scholarly journals Attitude Determination for GRACE-FO: Reprocessing the Level-1A SC and IMU Data

2021 ◽  
Vol 14 (1) ◽  
pp. 126
Author(s):  
Fan Yang ◽  
Lei Liang ◽  
Changqing Wang ◽  
Zhicai Luo
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Angular Acceleration ◽  
Sensor Data ◽  
Combination Method ◽  
Measurement Unit ◽  
Filter Method ◽  
Satellite Gravity ◽  
Range Rate ◽  
Gravity Mission

The satellite gravity mission GRACE(-FO) has not yet reached its designed baseline accuracy. Previous studies demonstrated that the deficiency in the sensor system or the related signal processing might be responsible, which in turn motivates us to keep revising the sensor data processing, typically the spacecraft’s attitude. Many efforts in the past have been made to enhance the attitude modeling for GRACE, for instance, the latest release reprocesses the attitude by fusing the angular acceleration with the star camera/tracker (SC) measurements, which helps to reduce the error in Level-2 temporal gravity fields. Therefore, in addition to GRACE, revising GRACE-FO attitude determination might make sense as well. This study starts with the most original raw GRACE-FO Level-1A data including those from three SCs and one IMU (Inertial Measurement Unit) sensors, and manage to generate a new publicly available Level-1B attitude product called HUGG-01 covering from June 2018 to December 2020, using our independently-developed software. The detailed treatment of individual payload is present in this study, and an indirect Kalman filter method is introduced to fuse the multiple sensors to acquire a relatively stable and precise attitude estimation. Unlike the direct SC combination method with a predefined weight as recommended in previous work, we propose an involvement of each SC measurement in the Kalman filter to enable a dynamic weight adjustment. Intensive experiments are further carried out to assess the HUGG-01, which demonstrate that the error level of HUGG-01 is entirely within the design requirement, i.e., the resulting KBR pointing variations are well controlled within 1 mrad (pitch), 5 mrad (roll) and 1 mrad (yaw). Moreover, comparisons with the official JPL-V04 attitude product demonstrate an equivalent performance in the low-to-middle spectrum, with even a slightly lower noise level (in the high spectrum) than JPL-V04. Further analysis on KBR range-rate residuals and gravity recovery on Jan 2019 indicates that, i.e., RMS of the difference (HUGG-01 minus JPL-V04) for the range rate is less than 3.234×10−8 m/s, and the amplitude of geoid height difference is approximately 0.5 cm. Both differences are below the sensitivity of the state-of-the-art satellite gravity mission, demonstrating a good agreement between HUGG-01 and JPL-V04.

Application of Kalman Filter in the Attitude Four-Axis Aircraft Control System

2012 ◽  
Vol 241-244 ◽  
pp. 1261-1264 ◽  
Author(s):  
Xuan Cui ◽  
Zhong Yan Fan ◽  
Hua Sun
Keyword(s):  
Kalman Filter ◽  
Flight Control ◽  
Attitude Control ◽  
Sensor Data ◽  
Measurement Unit ◽  
Filter Method ◽  
The Core ◽  
Aircraft Flight ◽  
Acceleration Sensors ◽  
Aircraft Flight Control

Attitude control is the core of the four-axis aircraft flight control，By analyzing the principle of four-axis aircraft flight，this paper, on the basis of the traditional method ,acceleration sensors to obtain data, we use the Kalman filter method, fuse acceleration sensor data and gyroscope data, MCU as the core of the inertial measurement unit test，The results showed that，The aircraft can be better stabilized at the test platform, can achieve the requirements of attitude control of the four-axis aircraft.

Attitude Determination by Integration of Multi-Sensors Based on Federated Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2958-2961
Author(s):  
Tao Lai ◽  
Guang Long Wang ◽  
Wen Jie Zhu ◽  
Feng Qi Gao
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Attitude Determination ◽  
Angular Rate ◽  
General Structure ◽  
Test System ◽  
Measurement Unit ◽  
Storage Test ◽  
Inertial Measurement ◽  
Federated Kalman Filter

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

Comparison of Attitude Determination Methodologies for Implementation with 9DOF, Low Cost Inertial Measurement Unit for Autonomous Aerial Vehicles

2013 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Man Ho Choi ◽  
Robert Porter ◽  
Bijan Shirinzadeh
Keyword(s):  
Kalman Filter ◽  
Gradient Descent ◽  
Attitude Determination ◽  
Low Cost ◽  
Euler Angle ◽  
Measurement Unit ◽  
Descent Algorithm ◽  
Complementary Filter ◽  
Gradient Descent Algorithm ◽  
Reference Input

The performances of three attitude determination algorithms are compared in this paper. The three methods are the Complementary Filter, a Quaternion-based Kalman Filter and a Quaternion-based Gradient Descent Algorithm. An analysis of their performance based on an experimental investigation was undertaken. This paper shows that the Complementary Filter requires the least computational power; Quaternion-based Kalman Filter has the best noise filtering ability; and the Quaternion-based Gradient Descent Algorithm produced estimates with the highest accuracy. As many attitude determination methodologies make use of the quaternion rotation representation, the attitude quaternion to Euler angle singularity property has been investigated. Experiments conducted show that when Y-rotation approach the singularity position (±90°), the X-rotation drifts away from the reference input. This paper proposes the use of an imaginary set of sensor measurements to replace the original sensor measurements as the Y-rotation approaches the singularity. The proposed methodology for overcoming the conversion singularity has been experimentally verified.

scholarly journals LOW-COST NAVIGATION AND GUIDANCE SYSTEMS FOR UNMANNED AERIAL VEHICLES — PART 2: ATTITUDE DETERMINATION AND CONTROL

2013 ◽  
Vol 20 (1) ◽  
pp. 97-126 ◽  
Author(s):  
Roberto Sabatini ◽  
Leopoldo Rodríguez ◽  
Anish Kaharkar ◽  
Celia Bartel ◽  
Tesheen Shaid ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Attitude Determination ◽  
Low Cost ◽  
Sensor Data ◽  
Measurement Unit ◽  
Medium Size ◽  
Integrated Navigation ◽  
Aerial Vehicles ◽  
Guidance Systems ◽  
And Control

ABSTRACT This paper presents the second part of the research activity performed by Cranfield University to assess the potential of low-cost navigation sensors for Unmanned Aerial Vehicles (UAVs). This part focuses on carrier-phase Global Navigation Satellite Systems (GNSS) for attitude determination and control of small to medium size UAVs. Recursive optimal estimation algorithms were developed for combining multiple attitude measurements obtained from different observation points (i.e., antenna locations), and their efficiencies were tested in various dynamic conditions. The proposed algorithms converged rapidly and produced the required output even during high dynamics manoeuvres. Results of theoretical performance analysis and simulation activities are presented in this paper, with emphasis on the advantages of the GNSS interferometric approach in UAV applications (i.e., low cost, high data-rate, low volume/weight, low signal processing requirements, etc.). The simulation activities focussed on the AEROSONDE UAV platform and considered the possible augmentation provided by interferometric GNSS techniques to a low-cost and low-weight/volume integrated navigation system (presented in the first part of this series) which employed a Vision-Based Navigation (VBN) system, a Micro-Electro-Mechanical Sensor (MEMS) based Inertial Measurement Unit (IMU) and code-range GNSS (i.e., GPS and GALILEO) for position and velocity computations. The integrated VBN-IMU-GNSS (VIG) system was augmented using the inteferometric GNSS Attitude Determination (GAD) sensor data and a comparison of the performance achieved with the VIG and VIG/GAD integrated Navigation and Guidance Systems (NGS) is presented in this paper. Finally, the data provided by these NGS are used to optimise the design of a hybrid controller employing Fuzzy Logic and Proportional-Integral-Derivative (PID) techniques for the AEROSONDE UAV.

scholarly journals Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Wang ◽  
Bo Song ◽  
Xueshuai Han ◽  
Yongping Hao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Transition Process ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Mems Sensors ◽  
Fusion Algorithm ◽  
Single Antenna

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

Research on Satellite Multi-Sensor Attitude Determination System

2012 ◽  
Vol 220-223 ◽  
pp. 1917-1921
Author(s):  
Lin Zhao ◽  
Zhong Hua Su ◽  
Yong Hao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Sensor Data ◽  
Performance Simulation ◽  
Sun Sensor ◽  
Satellite Attitude ◽  
Sensor Drift ◽  
Determination System ◽  
Simulation Results ◽  
Rate Sensor

An attitude determination system has been designed for the geocentric pointing triaxial stabilized satellites which employ a continuously running inertial rate sensor in conjunction with sun sensor and earth sensor. Earth/sun sensor data are processed to generate corrections to satellite attitude, gyro constant drift and earth sensor drift bias estimates. An extended Kalman filter based on the attitude determination system is derived in this paper for the satellite using two earth sensors, a two-axis digit sun sensor as attitude sensors and a three-axis gyro for the angular velocity. A simulation model is developed to estimate the attitude determination performance. Simulation results show that precision attitude determination is achieved using the selected attitude hardware and algorithms.

scholarly journals Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle

2019 ◽  
Vol 38 (10-11) ◽  
pp. 1286-1306 ◽  
Author(s):  
Adrian Battiston ◽  
Inna Sharf ◽  
Meyer Nahon
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Unscented Kalman Filter ◽  
Real Data ◽  
Attitude Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Post Processing ◽  
Extensive Evaluation ◽  
Aerial Vehicle

An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned filters is first evaluated in a simulation environment using modified sensor models to capture the effects of collision on inertial measurements. Simulation results help define the efficacy and use cases of the conventional and novel algorithms in a quadcopter collision scenario. An analogous evaluation is then conducted by post-processing logged sensor data from collision flight tests, to gain new insights into algorithms’ performance in the transition from simulated to real data. The post-processing evaluation compares each algorithm’s attitude estimate, including the stock attitude estimator of the PixHawk controller, to data collected by an offboard infrared motion capture system. Based on this evaluation, two promising algorithms, the MEKF and an adaptive [Formula: see text] filter, are selected for implementation on the physical quadcopter in the control loop of the collision recovery pipeline. Experimental results show an improvement in the metric used to evaluate experimental performance, the time taken to recover from the collision, when compared with the stock attitude estimator on the PixHawk (PX4) software.

scholarly journals Performance Analysis of Alignment Process of MEMS IMU

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Vadim Bistrov
Keyword(s):  
Kalman Filter ◽  
Data Processing ◽  
Attitude Estimation ◽  
Sensor Data ◽  
Inertial System ◽  
Measurement Unit ◽  
Mems Sensor ◽  
Alignment Process ◽  
Mems Imu ◽  
The Impact

The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given.

scholarly journals Kalman Filter Realization for Orientation and Position Estimation on Dedicated Processor

2014 ◽  
Vol 8 (2) ◽  
pp. 88-94 ◽  
Author(s):  
Sławomir Romaniuk ◽  
Zdzisław Gosiewski
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Filter Design ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Gps Receiver ◽  
Measurement Devices ◽  
Proper Estimation ◽  
Gps Accuracy

Abstract This paper presents Kalman filter design which has been programmed and evaluated in dedicated STM32 platform. The main aim of the work performed was to achieve proper estimation of attitude and position signals which could be further used in unmanned aeri-al vehicle autopilots. Inertial measurement unit and GPS receiver have been used as measurement devices in order to achieve needed raw sensor data. Results of Kalman filter estimation were recorded for signals measurements and compared with raw data. Position actualization frequency was increased from 1 Hz which is characteristic to GPS receivers, to values close to 50 Hz. Furthermore it is shown how Kalman filter deals with GPS accuracy decreases and magnetometer measurement noise.

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