Position Estimation Method Research Articles

Fast and Robust Position and Attitude Estimation Method Based on MARG Sensors

With the development of Internet of Things (IoT), people’s demand for location-based services is increasingly urgent. Based on magnetometer, accelerometer, and rate gyro, i.e., MARG sensors, the position and attitude estimation methods such as complementary filter (CF), Kalman filter (KF), and their various modifications have been the research hot spot. However, the CF-based methods are empirical and lack robustness; the KF-based methods are memory-free observers, whose solution may diverge when the filter lacks uniform observability. In this article, a virtual-measurement-combined extended KF (VMC-EKF) method is proposed by fusing the carrier’s motion state with EKF method. Similar to the graph optimization, the proposed method can measure the key information in VMC phase, and thus remove the requirement of uniform observability. The number of virtual measurements can be estimated based on carrier’s motion state and its gradient, which determines the number of iterations of the prediction phase and the correction phase. In order to verify the performance of the proposed method, a series of numerical simulation experiments, turntable experiments, and foot-mounted experiments are carried out. The corresponding testing platform is set up based on MPU9250, which is a typical and low-cost motion tracking integrated circuit (IC) of MARG sensors. The raw data of MARG sensors can communicate with the host computer via wired or wireless communication, and then be imported into MATLAB for processing and analysis by the compared methods. The test results show that the proposed method can achieve fast convergence of attitude estimation and avoid the divergence of position estimation compared with the state-of-the-art methods.

Accuracy Evaluation and Modification Method for Estimating Point Light Spot Positions with Gaussian Fitting Algorithm

This paper presents an accuracy evaluation method for point light spot position estimation with a Gaussian fitting algorithm combined with a centroid algorithm. Using centroid algorithm in the region around the Gaussian fitting distribution position estimation gets a position estimation value, and it is found that the deviation between this value and the Gaussian fitting algorithm position estimation approximately indicates the deviation between the Gaussian fitting algorithm position estimation and the real one. And at the basis of this finding, a modification method is proposed for position estimation with the Gaussian fitting algorithm. Experimental results show that the accuracy evaluation errors are less than 0.62 nm for 100- and 500-nm diameter fluorescent nanospheres, and the proposed modification method can enhance the accuracy by 3.1 nm.

Observers for High-Speed Sensorless PMSM Drives: Design Methods, Tuning Challenges and Future Trends

Rotor position and speed estimation methods are consolidated to reduce the cost and volume of permanent magnet synchronous motor (PMSM) sensorless control drives while maintaining high performance. Advanced nonlinear algorithms require accurate design for precise estimation tracking throughout the entire PMSM operation range. This paper presents a broad review of the main high-speed estimation methods for sensorless PMSM drives. First, the stability constraints and design methodologies of the main estimation techniques presented in the literature are discussed. In the second part, it is investigated the new observer design trends, which are used under non-ideal conditions, such as robustness to distortions, the effects of parameter variation, sensorless parameter estimation, and low sampling-frequency-to-speed ratio operation. Future trends on the design of observers for high-speed sensorless PMSM drives are also discussed.

Position and orientation estimation method using independent RTK-GNSS measurements

Position and orientation estimation method using independent RTK-GNSS measurements

UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

In this study, the unscented Kalman filter-based method was introduced as a new technique for position estimation of the two-degree-of-freedom facade cleaning robot known as the Dual Ascender Robot (DAR). While other facade cleaning robots use a winch, the DAR uses an ascender, resulting in rope slip inside the ascender. Rope slip does easily cause errors in length data, so DARs cannot be easily controlled based on length data as in the case of most facade cleaning robots. Therefore, the DARs estimate the length data and use it through position estimation to overcome the rope slip for control. DARs use a rope length-based sensor fusion method for position estimation. This method employs position data based on both length data and angle data to estimate the position; however, it is difficult to use for long periods of time owing to the increased error that accumulates with time. Therefore, the use of position data based on angle data is proposed herein via application of the unscented Kalman filter. This unscented Kalman filter-based method is tested to confirm that the positional estimation performance is improved relative to that achieved via the previously used method. The performance improvements are compared in terms of accuracy and repeatability using the double ball bar method, and the errors in accuracy and repeatability are found to be reduced by approximately 2–3 times.

Development of gripping position estimation method by transfer learning for handling robot

Development of gripping position estimation method by transfer learning for handling robot

Analog-Hall-Sensor-Based Position Detection Method With Temperature Compensation for Permanent-Magnet Linear Motor

Analog Hall sensors are utilized to detect the mover position of tubular permanent-magnet synchronous linear motors because of their low cost and small size. Typical Hall-sensor-based position estimation methods rely on additional displacement sensors, such as optical grating rulers, to identify the measurement model of the Hall sensors, but this reliance increases the difficulty of implementation of these methods. Moreover, most position detection methods disregard temperature change in the motors, leading to a position estimation error. Thus, a measurement model that considers temperature change is established in this study to overcome these problems. An algorithm consisting of particle swarm optimization and a successive solving algorithm (SSA) is used to identify the measurement model using only Hall sensors. A new SSA with temperature compensation is proposed for real-time position estimation. In this method, the forgetting factor recursive least-squares algorithm is modified to update the measurement model at the present temperature to avoid model mismatch. The proposed method is validated using a self-designed experimental platform. The experimental results show that the model’s identification accuracy is equivalent to that of the fast Fourier transform algorithm using an optical grating ruler. The position estimation error is less than $120~\mu \text{m}$ under different operating conditions.

Sensorless attitude estimation of three-degree-of-freedom actuator for image stabilization

Space recognition is a crucial issue for autonomous systems such as unmanned aerial vehicles, walking robots, and autonomous cars. These systems are necessary to obtain information using a camera while moving. However, reduction in recognition accuracy and system size increase has created problems. To solve these problems, we proposed the three-degree-of-freedom (DOF) actuator for image stabilization. This paper proposes a novel initial position estimation method and sensorless attitude estimation method for the three-DOF actuator to further downsize and reduce weight. The effectiveness of the proposed methods is verified by a magnetic field analysis, using a three-dimensional finite element method and numerical simulations.

An Improved Pulse Voltage Injection Based Initial Rotor Position Estimation Method for PMSM

It’s essential to obtain the initial rotor position of PMSM to prevent the reversal of the rotor and to ensure the maximum electromagnetic torque output during the startup process. An improved pulse-voltage injection-based initial rotor position estimation method is proposed in this paper. Different from the conventional pulse-voltage-injection-based method that only utilizes the information about the virtual d-axis current for the voltage vector injected to PMSMs, the information about the virtual q-axis current is also utilized in the proposed method to reduce the estimation errors caused by the current sampling errors. The experimental results indicate that the estimation accuracy can be greatly improved.

Predictive Controllers for Dual-Voltage Vector Current-Slope Sensorless IPMSM Drives

A rotor position estimation method based on a dual-voltage vector modulation technique for an IPMSM drive system is proposed in this paper. This method effectively increases the differences of the current slope in each switching state when compared to a single-voltage vector modulation technique, and it improves the current tracking capability of the current control as well as the accuracy of the rotor position estimation. The duty cycles of the dual-voltage vectors are systematically derived to generate the PWM switching states of the inverter. By measuring the current slope in each PWM switching state, the rotor position is easily estimated. In addition, the pulse width of the voltage vectors is not too narrow and does not require any extension or compensation. A predictive speed controller and a predictive current controller are designed and implemented to improve the dynamic responses of the drive system. All of the control algorithms, including dual-voltage vector modulation, rotor position estimation, and predictive control are realized through a DSP TMS-320F-2808. Experimental results show the proposed method provides an adjustable speed from 1 r/min to 2000 r/min with good performance, including transient responses, external load disturbance rejection, reversing speed, and tracking capability.

A Sliding-Mode Position Estimation Method With Chattering Suppression for LCL-Equipped High-Speed Surface-Mounted PMSM Drives

This paper proposes a sliding-mode position estimation method for high-speed surface-mounted permanent magnet synchronous machines with LCL filter. The implementation of the LCL filter aims at smoothing the motor current and reducing the iron loss caused by the harmonic currents. Firstly, the discrete-time model of the LCL-filtered motor drive system is developed. Based on the developed model, the sliding-mode observer is proposed with more robustness against the parameter variation to estimate the back-EMF, which contains the information of the rotor speed and position. Because of the elimination of the capacitor voltage sensors, the augmented sliding surface is designed to achieve arbitrary pole placement with only output feedback. Besides, considering the analog-to-digital scaling error and PWM harmonics, a reaching law with enhanced chattering suppression ability is proposed. Compared with the conventional methods, the chattering problem is well alleviated and thus the speed estimation ripple is much reduced. Finally, the effectiveness of the proposed method, even with the mismatched parameters adopted is validated at 100 kr/min with the sampling frequency 20 kHz.

Attitude Estimation Method of High-Spinning Body Through Onboard Sensor Fusion Under Uniaxial Gyroscope Saturation Environment

This article deals with a 3-D attitude estimation problem of a dynamic object that involves a uniaxial, high-speed rotational motion. As a high spinning dynamics typically causes an angular rate saturation in most gyros, it is also challenging to determine the object's attitude of the principal rotation axis and correspondingly the attitude of other orthogonal axes. To solve this problem, this article proposes two techniques to accomplish the attitude estimation under uniaxial gyro saturation condition. First, this article develops a new integrated filter scheme based on a reduced state model and attitude kinematics. The proposed estimation algorithm takes advantage of the magnetic measurement and gyro's partial rates to obtain a posteriori attitude estimate in the orthogonal axes and further develops to derive the principal axis attitude deterministically. Despite decent estimation performance, a measurement degeneracy may be caused in the reduced state model theoretically. To suppress the degeneracy effect, this study also exploits an augmented attitude estimation method, which adopts a post-processing computational rate in the principal axis through a time-frequency analysis on magnetic measurements. Finally, the proposed technique's performance was demonstrated through both simulation and experiment with the high spinning rate of 10846 and 2880°/s, respectively, where the error level was measured at the sub-degree in the orthogonal axes and slightly higher in the principal axis.

Vehicle platform attitude estimation method based on adaptive Kalman filter and sliding window least squares

Precision instrument measurement on an unstable platform is a difficult engineering problem, and the commonly used method is to compensate the instrument measurement results through platform attitude estimation. This paper derives a three-dimensional attitude estimation method based on inertial sensors including gyroscope and inclinometer. In order to deal with the inertial sensor noise, low-pass filter, Kalman filter, adaptive Kalman filter (AKF) and sliding window least squares (SWLS) are chosen to test the filtering performance from the frequency domain perspective. Practical filtering experiments indicate that AKF achieves the best filtering performance for gyroscope, while SWLS has the best filtering performance for inclinometer. Using AKF and SWLS to deal with inertial sensor outputs, the attitude estimation of the vehicle platform is realized. The proposed method is verified on vehicle-mounted electro-optical measurement equipment.

Pose estimation of a non-cooperative spacecraft without the detection and recognition of point cloud features

This paper presents a relative position and attitude estimation method using consecutive point clouds without feature extraction. Using this method, the inaccurate state estimation problems for non-cooperative targets caused by the mismatched point pairs or the low tracking accuracy of point cloud features can be resolved. First, point cloud registration is carried out by the transformation of the covariance matrices of the point cloud between two adjacent frames. Meanwhile, the random sample consensus algorithm is employed to reject the mismatched point pairs. Then, pose-graph optimization is adopted to eliminate the accumulated errors of consecutive point cloud registration. Finally, an Extended Kalman Filter is designed to estimate the position, velocity, and angular velocity of the target. The experimental results show that the covariance matrix transform algorithm can achieve the point cloud registration for close roto-translational motions, and the target motion state can be estimated effectively and continuously.

Attitude estimation method based on extended Kalman filter algorithm with 22 dimensional state vector for low-cost agricultural UAV

Attitude estimation method based on extended Kalman filter algorithm with 22 dimensional state vector for low-cost agricultural UAV

Implementation of a novel and versatile initial rotor position estimation method on surface-mounted permanent magnet synchronous motor prototype at zero speed

This paper presents a novel initial rotor position estimation method for a surface-mounted permanent magnet synchronous motor (SPMSM) using its inherent magnetic features. Here, the initial rotor position has been estimated utilising the variation in phase inductances arising out of the unavoidable but very small saliency occurring either out of magnetic saturation of the machine or the magnet shape. Detailed investigations have been carried out to enumerate the actual magnitude of the inductances of the SPMSM with distributed armature winding and the dynamic saturation status with armature current variation. Here, 3-phase balanced voltage of higher frequency (150 Hz) is applied to the motor terminals for a short period of time (for 300 ms) and the corresponding phase currents are indirectly used to determine the rotor position. Innovative signal processing steps have been used to distinguish small differences in the 3-phase currents caused by the small differences in the phase inductances. The position is determined from the relative values of different phase currents using a novel approach. This still leads to two diametrically opposite (electrical phase) solutions for the instantaneous rotor position. To eliminate this ambiguity, two alternative methods (for pole identification) have been proposed. Extensive co-simulations of finite-element method (FEM)-based electromagnetic simulation and system simulation (for logic implementation) have been conducted. The estimation methods have been validated experimentally on a laboratory-developed prototype SPMSM that was designed and fabricated by the authors. The experimental results are found to be in excellent agreement with the FEM-based simulation results. The complete initial position estimation method takes less than 1 s of real time, which is typically less than the pre-charging time of standard commercial inverters.

Dual-Band Infrared and Geomagnetic Fusion Attitude Estimation Algorithm Based on IMMEKF

In order to further improve the performance of dual-band infrared and magnetic fusion attitude measurement method, an interactive multimodel extended Kalman filter (IMMEKF) attitude fusion algorithm is proposed to calculate the rotating missile's full attitude angle by using the data collected from dual-band two-axis infrared sensors and three-axis geomagnetic sensor. Four filtering models are used to solve the problem of inaccuracy in the single-model filtering process. The interactive multimodel and extended Kalman filter attitude estimation algorithms are combined by the probability density function and are used to update the model probability in real time. Each model's probability weights are modified to achieve the optimal attitude angle estimation according to the environment. The semiphysical experimental results show that the IMMEKF algorithm has higher accuracy and reliability compared with other latest state-of-the-art fusion estimation algorithms under normal infrared and geomagnetic conditions. In the case of infrared or geomagnetic anomalies, only the IMMEKF algorithm can avoid using the affected sensor data and calculate an accurate value of attitude angle. The reliability of the dual-band infrared and geomagnetic combined attitude estimation method is improved, which provides an effective technical implementation for the rotating missile attitude measurement.

Autonomous image-based navigation using vector code correlation algorithm for distant small body exploration

This paper proposes an autonomous image-based navigation method for estimating the target-relative position of a spacecraft for distant small body exploration. The main focus is position estimation at high altitude where the outlines of a target body can be seen in images. The asteroid explorer Hayabusa2 touched down on the asteroid Ryugu with pin-point accuracy in February 2019. For this mission, the asteroid-relative position was estimated by ground operators from 20 km to 50 m above the surface of Ryugu. For the exploration of small bodies farther than the asteroid main belt, the delay of communication with Earth is unacceptably large for feedback guidance. This situation becomes worse for larger bodies because the time constant of the dynamics becomes smaller. Therefore, real-time autonomous navigation is required for distant small body exploration even at high altitude. To accomplish high-accuracy and real-time autonomous navigation, an autonomous position estimation method based on terrain-relative navigation (TRN) that estimates deviation by comparing nominal terrain information and actual terrain information is proposed. In addition to TRN, the vector code correlation (VCC) algorithm is used for the luminance comparison of terrain information. This algorithm is a type of correlation calculation method for template matching that finds the maximum correlated region in images. With the VCC algorithm, correlation can be calculated in real time via XOR operations suitable for FPGA. The estimation accuracy and processing time of the proposed method were evaluated with a comparison to those of other methods. The results show that a high estimation accuracy, similar to the image resolution, was accomplished in real time. Finally, an evaluation using flight data from Hayabusa2 shows that the estimation accuracy and processing time of the proposed method are suitable for a real mission environment. The proposed method will be a key technology for distant small body exploration.

Efficiency Optimization Strategy for Switched Reluctance Generator System With Position Sensorless Control

A novel efficiency optimization strategy combined with a position estimation method is proposed for switched reluctance generators (SRGs) in this article. This practical performance improvement strategy can expand the application of SRGs small-scale wind energy generation system, which is a good solution for mitigating the fossil energy crisis. First, the position estimation method based on current injection of the idle phase is presented. The angle error caused by sampling in this method aggravates with speed. Accordingly, an error compensation strategy in the linear range of inductance is suggested. Second, a method for improving the efficiency of SRG system current is proposed. It determines the range of turn- off angle with the minimum root-mean-square value of phase current, and further obtains the optimal turn- on angle by the fitting expression to improve the efficiency. This efficiency optimization strategy combined with the new position estimation method can greatly enhance the efficiency and reliability of SRG system and eliminate the position sensor. There are many simulation and experiment results that validate the effectiveness of the proposed schemes.

IMU-Based Automated Vehicle Body Sideslip Angle and Attitude Estimation Aided by GNSS Using Parallel Adaptive Kalman Filters

The sideslip angle and attitude are crucial for automated driving especially for chassis integrated control and environmental perception. In this article an inertial measurement unit (IMU)-based automated vehicle body sideslip angle and attitude estimation method aided by low-sample-rate global navigation satellite system (GNSS) velocity and position measurements using parallel adaptive Kalman filters is proposed. This method can estimate the sideslip angle and attitude simultaneously and is robust against the vehicle parameters and road friction even as the vehicle enters critical maneuvers. First, based on the acceleration and angular rate from the six-dimensional inertial measurement unit, the attitude, velocity and position (AVP) are integrated with the navigation coordinates and the AVP error dynamics and observation equations of the integration results are developed. Second, parallel innovation adaptive estimation (IAE)-based Kalman filters is designed to estimate the AVP error of the integration method to address the issues of the GNSS low sampled rate and abnormal measurements. Then the AVP error is forwarded to the AVP integration to compensate the accumulated error. To improve the heading angle estimation accuracy, the heading error is estimated by a decoupled IAE-based Kalman filter aided by GNSS heading. In addition, time synchronization of the IMU and GNSS is realized through hardware based on the pulse per second signal of the GNSS receiver and the spatial synchronization is achieved by a direct compensation method. Lastly, the sideslip angle and attitude estimation method is validated by a comprehensive experimental test including critical double lane change and slalom maneuvers. The results show that the estimation error of the longitudinal velocity and lateral velocity is smaller than 0.1 m/s $({1\sigma })$ , and the estimation error of the sideslip angle is smaller than 0.15° $({1\sigma })$ .