Position Estimation Algorithm Research Articles

A fuzzy adaptive complementary filter for attitude estimation based on norm judgment

Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.

GPS-free roll and pitch estimation through pressure altitude aided inertial navigation system for a jet aircraft

This paper presents a novel approach for estimating roll and pitch angles of an aircraft by regulating the dead-reckoning drift using pressure altitude measurements. The proposed algorithm uses measurements from rate gyroscopes, pressure altitude, and an air data system to estimate the roll and pitch angles of an aircraft. This unique combination of sensor data offers a new alternative to traditional navigation approaches relying on accelerometers and satellite systems to estimate the aircraft attitude. Multiple flight data sets from a high-speed crewed US Navy T-38 jet are used to validate the proposed approach. Comparable levels of attitude estimation accuracy are reported thus demonstrating that the proposed algorithm is a viable alternative for attitude estimation purposes. Therefore, this approach can be used to improve the robustness and accuracy of attitude estimation algorithms in scenarios where GPS coverage is denied, unreliable, spoofed and/or jammed.

A Study on the UWB-Based Position Estimation Method Using Dead Reckoning Information for Active Driving in a Mapless Environment of Intelligent Wheelchairs

As the world enters an aging and super-aged society, the application of advanced technology in assistive devices to support the daily life of the elderly is becoming a hot issue. Among them, electric wheelchairs are representative assistive devices for the walking support of the elderly, and their structural form is similar to AGV and AMR. For this reason, research is being introduced and underway to guarantee the right to voluntarily move or improve the convenience of movement for the elderly and severely disabled people who have difficulties in operating a joystick for operating an electric wheelchair. Autonomous driving of mobile robots is a technology that configures prior information on the driving environment as a map DB and operates based on it. However, active driving assistance technology is needed because wheelchairs do not move in a limited space, but can move to a place without a prior map DB or vehicle boarding depending on the passenger’s intention to move. Therefore, a system for estimating the moving position and direction of the wheelchair is needed to develop a driving assistance technology in the relevant driving environment. In order to solve the above problem, this study proposes a position and direction estimation algorithm suitable for active driving of a wheelchair based on a UWB sensor. This proposal is an algorithm for estimating the position and direction of the wheelchair through the fusion of UWB, IMU, and encoder sensors. In this proposal, it is difficult to design an active driving assistance system for wheelchairs due to low accuracy, obstacles, and errors due to signal strength in the position and direction estimation with UWB sensors alone. Therefore, this study proposes a wheelchair driving position and direction estimation system that fuses the dead recording information of a wheelchair and the UWB-based position estimation technique based on sensors applied in IMU and encoders. Applying quantitative verification to the proposed technique, the direction estimation accuracy of the wheelchair of about 15.3° and the position estimation error average of ±15 cm were confirmed, and it was verified that a driving guide for active driving was possible when the sensor system proposed in a mapless environment of the wheelchair was installed at a specific destination.

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks.

This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter

This paper proposes an improved attitude estimation algorithm based on the extended Kalman filter (EKF), and it is applied to suppress the accuracy reduction in attitude estimation caused by fusing magnetometer data under large angular motion. In the proposed attitude estimation structure, the approximate variance of the estimated horizontal northbound magnetic vector is used to dynamically adjust the participation of magnetometer data in attitude estimation, as the approximate variance increases significantly under large angular motion and fusing magnetometer data will reduce estimation accuracy. A three-axis position-velocity controlled turntable is used to conduct rocking experiments for validating the proposed attitude estimation algorithm. The results show a significant improvement in yaw angle estimation accuracy with the proposed attitude estimation algorithm and correspondingly enhance the distribution of pitch and roll angle errors.

Autonomous Trajectory Planning for Ultrasound-Guided Real-Time Tracking of Suspicious Breast Tumor Targets

Ultrasound image guidance could display the movement of soft tissue in real time, which provides an important basis for the operation path selection of tumor for examinations and interventions such as precise localization and puncture biopsy. However, factors such as tissue deformation, patient motion, and instrument contact pose great challenges for real-time smooth localization tracking of target tissues and maintaining a suitable acoustic window. Not only does it reduce the accuracy of ultrasound examination, but also may prolong the examination time. In this paper, a real-time autonomous ultrasound robot trajectory planning framework is proposed to achieve global scanning of breast tissue and real-time local tracking of suspicious tumor targets. In addition, an ultrasound suspicious tumor target next moment motion attitude and position estimation algorithm and an acceleration-continuous online trajectory generation (ACOTG) algorithm are proposed. Experiments on breast phantom showed that the fluctuation difference of both global scanning and tracking scanning force was less than 5 N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 15%, and ACOTG takes less than 1 microsecond to calculate the next moment state. The position, velocity, and acceleration of the tracking path did not change abruptly when the unknown target changed. The ultrasound image of the suspected tumor target could always be smoothly maintained in a suitable acoustic window, while the errors between the center of the suspected tumor target image and the center of the acoustic window in the horizontal direction and the depth direction were less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 1 mm and 30 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 10%, respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this study was to solve the clinical problem of difficulty in keeping the suspected breast tumor target stably in the center of the field of view under the influence of tissue deformation, patient motion and instrument contact in ultrasound guidance. We proposed the idea of global conventional scanning and local online tracking to achieve the autonomous scanning, detecting and tracking of suspicious tumor targets within the global scope of the breast. ACOTG algorithm is proposed and implemented, which can make the ultrasound image of suspicious tumor target can be displayed stably in the center of the acoustic window. With the help of this system, the accuracy of ultrasound examination can be improved and the time of examination can be reduced. The proposed system can also be applied to ultrasound scanning of other parts of the body.

Soft Error Assessment of Attitude Estimation Algorithms Running on Resource-constrained Devices under Neutron Radiation

Soft Error Assessment of Attitude Estimation Algorithms Running on Resource-constrained Devices under Neutron Radiation

Improved modeling and fast in-field calibration of optical flow sensor for unmanned aerial vehicle position estimation

To better reveal the principle of off-the-shelf OFS that used for UAV position estimation, two measurement models are derived, namely the continuous-time and discrete-time models. Then the intrinsic equivalence of these two models is also proven. Based on these two models, a calibration method for OFS is introduced, which only makes use of in-situ rotation to determine the OFS’s parameters including its focal length and installation angles with respect to the UAV. Furthermore, an OFS-based UAV position estimation algorithm is presented, which stems from the proposed discrete-time model. The proposed OFS models, calibration method, and position estimation algorithm form a full solution for UAV displacement measurement via OFS, and its feasibility and effectiveness is verified through flight tests on a quadcopter UAV in outdoor environment.

Modelling of Interactive Experience and User Satisfaction in Interior Design Based on Virtual Reality Technology

Abstract This paper firstly analyzes the essential features of virtual reality technology, constructs the framework of the interior design system based on virtual reality technology, and designs the interaction content of the system as well as the interaction process architecture. Secondly, the interior design system is technically analyzed, and the system’s moving trajectory relative to the environment is determined by using specific point calculation and camera position estimation algorithms, and specific target detection algorithms extract specific target HOC features. Finally, the platform is tested for performance, and the data is quantitatively analyzed using relevant data collected from users in the system as a research sample. The results show that 43 users are satisfied with being able to visit freely in a continuous space, 45 users are all very satisfied with switching the elements in the scene, and they think that replacing the items in the scene is conducive to participating in the design of the program and selecting the decorations they are satisfied with. It shows that the interior design system based on VR technology can directly interact with virtual information in real time, which improves the user’s interactive experience and makes the design “human-oriented”, starting from human needs and feelings.

A general visual-servoing control strategy for active non-cooperative target tracking of space manipulators

Non-cooperative target tracking plays an important role in many space missions. Recent studies on non-cooperative target tracking are mostly concentrated in the field of passive target tracking. Passive non-cooperative target tracking can effectively be used in the initial phases of a close-range rendezvous manoeuvre. Instead, during the terminal phases of such manoeuvres, active object tracking(AOT) techniques are more convenient. This study propose a general visual servo strategy for the active non-cooperative target tracking of space manipulator. Our proposed method do not need any a priori information about the targets and can be used for space manipulators of any configuration and binocular cameras of any types. Our strategy includes a real-time and accurate position estimation algorithm for non-cooperative targets and a motion planning algorithm combined Position-based visual servo(PBVS) and Image-based visual servo(IBVS). The experiment verified that the proposed strategy well solved the active non-cooperative target tracking task and can be used as a baseline for solving such tracking problems which provides a reference for subsequent studies.

Real-Time Attitude Estimation for Spinning Projectiles by Magnetometer Based on an Adaptive Extended Kalman Filter.

The attitude measurement system based on geomagnetic information offers advantages such as small space requirements, fast response times, excellent resistance to high-overload conditions, and cost-effectiveness. However, during the flight process of a high-mobility guided spinning projectile, calculating attitude based on geomagnetic information often leads to non-unique solutions. To address this challenge, this paper proposes the Adaptive Extended Kalman Filter (AEKF) attitude estimation algorithm, based on geomagnetic vector information. Based on the analysis of the short-term attitude motion characteristics of the projectile, the Kalman state system equation and the nonlinear observation equation are established, along with real-time correction of the yaw angle and adaptive updates of parameters. The effectiveness of the algorithm is verified by simulations and experiments, demonstrating its ability to eliminate the dual solution problem inherent in traditional Single-Epoch algorithms. It notably improves the accuracy of pitch and roll angle estimation while providing precise estimates of attitude angular rates. Furthermore, the algorithm effectively mitigates the impact of magnetic disturbances on attitude determination. The proposed method has many potential applications in attitude measurement and navigation using geomagnetic data.

LoRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh

The positioning task of the Internet of Things (IoT) for outdoor environments requires that the node devices meet the requirements of low power consumption, long endurance, and low cost and that the positioning system can achieve high-precision positioning and wide-area coverage. Considering that traditional IoT positioning technology cannot balance the cost, energy consumption, and positioning performance well, a Wide-Area Positioning System Based on Long Range Mesh (LoRaWAPS), which is a low-cost and low-power outdoor positioning system with multi-anchor wireless mesh networking and multi-dimensional data fusion, is designed in this paper. To meet the need for a positioning system, a low-power consumption and high-reliability LoRa Mesh protocol is proposed. Aiming at the problem that the accuracy of LoRa ranging is easily affected by the non-line-of-sight (NLOS) path propagation of signals, a distance estimation algorithm based on the fusion of time of flight (TOF) and received signal strength indicator (RSSI) multi-sampling data is proposed. Furthermore, a position estimation algorithm is designed to minimize the posteriori RSSI error for multi-anchor cooperative estimation scenarios. Furthermore, the prototype of LoRaWAPS is built and tested in the campus environment. The experimental results show that the proposed system can provide reliable location service with low power and low cost.

Fiducial Markers Aided Position Estimation for Vertical Landing

Autonomous landing is a critical step in unmanned aerial vehicles (UAVs) and requires accurate position information. In cases where GPS signals are unavailable or obstructed, vision-based approaches can provide support for landing capabilities. In this study, a vision-based position estimation algorithm is being developed in conjunction with markers used in vertical take-off and landing (VTOL) systems of UAVs. The developed framework is designed to be compatible with various types of visual markers. The Kalman Filter is used to the calculated position to correct measurement errors and reduce the uncertainty of the position estimation. The developed algorithm is extensively tested in a simulation environment. The positions of a quadrotor aircraft are compared with real measurements to analyze the performance of the proposed vision-based position estimation algorithm. The results demonstrate an acceptable level of accuracy for the algorithm. This study discusses the potential of using visual markers and integrating Kalman filtering to improve the accuracy of positioning in vertical takeoff and landing systems of the UAVs. The development of a vision-based position estimation algorithm can enhance the reliability and precision of autonomous landing capabilities and enable successful landings in situations where GPS signals are limited or unavailable.

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination.

For the orbit operation of spacecraft, due to environmental factors, a star sensor installed on the spacecraft must have data censoring, which greatly reduces the attitude determination ability of the traditional combined-attitude-determination algorithm. To address this problem, this paper proposes an algorithm for high-precision attitude estimation based on a Tobit unscented Kalman filter. This is on the basis of establishing the nonlinear state equation of the integrated star sensor and gyroscope navigation system. The measurement update process of the unscented Kalman filter is improved. The Tobit model is used to describe the gyroscope drift when the star sensor fails. The latent measurement values are calculated using the probability statistics, and the measurement error covariance expression is derived. The proposed design is verified via computer simulations. When the star sensor fails for 15 min, the accuracy of the Tobit unscented Kalman filter based on the Tobit model is improved by approximately 90% compared to the unscented Kalman filter. Based on the results, the proposed filter can effectively estimate the error caused by the gyro drift, and the method is effective and feasible, provided there is theoretical support for the engineering practice.

Magnetometer-Only Kalman Filter Based Algorithms for High Accuracy Spacecraft Attitude Estimation (A Comparative Analysis)

Kalman Filter (KF) based algorithms are the most frequently employed attitude estimation algorithms. Typically, a fully observable system necessitates the use of two distinct sensor types. Therefore, relying on a single sensor, such as a magnetometer, for spacecraft attitude estimation is deemed to be a challenge. The present investigation centers on utilizing magnetometers as the exclusive sensor. Several KF based estimation algorithms have been designed and evaluated to give the designer of spacecraft Attitude and Orbit Control System (AOCS) the choice of a suitable algorithm for his mission based on quantitative measures. These algorithms are capable of effectively addressing nonlinearity in both process and measurement models. The algorithms under examination encompass the Extended Kalman Filter (EKF), Sequential Extended Kalman Filter (SEKF), Pseudo Linear Kalman Filter (PSELIKA), Unscented Kalman Filter (USKF), and Derivative Free Extended Kalman Filter (DFEKF). The comparison of the distinct algorithms hinges on key performance metrics, such as estimation error for each axis, computation time, and convergence rate. The resulting algorithms provide numerous benefits, such as diverse levels of high estimation accuracy (with estimation errors ranging from 0.014o to 0.14o), varying computational demands (execution time ranges from 0.0536s to 0.0584s), and the capability to converge despite large initial attitude estimation errors (which reached 170o). These properties render the algorithms appropriate for utilization by spacecraft designers in all operational modes, supplying high-precision attitude estimations better than (0.5o) despite high magnetometer noise levels, which reached (200 nT).

Human Motion Recognition in Dance Video Images Based on Attitude Estimation

With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers’ movement correction, dance self-help teaching, and other application scenarios but also for athletes’ movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers’ movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms.

An attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP

Aiming at the attitude estimation of the telescopic arm of the boarding bridge in the process of docking with the offshore wind turbine, an attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP is proposed in this paper. YOLOv5 algorithm is used to detect four marks in the offshore wind turbine logo image, and then the EPnP algorithm is used to solve attitude angles of the telescopic arm relative to the landing port of the offshore wind turbine according to the 2D pixel coordinates of the center point of each mark. The experimental results show that the attitude estimation error of the proposed algorithm is less than 0.4° and the data update frequency of the algorithm implemented in NVIDIA Jetson AGX Xavier is 25Hz, which meets the real-time and accuracy requirements for the attitude detection of the boarding bridge telescopic arm.

2-D distributed pose estimation of multi-agent systems using bearing measurements

This article studies distributed pose (orientation and position) estimation of leader–follower multi-agent systems over κ-layer graphs in 2-D plane. Only the leaders have access to their orientations and positions, while the followers can measure the relative bearings or (angular and linear) velocities in their unknown local coordinate frames. For the orientation estimation, the local relative bearings are used to obtain the relative orientations among the agents, based on which a distributed orientation estimation algorithm is proposed for each follower to estimate its orientation. For the position estimation, the local relative bearings are used to obtain the position constraints among the agents, and a distributed position estimation algorithm is proposed for each follower to estimate its position by solving its position constraints. Both the orientation and position estimation errors converge to zero asymptotically. A simulation example is given to verify the theoretical results.

Three-axis high-accuracy spacecraft attitude estimation via sequential extended Kalman filtering of single-axis magnetometer measurements

Magnetometer is a highly advantageous sensor for determining a spacecraft’s attitude. This article provides a solution to the problem of spacecraft attitude estimation using magnetometer measurements only. To ensure full observability of spacecraft attitude states, it is necessary to use at least two types of sensors. Consequently, utilizing a single sensor, such as the magnetometer, poses a significant challenge for any attitude estimation algorithm, including the extended Kalman filter (EKF). Moreover, implementing the EKF algorithm, or any other attitude estimation algorithm, is computationally intensive. To address these issues, an algorithm has been developed that estimates spacecraft attitude angles and attitude rates using a sequential extended Kalman filter (SEKF). This algorithm offers numerous benefits over those found in the literature such as high accuracy, low computational resource requirements, the ability to converge even with large initial attitude and angular velocity estimation errors, and the ability to function even if two of the three measurement channels of the magnetometer are not functioning. With these benefits, the developed SEKF algorithm is capable of operating in all spacecraft operational modes, delivering accurate performance and computation time. In spite of measurements with large noise values, the high accuracy achieved by the SEKF algorithm enables the magnetometer to serve as the sole source of attitude information, even if one or two magnetometer measurement channels are not functioning.

A robust attitude estimation algorithm for seabed inverted ultra-short baseline

The ultra-short baseline(USBL) is fixed upside down on the seafloor to position an autonomous underwater vehicle, which is the inverted ultra-short baseline (i-USBL). Three attitude angles between the i-USBL coordinate system and the navigation coordinate system will be produced when the USBL is installed on the seabed. Significant positioning error will occur if the USBL attitude is undefined. Besides, it also affects the accuracy of attitude angles estimation that the outliers produced when using USBL. In this paper, we propose a robust singular value decomposition (RSVD) method for estimating the attitude angles. Specifically, we develop an attitude calibration model based on the i-USBL positioning mode. The USBL attitude angles is solved using singular value decomposition (SVD) according to the least squares method. The new attitude angles are used to calculate the residuals of the coordinates at each measurement point. The residuals are used to calculate the weights of each point by the IGG III weighting function. The recalculation of attitude angles is performed by using up-to-date weights and SVD. The performance of SVD, the conventional Gaussian Newton iterative method (GN), and RSVD are compared in the simulations. The simulations show that the RSVD has high calibration accuracy and robustness.