IMU Motion Detection Using Standard Deviation for Low-Speed Vehicles
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Solution Overview
Problem
Low-grade inertial measurement units (IMUs) struggle to accurately detect motion, particularly when vehicles are moving slowly, due to increased thresholds that can lead to incorrect stationary determinations and prolonged convergence times.
Innovation Solution
A system and method utilizing standard deviation of IMU measurements to calculate earth rate and normal gravity values, allowing for more sensitive threshold detection and reduced convergence times by creating rolling histories and comparing standard deviations to adaptive or preconfigured thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thresholds are increased to accommodate low-grade IMU biases and errors, then false motion detection is reduced, but slow motion detection capability deteriorates
Solution Approach 1:
The patent applies dynamics by making the threshold adaptive rather than static. The threshold is dynamically adjusted based on the standard deviation of IMU measurements, allowing the system to automatically lower thresholds when measurement stability indicates reliable data, thereby enabling detection of slow motions while maintaining reliability during high-error periods
Solution Approach 2:
The patent changes the parameter used for threshold determination from a fixed bias-based value to a variable standard deviation-based value. This parameter change allows the threshold to reflect actual measurement quality in real-time, resolving the contradiction between reliability and precision by adapting to changing measurement conditions
2Reliability
If thresholds are increased to accommodate low-grade IMU biases and errors, then false stationary determination is reduced, but convergence time increases
Solution Approach 1:
The adaptive threshold dynamically adjusts during the convergence process based on measurement standard deviation. This allows the system to use lower thresholds early in convergence when measurements are less stable, enabling faster convergence while maintaining reliability as measurements become more stable and thresholds are automatically adjusted
Solution Approach 2:
The system uses feedback from the standard deviation of measurements to continuously adjust the threshold. This feedback mechanism allows the system to learn from measurement quality and adapt thresholds accordingly, reducing convergence time while preventing false stationary determinations through intelligent threshold adjustment based on actual performance
Data Source
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AI summary
In an example embodiment, motion is detected with an IMU utilizing standard deviation. Specifically, an IMU may obtains IMU measurements. An IMU motion detection process may accumulate a particular number of IMU measurements over a time interval to calculate an absolute magnitude of earth rate (ERimu) value and an absolute magnitude of normal gravity value (GNimu). The values calculated may be referred to as a sample. The IMU motion detection process may create sample rolling histories based on a particular number of samples, e.g., consecutive samples. The IMU motion detection process may then calculate standard deviation values for a sample rolling history based on the ERimu and GNimu values included in the sample rolling history. The IMU motion detection process may compare the standard deviation values to respective motion threshold values, which may be adaptive, to determine if a body of interest, e.g., vehicle, is moving or is stationary.