Analysis and Design of a Fifteen State Stellar Inertial Attitude Determination System

Lam, Quang M.; Hunt, Teresa; Sanneman, Paul; Underwood, S.

doi:10.2514/6.2003-5483

Cited by 15 publications

(21 citation statements)

References 3 publications

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“…The MEMS gyroscope output is corrupted and influenced by several random noises [15]. Numerous experiments have shown that angular random walk (ARW) and rate random walk (RRW) are the most dominant and important random errors for MEMS gyroscopes that have low measurement precision.…”

Section: Methodology Comparison Of the Virtual Gyroscope System Modelmentioning

confidence: 99%

Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

Yuan

Xue

et al. 2015

Sensors

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In this paper, the performance of two Kalman filter (KF) schemes based on the direct estimated model and differencing estimated model for input rate signal was thoroughly analyzed and compared for combining measurements of a sensor array to improve the accuracy of microelectromechanical system (MEMS) gyroscopes. The principles for noise reduction were presented and KF algorithms were designed to obtain the optimal rate signal estimates. The input rate signal in the direct estimated KF model was modeled with a random walk process and treated as the estimated system state. In the differencing estimated KF model, a differencing operation was established between outputs of the gyroscope array, and then the optimal estimation of input rate signal was achieved by compensating for the estimations of bias drifts for the component gyroscopes. Finally, dynamic simulations and experiments with a six-gyroscope array were implemented to compare the dynamic performance of the two KF models. The 1σ error of the gyroscopes was reduced from 1.4558°/s to 0.1203°/s by the direct estimated KF model in a constant rate test and to 0.5974°/s by the differencing estimated KF model. The estimated rate signal filtered by both models could reflect the amplitude variation of the input signal in the swing rate test and displayed a reduction factor of about three for the 1σ noise. Results illustrate that the performance of the direct estimated KF model is much higher than that of the differencing estimated KF model, with a constant input signal or lower dynamic variation. A similarity in the two KFs’ performance is observed if the input signal has a high dynamic variation.

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Section: Methodology Comparison Of the Virtual Gyroscope System Modelmentioning

confidence: 99%

Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

Yuan

Xue

et al. 2015

Sensors

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“…where the matrix C is a direction cosine matrix relating the star tracker reference frame to the body frame, and is a vector of measurement white noise whose covariance matrix in the star tracker frame is: For the Kalman filtering algorithm, it is based on a standard scheme [2]- [4]. Therefore, it is not presented here due to the space constraint.…”

Section: Closed-form Solution Of the Q And R Matricesmentioning

confidence: 99%

“…To complete the system model, note the gyro bias stability is modeled as a random walk process and scale factor stability is modeled as a time-correlated noise process [4]. This leads to the following relationships I I I I I x x 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 where the 3x3 matrix is a diagonal matrix version of the estimated rate vector.…”

Section: Dynamic Model Derivationmentioning

confidence: 99%

Enhancing attitude estimation accuracy via system noise optimization

Lam¹,

Lakso²,

Hunt³

et al. 2004

SPIE Proceedings

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It is well known to the Kalman filter design and estimation community that the values for the process noise, Q, and measurement noise, R, covariance matrices primarily dictate the filter performance. In addition, selecting proper values for Q and R is traditionally done in an ad-hoc manner. This paper provides a new look into the roles of the process noise and measurement noise matrices using the spacecraft attitude estimation problem as the design benchmark. This includes an interesting situation where the theoretical values of Q and R, derived as a function of gyro and star tracker noise parameters, are exactly matched with the noise characteristics employed on the sensor model side. However, the filter still exhibits poor attitude estimation performance, as measured against an attitude knowledge requirement, while subject to a high rate slew profile. A simulation based tuning methodology is developed to optimize the filter performance and bring the attitude estimation back to within the required attitude knowledge bound. IntroductionSolutions to the spacecraft Attitude Determination Subsystem (ADS) can be considered as proven and complete since the early 1980's for nominal missions involving low spacecraft rates [1]. Work conducted in the area of advanced ADS development, at various levels, during the past 20 years continues to excel [2]-[6]. These areas include unscented filtering [7], high order and adaptive filtering [3] and [8], and nonlinear observers [10]. Regardless of the filtering scheme selected by designers, the process noise, Q, and measurement noise, R, covariance matrices still dominate the overall performance of the ADS. It is a common misperception that setting the ADS filter noise parameters, in the Q and R matrices, to the actual sensor noise parameters provided by the hardware vendors will demonstrate acceptable ADS filter performance. However, due to the random nature of sensor noise parameters and on-orbit variation, the exact application of the hardware noise parameters in the ADS filter does not guarantee acceptable performance. This methodology certainly does not provide the optimal performance desired in an ADS design. A small amount of research and development work has been conducted in the area of Q and R parameterization for spacecraft ADS design. This paper provides a new look into optimization of the process noise and measurement noise matrices and presents an optimization approach designed to extend the ADS filter performance beyond its normally accepted levels and provide a greater level of performance margin. Background & Motivation on Kalman Filter Based OptimizationThe process noise covariance matrix, Q, is computed as a function of gyro random noise sources, spacecraft rate, and filter update cycle time. The measurement noise covariance matrix, R, is computed as a function of star tracker noise and the star tracker alignment matrix. An exact computed value of the Q and R matrices, based on vendor provided gyro

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“…The attitude error kinematics can be described by [Ref. ]       δα ω δα δω (11) where  δω ω ω . From Eq.…”

Section: Extended Kalman Filtering For Attitude Estimationmentioning

confidence: 99%

Performance Comparison Between Extended Kalman Filter and Sparse Grid Quadrature Filter for Spacecraft Attitude Estimation Using Low Grade Attitude Sensors

Lam

Xin

Jia

2013

AIAA Infotech@Aerospace (I@A) Conference

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Attitude Determination Subsystem (ADS) is essential to the spacecraft pointing control and highly relies on the onboard sensors' quality and filtering techniques. For high quality gyros, it is quite doable to achieve precision ADS using the 6 state EKF. However, for low grade low cost Micro-Electro-Mechanical System (MEMS) gyros and Complementary Metal-Oxide Semiconductor (CMOS) star tracker (ST), especially in higher slew rates type of missions, the 6 state EKF may not be the right or adequate structure to achieve precision attitude estimation capability because MEMS Scale Factor Errors (SFE) and Mis-Alignment Errors (MAE) at high rate will become a dominant error source contaminating gyros' measured rate vector. As a result, SFE and MAE errors may need to be estimated on-line along with the gyro bias errors for real-time error removal and correction to address the high rate slew operating conditions. This paper looks into the emerging Sparse Grid Quadrature Filter (SGQF), a nonlinear point-based Gaussian approximation filtering technique, as an alternative to potentially replace the EKF from the accuracy and robustness standpoints. The study is intended to determine the pros and cons of each filtering scheme and perform a simulation based evaluation of both filters for a possible precision ADS solution using low cost MEMS gyros and CMOS star tracker.

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Analysis and Design of a Fifteen State Stellar Inertial Attitude Determination System

Cited by 15 publications

References 3 publications

Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

Enhancing attitude estimation accuracy via system noise optimization

Performance Comparison Between Extended Kalman Filter and Sparse Grid Quadrature Filter for Spacecraft Attitude Estimation Using Low Grade Attitude Sensors

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