Orientation Estimation Using Kalman Filter Sensor Fusion
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Solution Overview
Problem
Existing orientation measurement systems face challenges in accurately tracking the 3D orientation of rigid bodies in noisy environments due to physical, thermal, electric, magnetic, and other types of noise, especially in price-sensitive and size-limited consumer electronics devices where MEMS-based sensors are used, which are prone to performance compromise.
Innovation Solution
A method that utilizes angular velocity, linear acceleration, and magnetic field information to estimate orientation, incorporating Kalman filters with Markov chains to suppress disturbances and provide gyroscope bias output, while using simulated orientations to tune sensor data and handle magnetic field effects, allowing for accurate orientation estimation in various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If MEMS-based sensors are used in consumer electronics devices, then device size and cost are reduced, but measurement precision deteriorates due to noise and disturbances
Solution Approach 1:
The orientation measurement system is segmented into multiple independent sensor components (accelerometer, gyroscope, magnetometer) that each measure different physical quantities. By dividing the measurement task across multiple specialized sensors rather than relying on a single MEMS sensor, the system achieves higher precision while maintaining compact size.
Solution Approach 2:
The patent combines data from multiple sensor types (accelerometer, gyroscope, magnetometer) through sensor fusion algorithms to produce a unified orientation estimate. This merging of complementary measurements compensates for individual sensor weaknesses and achieves high precision orientation tracking suitable for consumer electronics.
2Device complexity
If traditional orientation estimation methods are used in noisy environments, then system complexity remains low, but reliability deteriorates due to magnetic disturbances and noise
Solution Approach 1:
The system implements feedback through Kalman filtering, where orientation estimates are continuously refined by comparing predicted measurements with actual sensor readings. This feedback mechanism compensates for magnetic disturbances and noise, significantly improving reliability while maintaining manageable system complexity through efficient algorithm design.
Solution Approach 2:
The patent dynamically adjusts measurement parameters and filter weights based on environmental conditions and sensor performance. By changing parameters such as filter gain, measurement noise covariance, and process noise models in response to detected disturbances, the system maintains high reliability across varying operational conditions.
3Duration of action of stationary object
If orientation tracking is performed over time in dynamic environments, then continuous orientation information is obtained, but measurement precision deteriorates due to accumulated errors and disturbances
Solution Approach 1:
The system maintains continuous orientation tracking by continuously fusing sensor measurements and updating the orientation estimate without interruption. This continuous operation, combined with error correction mechanisms, prevents precision degradation over time and maintains accurate orientation information throughout extended tracking periods.
Solution Approach 2:
The Kalman filter acts as an intermediary that processes and reconciles measurements from multiple sensors over time. This intermediary computation layer integrates accelerometer, gyroscope, and magnetometer data while compensating for accumulated errors, thereby maintaining precision during continuous tracking in dynamic environments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enhances the accuracy of orientation estimation by mitigating magnetic disturbances and maintaining precision over time, even in noisy conditions, and can be implemented in devices like augmented reality devices and consumer electronics without increasing size or cost.
Implementation Method 1
receiving a magnetic field state of an object... receiving a magnetic field measurement
Implementation Method 2
determining a gravitational term associated with the object... determining the gravitational acceleration state term
Implementation Method 3
receiving an inertial measurement unit (IMU) measurement... determining a magnetic field state term
Data Source
AI summary
Systems and methods for determining orientations measurements are provided. In one aspect, the method includes receiving a magnetic field state of an object, receiving a magnetic field measurement associated with the object, receiving an inertial measurement unit (IMU) measurement associated with the object, receiving a previous gravitational state term associated with the object, determining a gravitational acceleration state term based on the IMU measurement and the previous gravitational state term, determining a magnetic field state term based on the IMU measurement, the magnetic field measurement, and the gravitational acceleration term, and determining an orientation of the object using the gravitational acceleration state term and the magnetic field state term. The magnetic field measurement may be received from a magnetometer, and the IMU measurement may be received from a gyroscope and an accelerometer.