Accurate Position Estimation Research Articles

Intention inference‐based interacting multiple model estimator in photoelectric tracking

AbstractAiming to improve the estimation and prediction accuracy of a target's position, this paper proposes a state estimation method for photoelectric tracking systems, based on the evaluation of the tracked target's motion intention. Traditional photoelectric tracking systems utilize external physical quantities such as the position, velocity, and acceleration of the target as the estimated states. While this method can output good results for pre‐modelled target positions, it struggles to maintain the accuracy when facing manoeuvering targets or complex motion patterns targets. Here, the relevant parameters of the tracked target's motion intention are directly estimated innovatively, like estimating the circling point position rather than the circular flying target's position and velocity. This approach enables recognizing the target's motion intention and leads to precise estimation, which specifically consists of an interacting multiple model approach, multiple unscented Kalman estimators, and a robust estimator. The effectiveness and stability of this estimator are validated through software simulations and experiments on a dual‐reflection mirror platform.

Saliency-Based Sensorless Control Using Current Derivative in IPMSM Drives With Single DC-Link Current Sensor

In vector-controlled drive systems, a position sensorless drive with a single current sensor (SD-SCS) is an attractive system due to the minimum number of sen- sors. However, when the saliency-based SD-SCS is applied, the position estimation accuracy is deteriorated due to the errors in the injected pulse width modulated voltage and the reconstructed current. This paper proposes a current derivative-based sensorless control using a tri-active vector pulse width modulation (TAVPWM) to enhance the saliency-based SD-SCS. First, the TAVPWM scheme, which eliminates the current reconstruction dead zone in the low-modulation region, is adopted to improve the low- speed drive. Next, the derivatives of the phase currents are utilized and analyzed to minimize the position estimation error in the overall operating regions. Lastly, the transition strategy with respect to the speed is proposed for the full-speed sensorless drive. Owing to the proposed method, the switching frequency increases up to 16 kHz and the audible noise is virtually eliminated in the saliency-based SD-SCS. Furthermore, the enhanced dynamic performance is verified by various experimental results.

Positioning algorithm based on space constraint of the PD array in VLP system

Due to factors such as indoor multipath effects and noise, the positioning signal becomes unstable, posing a challenge for visible light positioning systems relying on a single photodetector (PD) to achieve accurate position estimation. To enhance localization precision and ranging accuracy, a positioning algorithm based on spatial constraints of the PD array is introduced in this paper. By leveraging spatial information from the PD array and the channel state information (CSI) from multiple PDs, cross-matching among multiple PDs is performed to alleviate the limitation of unstable positioning information of a single PD during the positioning process. Moreover, the distances obtained through cross-matching are fused with those based on CSI, and the measured distance is iteratively updated to reduce the interference of reflection paths on ranging. This algorithm utilizes the spatial arrangement of the PD array to constrain positioning information received by multiple PDs, rather than relying on a single PD for positioning. This method can achieve joint optimization of positioning accuracy and ranging accuracy without requiring additional equipment or modifying the transceiver structure. Simulation results within a 4 m × 4 m × 3 m indoor space showcase the proposed method's effectiveness. The average positioning error has been reduced to 1.28 cm, and the root mean square error has been reduced to 1.51 cm. In comparison to the least squares (LS) algorithm based on CSI and the nonlinear LS algorithm based on CSI, the new approach reduces average localization errors by approximately 50.4% and 41.3%, respectively. Additionally, the algorithm attains a ranging accuracy of 3.86 cm, outperforming CSI-based distance estimation accuracy by 41.9%.

Accurate drone corner position estimation in complex backgrounds with boundary classification

This study develops an efficient approach for precise channel frame detection in complex backgrounds, addressing the critical need for accurate drone navigation. Leveraging YOLACT and group regression, our method outperforms conventional techniques that rely solely on color information. We conducted extensive experiments involving channel frames placed at various angles and within intricate backgrounds, training the algorithm to effectively recognize them. The process involves initial edge image detection, noise reduction through binarization and erosion, segmentation of channel frame line segments using the Hough Transform algorithm, and subsequent classification via the K-means algorithm. Ultimately, we obtain the regression line segment through linear regression, enabling precise positioning by identifying intersection points. Experimental validations validate the robustness of our approach across diverse angles and challenging backgrounds, making significant advancements in UAV applications.

Autonomous navigation method of satellite constellation based on adaptive forgetting factors

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation, an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors (UKF-AFF) is proposed. The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors. The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors, equipping this method with more adaptability. The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues. Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations, the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively. Stability analysis of the proposed method is also conducted. The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments. The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30% and 44% respectively compared to those of the extended Kalman filter, and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

Position Estimation Method for Unmanned Tracked Vehicles Based on a Steering Dynamics Model

A position estimation method for unmanned tracked vehicles based on a steering dynamics model was developed during this study. This method can be used to estimate the position of a tracked vehicle in real time without relying on a high-precision positioning system. First, the relationship between the shear displacement of the track relative to the ground and the speed and yaw rate of the tracked vehicle during the steering process was analyzed. Next, the steering force of the tracked vehicle was calculated by using the shear force–displacement theory, and a steering dynamics model considering the acceleration of the vehicle was established. The experimental results show that this steering dynamics model produced more accurate position estimations for an unmanned tracked vehicle than did the kinematics model. This method can serve as a reference for the positioning of unmanned tracked vehicles working in special environments that cannot use precise positioning systems.

Sensorless Control of Surfaced-Mounted Permanent Magnet Synchronous Motor in a Wide-Speed Range

This paper delves into a comprehensive study of a wide-speed-range sensorless control approach for surface-mounted permanent magnet synchronous motors (SPMSMs). In the low-speed range, a novel high-frequency pulse voltage injection (HFPVI) method is introduced for rotor position estimation, which does not depend on motor saliency and is well-suited for SPMSMs. This method incorporates a second-order generalized integrator (SOGI) and a new modulation signal to enhance the accuracy of rotor position estimation. For medium-to-high speeds, an improved super-twisting sliding mode observer (STSMO) utilizing a continuous hyperbolic tangent function is proposed to mitigate chattering. Additionally, a new phase-locked loop (NPLL) is introduced to accurately obtain the rotor position. Furthermore, this paper designs an exponential weighted switching function to facilitate a smooth transition of the motor from the low-speed domain to the medium- and high-speed domains. The effectiveness and superiority of the proposed methods are validated through simulations and experiments conducted on an RTU-BOX platform. The rotor position estimation errors of the proposed new HFPVI method and the improved STSMO method under various operating conditions are both approximately 0.05 rad (2.8 elc·deg), and the SPMSM can switch smoothly from the low-speed range to the medium- and high-speed ranges.

A highly integrated shape memory alloy actuator and precision self-sensing tracking control based on position compensation

Shape memory alloy (SMA) actuators are widely used in various actuators due to their advantages of simplicity, high strength, and flexibility. The mapping relationship between the resistance change and displacement during the phase transition process of SMA enables its self-sensing control without external displacement sensors. However, the limited accuracy and high complexity of existing self-sensing models are two crucial factors restricting the wide application of self-sensing control. Due to the high nonlinearity of the SMA wire’s phase transition process, it is challenging to achieve accurate position estimation using a simple self-sensing model. Thus, this paper proposes a novel approach by utilizing the simplest linear model for position estimation. The inevitable modeling errors are directly compensated in the final control results, rather than compensating for estimation errors. This significantly reduces the implementation difficulty of SMA self-sensing control and effectively improves control accuracy. Furthermore, a highly integrated prototype of SMA actuator is designed in this paper, where the additional position sensor and self-resetting loading device are integrated, greatly reducing the size of the SMA actuator. A series of self-sensing tracking experiments are conducted on this prototype, validating that the compensated self-sensing control can achieve comparable performance to the control method with external sensors.

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios.

Multi-sensor Attitude Estimation using Quaternion Constrained GNSS Ambiguity Resolution and Dynamics-Based Observation Synchronization

Recently, high accuracy and low-cost navigation hardware is becoming increasingly available that can be efficiently used for the control of autonomous vehicles. We present a sensor fusion method providing tightly coupled integration of pseudorange, carrier phase, and Doppler satellite measurements taken at multiple vehicle-mounted GNSS antennas with onboard inertial sensor observations. The key of accurate GNSS position and orientation estimation is the successful integer ambiguity resolution. We propose a method that uses the quaternion states as constraints to improve ambiguity resolution and to increase the accuracy of the GNSS based attitude determination. Generally, the low-cost hardware neither allows a hardware-level time synchronization between the GNSS receivers due to a lack of a common external oscillator nor provides the clock steering function available in geodetic GNSS receivers. The lack of observation synchronization causes several degrees of error in attitude estimation. To eliminate this effect, a dynamics-based solution is presented that synchronizes the observations by taking the dynamics of the moving platform into account. Compared to common external oscillator based sensor setups, our solution allows to increase both the number of rover receivers on the platform and the baselines between them easily, thus it opens up new possibilities in the attitude determination of large vehicles. We validate our approach against a tactical grade inertial navigation system. The results show that our approach using low-cost sensors provides the ambiguity success rate of 100% for the moving baselines, and the positioning and attitude error reached the centimeter and half a degree level, respectively.

Development of a Double-Resampling-Based Least-Squares Particle Filter for Accurate Position Estimation of a GPS Receiver in Visakhapatnam Region of the Indian Subcontinent

Development of a Double-Resampling-Based Least-Squares Particle Filter for Accurate Position Estimation of a GPS Receiver in Visakhapatnam Region of the Indian Subcontinent

A High-Precision Position Estimation Method for Low Saliency Ratio Machine Based on Convolutional Neural Networks

Sensorless control of surface permanent magnet synchronous motor (SPMSM) faces great challenges in achieving high precision of rotor position estimation at low speed because the saliency of an SPMSM is only a minor secondary effect. In order to extract accurate position information for SPMSM with low and ultra-low saliency ratios, this paper presents a method that implements image recognition techniques with high-frequency (HF) rotating voltage vector injection. For building an image of the induced current vector, the information of the negative-sequence and the second harmonic of positive-sequence HF current signals in the stationary reference frame is extracted. Then, convolutional neural networks (CNN) are applied to the current vector images to establish the relationship between the vector image and the rotor position. Subsequently, the rotor position can be obtained from the result of image recognition from the neural network. Benefiting from CNN’s excellent performance in image classification, the proposed method is able to recognize subtle position signatures even under low saliency. This confirms the opportunity to increase position estimation accuracy for sensorless control of SPMSM. Finally, the experimental results have provided proof that the proposed method has significantly enhanced estimation accuracy.

A Rotor Position Detection Method for Permanent Magnet Synchronous Motors Based on Variable Gain Discrete Sliding Mode Observer

The purpose of this paper is to study the sensor-less rotor position estimation method for permanent magnet synchronous motors, and to achieve accurate estimation of rotor position in different conditions. Firstly, the traditional super-twisting observer algorithm is analyzed, and a new discrete variable gain sliding mode observer is designed to solve the buffeting problem in discrete systems, taking the reaction force as the disturbance signal. By estimating the back potential of the observer, the buffeting problem in the sliding mode algorithm can be effectively improved as shown by the simulation results. Then, to solve the problem of phase delay in rotor position estimation, an adaptive orthogonal phase-locked loop method is used to compensate the estimation error caused by the change in motor speed and increase the estimation accuracy of rotor position. The stability of the method can be proven by Lyapunov’s second method. Simulation experiments verify the accuracy of the proposed PMSM rotor position estimation method.

An Integration visual navigation algorithm for urban air mobility

This paper presents an integration visual navigation algorithm called PnP-ORBSLAM for UAV position estimation in Urban Air Mobility (UAM). ORBSLAM is a popular and benchmark algorithm for vision based navigation applications. The proposed method improve the performance of ORBSLAM by adding a post-processing marker recognition phase to the model. Based on the features extracted from the markers, PnP algorithm is introduced to estimate the position of the monocular camera. The position estimation accuracy of the UAV is supposed to be improved by adding the position information of the camera to the model. Experiment is carried out based on Airsim simulation platform. Results show that the PnP-ORBSLAM algorithm can improve the three-dimensional accuracy by a margin of 5.38 % compared with ORBSLAM. In addition, the process speed of the proposed method can reach about 28 frames per second. It means that the PnP-ORBSLAM algorithm can work in real-time.

Mobile Data Gathering and Preliminary Analysis for the Functional Reach Test.

The functional reach test (FRT) is a clinical tool used to evaluate dynamic balance and fall risk in older adults and those with certain neurological diseases. It provides crucial information for developing rehabilitation programs to improve balance and reduce fall risk. This paper aims to describe a new tool to gather and analyze the data from inertial sensors to allow automation and increased reliability in the future by removing practitioner bias and facilitating the FRT procedure. A new tool for gathering and analyzing data from inertial sensors has been developed to remove practitioner bias and streamline the FRT procedure. The study involved 54 senior citizens using smartphones with sensors to execute FRT. The methods included using a mobile app to gather data, using sensor-fusion algorithms like the Madgwick algorithm to estimate orientation, and attempting to estimate location by twice integrating accelerometer data. However, accurate position estimation was difficult, highlighting the need for more research and development. The study highlights the benefits and drawbacks of automated balance assessment testing with mobile device sensors, highlighting the potential of technology to enhance conventional health evaluations.

A self-calibration method for temperature errors of non-orthogonal angles between gimbals in rotational inertial navigation system

The rotational inertial navigation system (RINS) employs a rotating modulation technique by introducing rotation gimbals, thereby enhancing accuracy of the RINS in position and velocity estimation. Furthermore, the rotation gimbals provide the self-calibration ability in the RINS. However, it introduces new sources of error, such as non-orthogonal angles between gimbals, which result in inaccuracies in attitude outputs and thus require precise calibration and compensation. Previous research has largely overlooked the influence of temperature on non-orthogonal angles and the calibration accuracy are susceptible to various factors. In this paper, we discuss the causes of temperature-induced errors in non-orthogonal angles and their impact on attitude estimation, and a self-calibration method based on outputs from fiber optic gyroscopes and optical angle encoders is proposed, showing good repeatability by using periodic averaging. The rapid rotation scheme adopted is suitable for calibrating temperature errors during system operation and a temperature-stabilized chamber is not required. The results of navigation experiments validate the effectiveness of the calibration and compensation method. After compensating the temperature errors of non-orthogonal angles, we can see a decrease in the amplitude of attitude output oscillation from 80″ to less than 10″.

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.

An Inertia-Based Upper Limb Motion Capture Model for Accurate Position Estimation in Magnetic Field Disturbances

An Inertia-Based Upper Limb Motion Capture Model for Accurate Position Estimation in Magnetic Field Disturbances

Enhancing Satellite Image Sequences through Multi-Scale Optical Flow-Intermediate Feature Joint Network

Satellite time-series data contain information in three dimensions—spatial, spectral, and temporal—and are widely used for monitoring, simulating, and evaluating Earth activities. However, some time-phase images in the satellite time series data are missing due to satellite sensor malfunction or adverse atmospheric conditions, which prevents the effective use of the data. Therefore, we need to complement the satellite time series data with sequence image interpolation. Linear interpolation methods and deep learning methods that have been applied to sequence image interpolation lead to large errors between the interpolation results and the real images due to the lack of accurate estimation of pixel positions and the capture of changes in objects. Inspired by video frame interpolation, we combine optical flow estimation and deep learning and propose a method named Multi-Scale Optical Flow-Intermediate Feature Joint Network. This method learns pixel occlusion and detailed compensation information for each channel and jointly refines optical flow and intermediate features at different scales through an end-to-end network together. In addition, we set a spectral loss function to optimize the network’s learning of the spectral features of satellite images. We have created a time-series dataset using Landsat-8 satellite data and Sentinel-2 satellite data and then conducted experiments on this dataset. Through visual and quantitative evaluation of the experimental results, we discovered that the interpolation results of our method retain better spectral and spatial consistency with the real images, and that the results of our method on the test dataset have a 7.54% lower Root Mean Square Error than other approaches.

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Sensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved in the parameter identification process in a sensorless control system, an online inductance identification method independent of the rotor position information is developed in this paper. The proposed method utilizes the recursive least square algorithm and the particle swarm optimization algorithm to realize real-time identification of the inductance along the direct axis and the quadrature axis, respectively, based on the deduced parametric equations without position information. The proposed method is efficient enough to be implemented within 0.2 ms and does not introduce any additional signal injection. A test bench is built to validate the characteristics of the method, and the experimental results show that the identified inductance can converge to the actual value rapidly and is robust to changes in the initial values and stator current. With the proposed method, accurate estimation of the rotor position and speed can be obtained using traditional model-based position estimators, and the stability of the sensorless control system can be improved significantly.