Position Estimation Algorithm Research Articles

A Multi-sensor Data Fusion Algorithm Based on Unscented Kalman Filter for the Attitude Estimation of UAV

Aiming at the low accuracy of the inertial measurement unit and the error of the traditional attitude estimation algorithm, a UAV attitude estimation algorithm based on Unscented Kalman Filter (UKF) is proposed. The Euler angle method is used to describe the attitude algorithm model of the aircraft, and on this basis, the system state equation and observation equation of the UAV are established; the unscented Kalman filter algorithm is used to achieve the calculation of the attitude angle of the aircraft. By using APM flight control data, the simulation experiment is compared with the traditional attitude estimation algorithm. The experimental results show that the proposed algorithm has a great improvement in reliability and accuracy compared with the attitude estimation algorithm using extended Kalman filter (EKF).

Vision-based Position estimation and Indoor scene recognition algorithm for Quadrotor Navigation

In this paper, an effective and simple Grid based vanishing point detection position estimation algorithm and Enhanced GIST descriptors based indoor scene recognition algorithm for navigation of MAV in indoor corridor environment is described. Two different classifiers, k-nearest neighbour classifier and support vector machine is employed for the categorization of indoor scenes into corridor, staircase or room. Indoor scene classification was performed on Dartaset-1. In the training phase of the indoor scene recognition algorithm, GIST, HODMG and Enhanced-GIST feature vectors are extracted for all the indoor training images in the Dataset-1 and indoor scene classifiers are trained for the extracted image feature vectors and assigned image labels of the indoor scenes (corridor-1, staircase-2 and room-3). In the testing phase of the indoor scene recognition algorithm, for each unknown test image frame GIST, HODMG and Enhanced-GIST feature vectors are extracted and the indoor scene classification is performed using a trained scene recognition model. The proposed indoor scene recognition algorithm using SVM with Enhanced GIST descriptors produced high recognition rates of 99.33% compared to the KNN classifiers. After recognizing the indoor scene as corridor, the MAV has to estimate its position based on the detection of vanishing point in the indoor corridor image frames. Experimental results show that the proposed method is suitable for real time operations.

Robust attitude estimation of rotating space debris based on virtual observations of neural network

SummaryHigh precise estimation and prediction of the target's attitude motion are key technologies for capturing and removing rotating space debris. In this article, a neural‐network‐enhanced Kalman filter (NNEKF) is proposed to improve the precision and robustness of attitude estimation algorithm. The main innovation of the NNEKF is to utilize virtual observations of the inertia characteristics to improve the filter's performances. The virtual observations are obtained using a neural network, which is offline trained using simulation data. In order to decrease the number of nodes of the network, the input data are preprocessed using the discrete Fourier transformation method. Moreover, by involving the characteristic frequencies in the input vector, the neural network can extract information from all the past observations, so as to grasp long‐term characteristics of the dynamical system. Therefore, the NNEKF can provide more precise estimation of the target's moment of inertia, and furthermore improve the accuracy and robustness of attitude estimation and prediction. Simulation results indicate that the NNEKF can reduce the estimation errors by 39% compared with the conventional EKF method when using the same measurement data. And the accumulation errors of prediction using estimates of the NNEKF is just as 24% as the conventional EKF.

Replacement of In-Orbit Modern Spacecraft Attitude Determination and Estimation Algorithms with Neural Networks

In-orbit spacecraft attitude determination or estimation algorithms are considered to be of utmost importance and criticality. In the current research article, several In-Orbit attitude determinations and estimation algorithms are reviewed. These algorithms are TRIAD, Q-method, and Extended Kalman Filter (EKF). Three analogous algorithms are developed based on Cascade-Forward Neural Networks (CFNN) in order to replace these traditional in-orbit estimation and determination algorithms. In order to prove the concept, the former Egyptian spacecraft EGYPTSAT-1 is utilized as a test case. The objective of the developed CFNNs is to mimic the performance of the presented attitude determination and estimation algorithms. The performance of the proposed CFNNs is evaluated. The proposed CFNN algorithms have the same accuracy level of TRIAD, Q-method, and EKF. The maximum error achieved has been ±0.1° with an average execution time about half of the average execution time of EKF. The proposed CFNN are proven to be generic and could replace any determination or estimation algorithm within the same accuracy levels. The computational load of CFNN is independent of the complexity level of the attitude determination or estimation algorithm that it tries to mimic. This enables the usage of more elaborated models to increase the accuracy within the same computational load.

Comparative Analysis of Low Earth Orbit Satellite Attitude Determination using Sensor Integration and Attitude Estimation using Kalman Filter

The objective of the research is to undergo a performance comparison in terms of accuracy, convergence time, amount of memory, etc. between the satellite attitude determination and attitude estimation using non-linear filters. The fundamental approach towards it is to design an OBC (On Board Computer) that would help in achieving a controlled output for the chosen plant (MicroMAS1 satellite). The attitude determination algorithm is implemented through TRIAD algorithm, which takes sensor readings of body frame and inertial frame of reference. Then it is used to determine the rotation matrix (DCM) by converting the matrix form into vector form and again back to matrix form to determine the 3x3 matrix, which includes all the Euler angle equations to determine the pitch, roll, yaw characteristics of the system. The attitude estimation algorithms involves the use of nonlinear filters which provide an added advantage that energy can be transferred in a designed manner and extra degrees of freedom are available in filter design. The Unscented Kalman Filter (UKF) is preferred as it addresses the problem using a deterministic sampling approach. Moreover, the non-linear filters are used to remove the noise error and disturbance caused by engine. The design of satellite attitude models involves more of a mathematical approach that would be dealt with MATLAB and SIMULINK operations.

Vision-based Software Tool System for Position Estimation Using a Smartphone

Context: Current smartphone models have a very interesting set of sensors such as cameras, IMUs, GPS, and environmental variables. This combination of sensors motivates the use of smartphones in scientific and service applications. One of these applications is precision agriculture, specifically drone position estimation using computer vision in GPS-denied environments for remote crop measurements. Method: This work presents the development of EVP, a vision-based position estimation system using a modern smartphone and computer vision methods. EVP consists of two software applications: an Android app (mobile station) running on a smartphone capable of controlling the drone’s flight, acquiring images, and transmitting them through a wireless network; and another application (base station) running on a Linux-based computer capable of receiving the images, processing them and running the position estimation algorithms using the acquired images. In this work, the mobile station is placed in a quadcopter. Using EVP, users can configure the mobile and base station software, execute the vision-based position estimation method, observe position graph results on the base station, and store sensor data in a database. Results: EVP was tested in three field tests: an indoor environment, an open field flight, and a field test over the Engineering Department’s square at Universidad del Valle. The root mean square errors obtained in XY were 0,166 m, 2,8 m, and 1,4 m, respectively, and they were compared against the GPS-RTK measurements. Conclusions: As a result, a vision-based position estimation system called EVP was developed and tested in realworld experiments. This system can be used in GPS-denied environments to perform tasks such as 3D mapping, pick-up and delivery of goods, object tracking, among others.

Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation.

This paper presents a Kalman filter-based attitude estimation algorithm using a single body-mounted inertial sensor consisting of a triaxial accelerometer and triaxial gyroscope. The proposed algorithm has been developed for attitude estimation during dynamic conditions such as walking and running. Based on the repetitive properties of the velocity signal of human gait during walking, a novel velocity-aided observation is used as a measurement update for the filter. The performance has been evaluated in comparison to two standard Kalman filters with different measurement update methods and a smoother algorithm which is formulated in the form of a quadratic optimization problem. Whereas two standard Kalman filters give maximum 5 degrees in both pitch and roll error for short walking case, their performance gradually decrease with longer walking distance. The proposed algorithm shows the error of about 3 degrees in 15 m walking case, and indicate the robustness of the method with the same performance in 75 m trials. As far as the accuracy of the estimation is concerned, the proposed method achieves advantageous results due to its periodic error correction capability in both short and long walking cases at varying speeds. In addition, in terms of practicality and stability, with simple parameter settings and without the need of all-time data, the algorithm can achieve smoothing-algorithm-performance level with lower computational resources.

Reliable attitude estimation algorithm considering atypical observation

Reliable attitude estimation algorithm considering atypical observation

UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Three-dimensional attitude estimation for unmanned aerial vehicles is usually based on the combination of magnetometer, accelerometer, and gyroscope (MARG). But MARG sensor can be easily affected by various disturbances, for example, vibration, external magnetic interference, and gyro drift. Optical flow sensor has the ability to extract motion information from image sequence, and thus, it is potential to augment three-dimensional attitude estimation for unmanned aerial vehicles. But the major problem is that the optical flow can be caused by both translational and rotational movements, which are difficult to be distinguished from each other. To solve the above problems, this article uses a gated recurrent unit neural network to implement data fusion for MARG and optical flow sensors, so as to enhance the accuracy of three-dimensional attitude estimation for unmanned aerial vehicles. The proposed algorithm can effectively make use of the attitude information contained in the optical flow measurements and can also achieve multi-sensor fusion for attitude estimation without explicit mathematical model. Compared with the commonly used extended Kalman filter algorithm for attitude estimation, the proposed algorithm shows higher accuracy in the flight test of quad-rotor unmanned aerial vehicles.

Motor Speed Control With Convex Optimization-Based Position Estimation in the Current Loop

This article presents a sensorless machine drive scheme, combining the motion model predictive control (MPC) in the speed loop and the position estimation in the current loop. The current-loop-based position estimation algorithm is to estimate the present rotor position with the phase current information of the previous samples. These methods cannot perform perfectly in the low speed when a heavy load suddenly occurs, as the aggressive motion disturbance can impact the convergence of the low-speed position estimation, or even make divergence and estimation failure. This issue is especially terrible for the surface permanent magnet synchronous machines (SPMSMs), as the magnetic saliency is weak at heavy loads. The target of this article is to solve this issue with respect to the servo system, where the machines need to frequently start and stop with heavy loads, and hence the above risk must be considered. The convex optimization is applied in the position estimation, and the optimization cost value is extended to adjust the injection amplitude and the reference trajectory of the MPC control. With this strategy, the sensorless control can operate with a high stability and accuracy. The scheme is validated in an SPMSM test bench.

An Open-Source Test Environment for Effective Development of MARG-Based Algorithms.

This paper presents an open-source environment for development, tuning, and performance evaluation of magnetic, angular rate, and gravity-based (MARG-based) filters, such as pose estimators and classification algorithms. The environment is available in both ROS/Gazebo and MATLAB/Simulink, and it contains a six-degrees of freedom (6 DOF) test bench, which simultaneously moves and rotates an MARG unit in the three-dimensional (3D) space. As the quality of MARG-based estimation becomes crucial in dynamic situations, the proposed test platform intends to simulate different accelerating and vibrating circumstances, along with realistic magnetic perturbation events. Moreover, the simultaneous acquisition of both the real pose states (ground truth) and raw sensor data is supported during these simulated system behaviors. As a result, the test environment executes the desired mixture of static and dynamic system conditions, and the provided database fosters the effective analysis of sensor fusion algorithms. The paper systematically describes the structure of the proposed test platform, from mechanical properties, over mathematical modeling and joint controller synthesis, to implementation results. Additionally, a case study is presented of the tuning of popular attitude estimation algorithms to highlight the advantages of the developed open-source environment.

Vehicle localisation by altitude data matching in spatial domain and its fusion with dead reckoning

In this paper, localisation algorithm based on altitude profiles is proposed. It is a kind of landmark-based localisation algorithm in which altitude profile which is obtained while driving a vehicle are treated as landmarks. The author proposes an algorithm for measuring the location of vehicles by using altitude data in locations where GNSS positioning is difficult. Specifically, altitude is estimated from the pressure information obtained from a barometer, in other words, pressure sensor. This is a position estimation algorithm that attempts to identify the current position by matching altitude reference data prepared in advance with altitude data currently acquired. A sensor fusion with wheel pulse speed data is also proposed as an extension to the original altitude similarity matching algorithm to make it more applicable to the cases when variation in altitude is little. The effectiveness of the algorithm is presented with several experimental results obtained with real data.

A Vector Controlled Drive System for Electrically Power Assisted Steering Using Hall-Effect Sensors

Permanent Magnet Synchronous Motors (PMSMs) have been replacing conventional DC motors in numerous automotive applications. One of which is Electrically Power Assisted Steering Systems (EPAS). PMSMs offer better performance and a longer lifetime while slightly increasing the system’s cost and complexity. In a vector-controlled PMSM drive system, the rotor position sensor’s resolution plays a vital role in the overall system performance. The better the resolution, the higher the cost. Although numerous sensor-less control algorithms currently exist, machine startup and operation with dynamically changing set-points and loads still impose a challenge for such an approach. For this reason, inexpensive Hall-Effect sensors have been recently used along with a proper position estimation algorithm to provide high-resolution rotor position. In this paper, an implementation of a vector-controlled drive system is applied to an EPAS using only low-resolution Hall-Effect sensors. Additionally, an improved rotor position estimation algorithm based on speed integration is developed to decrease estimation errors and torque ripples in the case of direction reversal. This is to cater to the dynamically changing commands encountered during the normal power assist operation. Comparisons of the proposed rotor position estimation system with the conventional technique are introduced. A complete representation of the proposed system is built using MATLAB/Simulink. An experimental setup is developed and built around a Motor-Driven Power Steering (MDPS) unit which is a column-assist type EPAS system made by Hyundai Mobis. The simulation and experimental results are presented to verify and evaluate the effectiveness of the proposed algorithm.

Robust Attitude Estimation Using IMU-Only Measurements

This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the IMU-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain robustness. The proposed algorithms provide an adaptive method for tuning the measurement noise covariance such that they can accurately estimate the attitude in the two axes. The first method relies on a single tuning factor, whereas the second one tunes the covariance with different (multiple) factors for each measurement axis. The proposed methodologies are tested and compared with other existing filtering algorithms in the literature under different dynamical conditions and using real-world experimental datasets in order to validate their effectiveness. Results show that highly dynamic scenarios, especially the multiple tuning factor strategy, can increase the attitude estimation accuracy more than two-times compared to the competitive algorithms.

Attitude Estimation Using Low-Cost MARG Sensors With Disturbances Reduction

Attitude information obtained from low-cost magnetic, angular rate, and gravity (MARG) sensors is prone to be deteriorated by disturbances such as external acceleration and magnetic anomaly in operational environments. To address the problem, a robust attitude estimation algorithm using low-cost MARG sensors is proposed. An error state Kalman filter (ESKF) is used for data fusing, in which the attitude errors are interpreted as a small rotation vector, and the gyro bias variation is also estimated. The nominal state and the error state are propagated with unit quaternion and rotation vector, respectively. An attitude correction is followed which combines the nominal and error parts by quaternion multiplication. The robustness of the proposed algorithm is embodied in resisting accuracy degradation of the attitude estimation caused by both external acceleration and magnetic anomalies. While external accelerations and magnetic disturbances are detected online, the accuracy reduction in attitude estimation is prevented by adaptively adjusting the related measurement noise covariance matrix. Besides, a two-step measurement update strategy is designed to guarantee that the roll and pitch update is separated from the yaw update. Various rotation and land vehicle dynamic tests have been conducted to validate the effectiveness and robustness of the proposed algorithm.

High-precision roll attitude estimation of decoupled canards relative to the projectile body using bipolar hall-effect sensors

The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation method has the advantages of the high accuracy and good real-time performance.

Proposing New Planar-Type Search Coil for Permanent Magnet Synchronous Motor: Design and Application for Position Estimation

Conventional position sensors for permanent magnet synchronous motor, such as optical encoder, resolver, or hall sensor, can get fault by the stress. The fault of the motor position sensor may cause serious accidents. Thus, to prevent damages by the position sensor failure, adding a redundancy sensor such as wound search coil is recommendable. In this paper, a new planar search coil design that can be used as a position sensor for permanent magnet synchronous motor is proposed. This new design is a planar coil based on a flexible-printed circuit board that converts airgap flux to a back-electromotive force signal. Unlike the wound search coil design, the proposed design is advantageous for manufacturing and slot-space preservation. Also, this new design is distant from the motor main winding. As a result, the exposure of the electrical and thermal stresses to the sensing coil can be decreased. This research shows the mathematical model for the design of the new planar-shaped airgap flux search coil design. In addition, the position estimation algorithm based on the new search coil design is shown. Finally, the position estimation using the new search coil design is presented.

Permanent magnet synchronous motor sensorless control based on rotor flux observer with low-pass filter adaptation

This paper presents a strategy for a permanent magnet synchronous motor sensorless vector control based on rotor flux estimation considering a rotor saliency. The integration problem in the stator flux estimation was solved by replacing the integrator with a low-pass filter. The low-pass filter parametric adaptation algorithm with phase delay compensation, based on Bode diagrams was designed for the rotor angular position estimation accuracy improving. The proposed adaption algorithm allowed to expand a stable motor control with a high accuracy in the rotor position estimation down to 15 rpm. Also the developed rotor angular position estimation algorithm together with adaptive field weakening algorithm provides a sable motor operation with a high accuracy in the rotor position estimation at the high speed.

Volleyball Action Extraction and Dynamic Recognition Based on Gait Tactile Sensor

At present, the industry research of volleyball technology is relatively in‐depth, and the analysis of the muscle strength characteristics and coordination of the jumping ball is less, which is not conducive to the control of technical movements. This study used a wireless portable surface EMG tester (16 lines) to analyze the EMG of the main muscle groups in athletes’ volleyball and conducted a video synchronization test method to find the position of the human body. Therefore, a background‐based frame difference method is proposed to detect the position and obtain the precise position of the human body. Experiments show that the background‐based three‐frame difference method effectively eliminates the “hole” effect of the original three‐frame difference method and provides an accurate and complete framework for identifying the human body. Adjust the recognition frame according to the proportion of the human body in the image, and use the predefined parameters of the severe frame to perform forward/volleyball background segmentation. The novelty of this document lies in the completion of the complete human body placement of the above three tasks, precapture/background segmentation, and an improved human body position estimation algorithm to extract the human body pose from the video. First, locate the human body in each frame of the video, and then, perform the process of estimating the position of the graphic model based on the color and texture of the unit. After recognizing the gesture of each image in the video, the recognition result will be displayed. Experiments show that after detecting the position of the human body, the predefined frame setting process of the tomb is carried out in two steps, which improves the automation of the human body image detection algorithm, effectively extracts the human motion video, and increases the motion capture rate by more than 30%, to provide a useful reference for the improvement of college volleyball players’ movement skills and training competitions.

A Reliable Sensorless Starting Scheme for SRM With Lowered Pulse Injection Current Influences

In this article, a synthetic sensorless starting scheme, which is based on pulse injection technology, is proposed for switched reluctance motor (SRM) drives to enhance the starting reliability. This scheme consists of a set of sensorless position estimation algorithms for different stages of SRM starting. In order to start the SRM from standstill stage smoothly, the initial driving phase is selected by pairwise comparing of the three-phase inductance. Then, to ensure reliable starting acceleration, a double inductance dynamic thresholds algorithm is developed for estimating the direct phase-driving signals. Meanwhile, to estimate the real-time rotor position, a position index pulse estimation algorithm, which is with phase lacking fault-tolerant capability, is developed based on an idle phase inductance threshold. By seamless switching between the above mentioned two algorithms, higher reliability of the sensorless starting control can be achieved. Furthermore, the performance of three pulse injection methods are compared, and the best one is then selected to reduce the influences of the high-frequency pulse signals in the idle phases. The theoretical analysis and experimental results are provided to verify the feasibility of the proposed control scheme.