7.3 Sensor fusion and complementary filtering

Sensor fusion combines data from multiple spacecraft sensors to improve accuracy and reliability in attitude determination. It integrates information from gyroscopes, accelerometers, and magnetometers to reduce uncertainty and compensate for individual sensor limitations.

Complementary and Kalman filtering are key techniques in sensor fusion. Complementary filters blend high and low-frequency data, while Kalman filters estimate system state using statistical methods. These approaches enhance attitude determination across various operational scenarios.

Sensor Fusion Techniques

Fundamentals of Sensor Fusion

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• Sensor fusion combines data from multiple sensors to improve accuracy and reliability
• Integrates information from various sources (gyroscopes, accelerometers, magnetometers)
• Reduces uncertainty and compensates for individual sensor limitations
• Enhances overall system performance in attitude determination and control
• Improves robustness against sensor failures or environmental disturbances

Complementary and Kalman Filtering

• Complementary filter blends high-frequency and low-frequency sensor data
  • Uses weighted average of different sensor inputs
  • Typically combines gyroscope and accelerometer data
  • Provides quick response and long-term stability
• Kalman filter estimates system state using statistical methods
  • Recursive algorithm predicts and updates state estimates
  • Minimizes mean squared error of estimates
  • Handles noisy measurements and system uncertainties
  • Widely used in spacecraft attitude determination

Advanced Fusion Techniques

• Multi-sensor integration incorporates data from diverse sensor types
  • Combines information from star trackers, sun sensors, and inertial measurement units
  • Improves attitude estimation accuracy across various operational scenarios
• Data fusion algorithms process and merge information from multiple sources
  • Include methods like Bayesian inference, fuzzy logic, and neural networks
  • Adapt to changing conditions and sensor characteristics
  • Enable real-time decision-making for attitude control systems

Sensor Error Correction

Noise Reduction Strategies

• Implement digital filtering techniques (low-pass, high-pass, band-pass filters)
• Apply moving average or exponential smoothing to sensor readings
• Utilize wavelet transforms for signal denoising
• Employ sensor-specific noise reduction algorithms (Allan variance analysis for gyroscopes)
• Implement oversampling and decimation techniques to improve signal-to-noise ratio

Bias Estimation and Calibration

• Bias estimation identifies and corrects systematic errors in sensor measurements
  • Utilizes statistical methods to determine constant offsets
  • Implements adaptive algorithms for time-varying biases
• Sensor calibration improves measurement accuracy and precision
  • Involves determining scale factors, misalignment angles, and non-linearity corrections
  • Performs in-flight calibration to account for environmental changes
  • Uses reference measurements or known physical properties for calibration

Advanced Error Correction Techniques

• Implement temperature compensation for sensor outputs
  • Corrects for thermal drift in sensor characteristics
  • Uses onboard temperature sensors for real-time adjustments
• Apply cross-axis sensitivity corrections for inertial sensors
  • Accounts for unintended measurements along non-primary axes
  • Improves overall sensor accuracy and performance
• Implement sensor fusion-based error correction
  • Uses data from multiple sensors to identify and correct individual sensor errors
  • Enhances overall system reliability and accuracy

Attitude Representation

Quaternion-Based Attitude Description

• Attitude quaternion represents spacecraft orientation in three-dimensional space
  • Consists of four components: one scalar and three vector elements
  • Avoids singularities associated with Euler angle representations
  • Enables efficient attitude computations and transformations
• Quaternion multiplication describes successive rotations
  • Allows for smooth interpolation between orientations
  • Facilitates attitude control algorithm implementation
• Quaternion-to-rotation matrix conversion
  • Enables transformation between different attitude representations
  • Supports integration with various sensor outputs and control systems

Quaternion Operations and Properties

• Quaternion normalization ensures unit magnitude for valid attitude representation
• Quaternion conjugate represents the inverse rotation
• Quaternion differentiation relates angular velocity to attitude change
  • $\dot{q} = \frac{1}{2}q \otimes \omega$
  • Where $q$ is the attitude quaternion and $\omega$ is the angular velocity vector
• Error quaternion calculation determines the rotation between two orientations
  • Useful for attitude control and trajectory planning

Quaternion Applications in Attitude Determination

• Implement quaternion-based Kalman filters for attitude estimation
  • Avoids numerical issues associated with other representations
  • Provides computationally efficient attitude updates
• Use quaternions in sensor fusion algorithms
  • Integrate data from various attitude sensors (star trackers, sun sensors)
  • Combine quaternion outputs for improved attitude determination
• Apply quaternion algebra in attitude control systems
  • Design feedback controllers using quaternion error
  • Implement quaternion-based steering laws for spacecraft maneuvers