Improved spacecraft attitude filter using a sequentially correlated magnetometer noise model

We address the problem of attitude and rate gyro bias estimation based on inertial and range data. Range measurements are obtained from beacons installed in an inertial frame and acoustic sensors fixed in the moving body reference frame. A nonlinear observer is proposed and its stability and performance properties are studied using Lyapunov techniques. In the presence of constant bias on the rate gyro measurements, the attitude and bias estimates are shown to converge exponentially to the desired values inside an almost global region of attraction. For time-varying bias with bounded derivative, a local result of ultimate boundedness is provided. The performance of the proposed nonlinear observer is validated experimentally using a high-accuracy calibration table, which provides ground-truth signals for comparison with the resulting estimates.

Traditionally, attitude estimation has been performed using a combination of external attitude sensors and internal three-axis gyroscopes. There are many studies of three-axis attitude estimation using gyroscopes that read angular rates. Rate-integrating gyroscopes measure integrated rates or angular displacements, but three-axis attitude estimation using these types of gyroscopes has not been as fully investigated. This paper derives a Kalman filtering framework for attitude estimation using attitude sensors coupled with rate-integrating gyroscopes. To account for correlations introduced by using these gyroscopes, the state vector must be augmented, compared with filters using traditional gyroscopes that read angular rates. Two filters are derived in this paper. The first uses an augmented state-vector form that estimates attitude, gyroscope biases, and gyroscope angular displacements. The second ignores correlations, leading to a filter that estimates attitude and gyroscope biases only. Simulation compa...

In this paper, a method for estimating aircraft att itude is proposed using an accelerometer based device combined with a gyro based sensors for estimating rotational rates. The device incorporates a series of low-cost accelerometers mounted in a circular arc to determine the local gravitational field vector. Once the local gravitational field vector is determined the orientation of the device can be est imated. However, the attitude estimate occurs at discrete time values and therefore is not a continuous estimate. Rate gyros measurements can be integrated producing continuous attitude estimates. However, the attitude estimates produced by rate gyro integratio n tend to drift over time due to drift and bias inherent to the rate gyro sensor. In this pape r, a method for re-initializing the initial condition of the rate gyro integration estimate is proposed using the discrete attitude estimate found from determining the local gravitati onal field vector. In an addition, the rate gyro biases and drift may be computed with knowledge of attitude at discrete intervals and subtracted from the rate gyro measurements producing a more accurate continuous attitude estimate. For this study, pitch angle estimates are only considered to analyze the feasibility of the proposed method but may be easily extended i ncluding the remaining degrees-offreedom. Simulation results of the proposed device are presented including real-world sensor noise and bias/drift effects analyzing the f easibility of the proposed system. A prototype device has been constructed and is currently being tested in a laboratory environment. Results of the simulation study indica te that accurate attitude estimates can be developed in the presence of large rate gyro drift and bias effects.

Attitude control system plays the important role for to maintain the satellite to desired orientation. To control the satellite it is necessary to do the attitude stabilization. Attitude stabilization achieved by Star sensor, sun sensor, Earth sensors. Attitude control is mainly use for antenna pointing accuracy, camera focus to earth surface and solar panel pointing toward sun. Due to tumbling effects, satellite will rotate all the direction in the space. To maintain the orientation of the satellite it is necessary to design the attitude determination and control. Satellite consider as the rigid body. Inertia matrix describes the rigid body dynamics. The orientation of the satellite determine by Euler angle and Quaternion. The Low earth orbiting satellite will have enormous amount of aerodynamic drag stinking the satellite body and gravitational attraction another problem. Because of that satellite dwell, time is reduced. It means satellite more time spending particular part of the earth. The attitude estimation is measures by the orientation of vectors. Attitude estimation means to find the position and orientation of flying object with respect to the fixed reference of reference. Vector remains considered in the frame of reference to compute for find the orientation of the body of the satellite in the inertial reference system. The Earth is an inertial reference frame, and Satellite is a body frame. Attitude sensor used to measure the satellite orientation in the reference frame. This will help in accurately predicting the orbit deviation and a control system to correct if any by providing the satellite momentum means ‘mass in motion’ changes in a body rapidly in Low earth orbit due to centripetal force acting on a satellite. Attitude control system (ACS) need the numerical simulation to find the required torque demand by the help of difference between reference input (Attitude) signal and feedback signal measure by attitude sensor to trim the control surface maintain the actuator required orientation . The results will consist of two parts the first part consisting of the attitude estimation using Euler angle and Quaternion method, second part consist of estimate the control torque from magnetic torquer and error estimation using non-linear filter (Unscented Kalman Filter) with MATLAB simulation. Â

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.

MEMS (micro-electro-mechanical-system) based gyroscopes are commonly used for satellite's attitude determination and control system in recent years. They are packaged in a small form factor and have lower power consumption. They provide a low-cost solution to the emerging new space industry. MEMS gyroscopes exhibit some time-zero null bias with variation over temperature. To overcome this problem, this paper proposes a novel on-orbit gyroscope bias compensation technique based on the model predictive control approach. The proposed method can estimate the gyroscope bias instantly which is not feasible with the typical method. Furthermore, the proposed method has a low computational requirement as compared to the typical EKF (Extended Kalman Filter) method. While the proposed method allows on-orbit compensation of gyroscope bias, the satellite's attitude is required to be nearly at steady state. This limitation will be addressed in detail.

NASA and other space agencies have shown interest in sending humans on missions beyond low Earth orbit. Proposed is an algorithm that estimates the attitude of a manned spacecraft using measured line-of-sight (LOS) vectors to stars and gyroscope measurements. The Manual Optical Attitude Reinitialization (MOAR) algorithm and corresponding device draw inspiration from existing technology from the Gemini, Apollo and Space Shuttle programs. The improvement over these devices is the capability of estimating gyro bias completely independent from re-initializing attitude. It may be applied to the lost-in-space problem, where the spacecraft's attitude is unknown.;In this work, a model was constructed that simulated gyro data using the Farrenkopf gyro model, and LOS measurements from a spotting scope were then computed from it. Using these simulated measurements, gyro bias was estimated by comparing measured interior star angles to those derived from a star catalog and then minimizing the difference using an optimization technique. Several optimization techniques were analyzed, and it was determined that the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm performed the best when combined with a grid search technique. Once estimated, the gyro bias was removed and attitude was determined by solving the Wahba Problem via the Singular Value Decomposition (SVD) approach. Several Monte Carlo simulations were performed that looked at different operating conditions for the MOAR algorithm. These included the effects of bias instability, using different constellations for data collection, sampling star measurements in different orders, and varying the time between measurements. A common method of estimating gyro bias and attitude in a Multiplicative Extended Kalman Filter (MEKF) was also explored and disproven for use in the MOAR algorithm.;A prototype was also constructed to validate the proposed concepts. It was built using a simple spotting scope, MEMS grade IMU, and a Raspberry Pi computer. It was mounted on a tripod, used to target stars with the scope and measure the rotation between them using the IMU. The raw measurements were then post-processed using the MOAR algorithm, and attitude estimates were determined. Two different constellations---the Big Dipper and Orion---were used for experimental data collection. The results suggest that the novel method of estimating gyro bias independently from attitude in this document is credible for use onboard a spacecraft.

In order to enhance the accuracy and the robustness of the attitude determination and control system in observation satellites, a new way to fuse gyro and star tracker measurement with image registration is described. In this method, a novel and complete framework is proposed to estimate the on-orbit attitude variations from multi-spectrum remote sensing images. An extended Kalman filter is derived to calibrate the gyro bias drift and the star tracker error. The new framework is tested with realistically simulated data and remote sensing images based on JL-1 satellite. Simulation and experiment results indicate that based on the image registration, the satellite attitude variations could be detected in real time and applied for the accurate gyro and star tracker bias calibration.

In this paper a novel approach is developed for relative state estimation of spacecraft flying in formation. The approach uses information from an optical sensor to provide multiple line-of-sight vectors from one spacecraft to another. The line-of-sight measurements are coupled with gyro measurements and dynamical models in an extended Kalman filter to determine relative attitude, position, and gyro biases. The quaternion is used to describe the relative kinematics, whereas general relative orbital equations are used to describe the positional dynamics. Three different attitude formulations are presented. The first estimates the relative attitude and individual gyro biases for the chief and deputy spacecraft. The second estimates the relative attitude, and the relative velocity bias and the deputy gyro bias. The third estimates the relative attitude, and the relative velocity bias and the chief gyro bias. Simulation results indicate that the combined sensor/estimator approach provides accurate relative attitude and position estimates.

A method of attitude estimation with a low-cost, strap-on MEMS sensor was proposed in the article. The method relies on dynamic gyroscope bias change estimation and updates during detected stops of the robot. The algorithm has been tested in laboratory with iRobot Roomba robot and should be improved to be useful in an industrial environment. The measurement of attitude of a mobile platform is necessary for correct navigation, especially in autonomous vehicles, which use dead reckoning between position updates from external sources. Since the system is intended to operate indoors, in industrial halls and shops, it cannot avail of GPS and Earth magnetic field sensors because of anomalies, which are common inside the steel constructions. Therefore, the accuracy of the gyroscope-based attitude estimation is significant. The proposed method aims to address the common problem of gyroscope bias drift, by dynamic update of sensor bias and simultaneous use of all gyroscope axes, to improve the quality of the measurements. A popular 3-axial gyroscope and 3-axial accelerometer sensors were used during the test runs. Obtained results suggest that it is possible to improve short-term accuracy of inertial dead reckoning, to get a system that could be of practical use in industrial AGV systems or intelligent vehicles.

Tether dynamics investigations for the Canadian OEDIPUS sounding rocket program

A GROUND LOOP ATTITUDE CONTROL SYSTEM FOR ANIK B

Spinning a ballistic missile is a common practice to stabilize the attitude of the vehicle; however, this requires the vehicle to be accurately balanced to avoid nutation and precession due to axis misalignment. While a missile is spinning, it is subject to nutation attitude behavior depending on the non-diagonal terms of the inertia matrix. Utilizing the onboard Attitude Control System (ACS), the nutation and precession energy can be dissipated to achieve accurate spinning motion. This may be desirable if the vehicle is carrying a sensor package that needs to be pointed accurately. This paper develops the equations of motion for a spinning ballistic missile utilizing the full inertia matrix with non-zero cross products of inertia (POI). The unbalanced condition that is represented by the non-zero cross POI is also evaluated in terms of axis misalignment between principal axis of rotation and geometrical body axis. We present an active nutation control scheme using the missile’s ACS to minimize the nutation and precession angles. A typical nutation control scheme can create an aberration of the precession angle and this interaction is examined with the goal of minimizing both.

This paper proposes using TRIAD and Unscented Kalman Filter (UKF)algorithms in a sequential architecture as a part of the small satellite attitude estimation algorithm. This TRIAD+UKF approach can provide accurate attitude estimates for the satellite by calibrating the magnetometers in real-time. A complete calibration model for the magnetometers, considering bias, scale factor, soft iron and nonorthogonality errors, is assumed. In algorithm's first stage, the TRIAD uses the available vector measurements (e.g. from Sun sensor and magnetometers)to estimate a coarse attitude. In the second stage, these estimated values are used as quaternion measurements in the UKF algorithm, together with angular rate measurements provided by a triad of gyros. The state vector for the UKF is composed of the attitude, gyro biases and the magnetometer error terms. In result, we get fine attitude estimate for the satellite by calibrating the magnetometers against scaling, soft iron, nonorthogonality and bias errors in real-time. We evaluate the algorithm for a hypothetical nanosatellite by numerical simulations. The results show that the attitude of the satellite can be estimated with accuracy better than 1deg and the magnetometers can be fully calibrated.

This paper details a method to improve accuracy of the land vehicle attitude estimation. It employs the vehicle model constraints to enhance the integration of GPS and a low-cost MEMS inertial measurement unit (MIMU) (GPS/MIMU). To improve the yaw angle estimation, we propose a lateral velocity constraint (LVC) aided method based on the observability analysis for the integrated fusion system. The theoretical analysis indicates that the yaw angle will be directly observed if LVC is augmented into the GPS/MIMU fusion algorithm. Furthermore, for LVC/MIMU system without GPS, the roll angle is always well estimated regardless of whether the vehicle is moving or stationary, and, surprisingly, the pitch angle can also be relatively accurately estimated if the vehicle is moving. Thus, when GPS is unavailable, the LVC/MIMU fusion is proposed by replacing the GPS measurement with the virtual measurement LVC. It is an encouraging improvement since the accumulating errors induced by gyro bias can be suppressed using MIMU alone. Moreover, to overcome the shortage that the pitch angle is not observed if the vehicle is stationary, we propose to fuse the gyros with gravity-based angles by Kalman filtering if the vehicle is detected motionless based on a switching rule. The experimental results compare favorably to theoretical analysis, showing that the proposed method can effectively improve the accuracy for land vehicle attitude estimation.