM robot positioning and navigation algorithm, then uses the Kalman filter technology to locate through Voronoi mapping. Second, this paper proposes a five-layer neural network approach to how robots can plan and control routes in high-density scenarios. Third, the robot takes the surrounding obstacle distance, the target angle and the target distance as input parameters then the output parameters of the fuzzy neural network are the wheel speed and the steering angle. The inspection robot uses the sensors on the body to detect the surrounding environment’s parameter information. The dynamic path planning algorithm changes the path accordingly, prevents collision, and finally reaches the preset target. Finally, the paper collects the features obtained by Voronoi mapping in a large-scale simulation environment. The fuzzy neural network algorithm for detecting robots is validated in a complex environment, proving the effectiveness and accuracy of the algorithm.

Two terminals protection method for HVDC transmission lines based on maximal overlap discrete wavelet transform and cross-correlation coefficient of traveling wave

This study presents a novel method of HVDC protection based on the maximal overlap discrete wavelet transform (MODWT) and the kurtosis of cross correlation coefficient. Compared with the discrete wavelet transform (DWT), this method does not have data down sampling, and can realize fault identification and fault location at the same time. According to the high frequency content is different under different fault types and different fault area, firstly use of MODWT four layers of fault traveling wave decomposition, the double side of the decomposed high frequency component cross-correlation analysis; subsequently, kurtosis ratio relations to each other to judge fault zone and the fault types, the influence of noise on the protection method is also considered; finally, the maximum coordinate of cross-correlation function is used to calculate the fault location. At the same time, this paper analyzes the reduction of ranging accuracy caused by the decrease of sampling frequency, and further use of the interpolation algorithm for accurate positioning of internal fault. The simulation model of ±800 kV bipolar DC transmission is built in PSCAD/EMTDC, and the simulation analysis is carried out under different fault distance, transition resistance and fault type, the simulation results show that the protection method is not affected by the transition resistance, fault type and noise, and the fault location is more accurate.

An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost.

Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26-45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance.

Logic adaptive optimization model for dynamic temperature compensation of heating furnace based on proportion‐integral‐derivative position algorithm

AbstractIn order to improve the adaptive compensation control ability of the furnace dynamic temperature compensation logic, an adaptive optimal control model of the furnace dynamic temperature compensation logic based on proportion‐integral‐derivative (PID) position algorithm is proposed. Firstly, the constraint index model of the dynamic compensation logic control of the heating furnace temperature is constructed, and the target structure analysis of the electric heating furnace temperature control is realized by using the PID control mode. The platinum resistor is used to collect the temperature in the feedback link, and the controlled equipment converts electrical energy into heat energy for release. As the temperature rises, the difference between the two is sent to the fuzzy controller. The model fuzzy controller outputs an analog value, and the PID control system outputs different control parameters, thus realizing the parameter analysis and optimization design of the control law of the heating furnace temperature dynamic compensation logic control. The test results show that the method has good accuracy, with an error of 0.01 to 0.02, and has certain application value.

Energy Consumption Optimization for UAV Base Stations With Wind Compensation

In this letter, an energy-efficient algorithm for positioning of unmanned aerial vehicle-based base stations (UAV-BSs) is presented. The objective is to reduce the propulsion power consumption of UAV-BSs while not compromising the communication capacity of user equipments (UEs). As a significant step beyond state-of-the-art, we consider an effect of wind. To this end, we develop a new model of a propulsion energy consumption for the UAV-BSs reflecting an impact of wind. Furthermore, we propose a novel algorithm based on an ensemble learning optimizing the 3D trajectory of UAV-BSs over time in realistic environment with wind to reduce the propulsion energy consumption. The results show that the proposed approach reduces the propulsion energy consumption of UAV-BSs by up to 47% with only a negligible degradation in the UEs capacity compared to state-of-the-art works.

An Artificial Plant Community Algorithm for the Accurate Range-Free Positioning of Wireless Sensor Networks.

The problem of positioning wireless sensor networks is an important and challenging topic in all walks of life. Inspired by the evolution behavior of natural plant communities and traditional positioning algorithms, a novel positioning algorithm based on the behavior of artificial plant communities is designed and presented here. First, a mathematical model of the artificial plant community is established. Artificial plant communities survive in habitable places rich in water and nutrients, offering the best feasible solution to the problem of positioning a wireless sensor network; otherwise, they leave the non-habitable area, abandoning the feasible solution with poor fitness. Second, an artificial plant community algorithm is presented to solve the positioning problems encountered in a wireless sensor network. The artificial plant community algorithm includes three basic operations, namely seeding, growing, and fruiting. Unlike traditional artificial intelligence algorithms, which always have a fixed population size and only one fitness comparison per iteration, the artificial plant community algorithm has a variable population size and three fitness comparisons per iteration. After seeding by an original population size, the population size decreases during growth, as only the individuals with high fitness can survive, while the individuals with low fitness die. In fruiting, the population size recovers, and the individuals with higher fitness can learn from each other and produce more fruits. The optimal solution in each iterative computing process can be preserved as a parthenogenesis fruit for the next seeding operation. When seeding again, the fruits with high fitness can survive and be seeded, while the fruits with low fitness die, and a small number of new seeds are generated through random seeding. Through the continuous cycle of these three basic operations, the artificial plant community can use a fitness function to obtain accurate solutions to positioning problems in limited time. Third, experiments are conducted using different random networks, and the results verify that the proposed positioning algorithms can obtain good positioning accuracy with a small amount of computation, which is suitable for wireless sensor nodes with limited computing resources. Finally, the full text is summarized, and the technical deficiencies and future research directions are presented.

Morphometric Parameters and Geospatial Analysis for Flash Flood Susceptibility Assessment: A Case Study of Jeddah City along the Red Sea Coast, Saudi Arabia

Flash floods are one of the most common and destructive natural hazards, and recent flood events show their severe impact on Saudi Arabia. Flash floods are increasing year by year in duration and intensity, resulting in huge destructive impacts for the nation concerned. Anticipating the spatial patterns and occurrence of rainfall-induced floods is in high demand. Recent technique-based studies and their comprehensive results aid in understanding the flood potential of the drainage basins and in minimizing the risks of a threat to humans and of economic damage. Jeddah City is located at the western coast of the Red Sea in Saudi Arabia and is one of the most important coastal cities in the Arabian Kingdom. It has experienced several destructive flash flood events, particularly in 2009 and 2011, causing serious damage and significant loss of life. An analysis of the morphometric parameters using geospatial techniques provides significant insights into the hydrological response of the drainage basins to major heavy rainfall events. In this paper, two relative flood susceptibility scenarios were produced: drainage basin levels and very accurate pixel-level conditions. The morphometric comparison levels suggest that basins 1 and 7 are very high, whereas the other basins have moderate and low levels. The derived flood susceptibility map was integrated with the topographic position and wetness algorithms (TPI and TWI) through overlay processing. The integration analysis aids in realizing the relationship between the general basin morphometric characteristics and the in situ relief for producing the flood susceptibility spots over the entire basins. Thus, the method of this paper can be applied to evaluate the site-specific plan minimizing the effects of flash flood risks in similar areas.

Position and Attitude Determination in Urban Canyon with Tightly Coupled Sensor Fusion and a Prediction-Based GNSS Cycle Slip Detection Using Low-Cost Instruments.

We present a position and attitude estimation algorithm of moving platforms based on the tightly coupled sensor fusion of low-cost multi baseline GNSS, inertial, magnetic and barometric observations obtained by low-cost sensors and affordable dual-frequency GNSS receivers. The sensor fusion algorithm is realized by an Extended Kalman Filter and estimates the states including GNSS receiver inter-channel biases, integer ambiguities and non-GNSS receiver biases. Tightly coupled sensor fusion increases the reliability of the position and attitude solution in challenging environments such as urban canyons by utilizing the inertial observations in case of GNSS outage. Moreover, GNSS observations can be efficiently used to mitigate IMU sensor drifts. Standard GNSS cycle slips detection methods, such as the application of triple differences or linear combinations such as Melbourne-Wübbena combination and the phase ionospheric residual extended TurboEdit method. However, these techniques are not well suited for the localization in quickly changing environments such as urban canyons. We present a new method of tightly coupled sensor fusion supported by a prediction based cycle slip detection technique, applied to a GNSS setup using three antennas leading to multiple moving baselines on the platform. Thus, not only the GNSS signal properties but also the dynamics of the moving platform are considered in the cycle slip detection. The developed algorithm is tested in an open-sky validation measurement and two sets of measurement in an urban canyon area. The sensor fusion algorithm processes the data sets using the proposed prediction-based cycle slip method, the loss-of-lock indicator-based, and for comparison, the Melbourne-Wübbena and the TurboEdit cycle slip detection methods are also included. The obtained position and attitude estimation results are compared to the internal solution of raw data source GNSS receivers and to the observations of a high-accuracy GNSS/INS unit including a fiber optic gyro. The validation test confirms the proper cycle slip detection in an ideal environment. The more challenging urban canyon test results show the reliability and the accuracy of the proposed method. In the case of the second urban canyon test, the proposed method improved the integer ambiguity resolution success rate by 19% and these results show the lowest horizontal and vertical coordinate distortion in comparison of the linear combination and the loss-of-lock-based cycle slip methods.

Performance of a Full-Scale Upstream MAPS-Based Verification Device for Radiotherapy.

Intensity-modulated radiotherapy is a widely used technique for accurately targeting cancerous tumours in difficult locations using dynamically shaped beams. This is ideally accompanied by real-time independent verification. Monolithic active pixel sensors are a viable candidate for providing upstream beam monitoring during treatment. We have already demonstrated that a Monolithic Active Pixel Sensor (MAPS)-based system can fulfill all clinical requirements except for the minimum required size. Here, we report the performance of a large-scale demonstrator system consisting of a matrix of 2 × 2 sensors, which is large enough to cover almost all radiotherapy treatment fields when affixed to the shadow tray of the LINAC head. When building a matrix structure, a small dead area is inevitable. Here, we report that with a newly developed position algorithm, leaf positions can be reconstructed over the entire range with a position resolution of below ∼200 μm in the centre of the sensor, which worsens to just below 300 μm in the middle of the gap between two sensors. A leaf position resolution below 300 μm results in a dose error below 2%, which is good enough for clinical deployment.

Scheduling optimization for production of prefabricated components with parallel work of serial machines

Scheduling optimization of manufacturing prefabricated components is critical for construction in enhancing efficiency and productivity. However, problems of low formulation efficiency and low resource utilization still exist, impeding productivity and increasing costs. Although numerous efforts have been devoted to scheduling optimization, limited research has considered the parallel work of serial machines in production lines, leading to a deviation between actual situations and expectations. Therefore, a new optimization method is proposed with two major contributions. First, a Prefabricated Components Production Scheduling (PCPS) model considering the parallel work of serial machines is established, containing 3 new constraint conditions. a Position Algorithm (PA) is also designed to facilitate a constraint related to the locations of component groups. Second, a genetic algorithm-based method is designed to seek potential optimal scheduling schemes. The model is demonstrated and evaluated in an actual case. The results show that: (1) Optimal schedules calculated by the PCPS model can reduce penalty cost by 28.6% and total completion time by 14.9% compared to the case without considering the parallel work of serial machines. (2) Optimal schedules can effectively reduce penalty cost by 63.9% and total completion time by 34.4% compared to the empirical method of ascending order by the due date. The optimal schedules can provide a solution for more detailed and practical scheduling. For future improvement, other factors affecting productivity, such as operator proficiency, will be added to the model constraints.

Design of Multi-sensor Fusion Positioning and Navigation Method Based on Federated Kalman Filter for Low-Speed Rotorcraft of Atmospheric Detection

A rotorcraft needs to accurately sample the predetermined path at a low flight rate when performing atmospheric sounding tasks; thus, it is particularly critical to obtain accurate positioning and navigation information in real-time. Hence, this paper designs a multi-sensor fusion positioning and navigation algorithm for atmospheric detection of low-speed rotorcraft. The multi-sensor fusion positioning and navigation algorithm for velocity and spatial position are presented based on the Federated Kalman Filter combined with the accelerometer, Global Positioning System (GPS), and barometer. The experimental results show that the fusion algorithm result of the velocity and spatial position is closer to the actual position than a single sensor. In conclusion, this paper’s multi-sensor fusion positioning and navigation algorithm overcomes the problems of low positioning accuracy of a single sensor and cumulative errors and effectively improves positioning accuracy, real-time, and stability.

The Integrated Strategy of LIDAR Points’ Evaluation Localization and Lateral Tracking Optimization for a New Parking Robot's Tight Space Transportation

With the increase of car's number, the problem of insufficient parking space in cities is becoming more and more prominent. The high-density parking lots using parking robots can greatly improve space utilization. However, there is a great collision risk in the parking lot's tight space navigation. So, precise LIDAR localization and trajectory tracking control are the key technologies of autonomous driving along a predetermined path. Due to the laser points’ sparse character, the LIDAR points’ extracted features have distribution errors. In order to further optimize the LIDAR's localization, this article establishes the feature's error ellipsoid model, and the model's information and error entropies are calculated separately. The error entropy is utilized to optimize the feature-matching weight and improve the localization accuracy. Based on the higher localization result, a state-of-the-art lateral tracking model is proposed to address the challenges faced by the long parking robot. Then, the adaptive fuzzy control is designed to achieve precise control of wheel motion. Finally, an experimental platform is built to compare the effectiveness of different positioning and tracking algorithms. The comparison results show that the integration of laser evaluation localization and lateral tracking optimization algorithm can provides a collision-free improvement of 50% in the parking lot's tight spaces.

Evaluation and Characterization of the Influence of Solar Position Algorithm on the Performance of Parabolic Trough Solar System

For the open loop control system based on the solar position algorithm (SPA), without sensor correction, the error of SPA will bring the tracking error directly to decrease the efficiency of the solar system. By comparing with SPA proposed by NREL, this paper first evaluates main SPAs with different accuracy and presents the error of SPA on tracking error. Based on the annual average efficiency of the solar trough system, this paper evaluates the impact of the tracking error caused by SPA on the solar trough system, and proposes that the average SPA calculation error can be applied to characterize the impact of SPA on the trough solar system. By making a comparison of solar trough system efficiency calculated with fixed annual average tracking error and normal constantly changing tracking error within a year, respectively, the evaluation results show that for most SPAs, the introduced error is less than 0.05%, and only a few empirical algorithms with large tracking errors introduce larger errors, but they are not suitable for the trough solar system. Therefore, the SPA evaluation method proposed in this paper is applicable to the solar trough system.