Motion Parameter Estimation Using Inertial Sensors
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Solution Overview
Problem
Existing motion sensing apparatuses require GPS receivers and inertial sensors to be used together, increasing size and power consumption, and are unable to estimate motion parameters independently of external signals.
Innovation Solution
A system that includes a receiver, inertial sensors, and a processing system capable of generating a motion model to estimate motion parameters using signals from both sources, allowing parameter estimation even when the receiver is disabled or unable to receive external signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS receivers and inertial sensors are used together to accurately estimate motion parameters, then measurement precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing inertial sensor data to create motion models and calibration parameters before actual use. The processing system generates motion models based on inertial sensor signals during calibration phases, so that when GPS signals are unavailable, the pre-generated models can immediately provide accurate motion parameter estimates without requiring the full GPS-inertial sensor suite to be physically present.
Solution Approach 2:
The patent extracts the essential motion estimation capability from the GPS-receiver combination by demonstrating that inertial sensor data, when processed through motion models generated during GPS-available periods, can independently provide accurate motion parameters. This extraction allows the system to function with only inertial sensors in certain conditions, reducing device complexity while maintaining measurement precision.
2Reliability
If GPS receivers and inertial sensors are used simultaneously to ensure continuous motion parameter estimation, then reliability is improved, but power consumption increases
Solution Approach 1:
The system dynamically adapts its sensor usage based on GPS signal availability. When GPS signals are present, the system uses both GPS and inertial sensors to generate motion models and maintain high reliability. When GPS signals are unavailable, the system transitions to using only the inertial sensor with pre-generated motion models, thereby reducing power consumption while maintaining continuous estimation capability through the dynamic switching between different operational modes.
Solution Approach 2:
The system employs periodic calibration phases during which GPS and inertial sensor data are collected together to generate and update motion models. These periodic actions occur during GPS-available periods, allowing the inertial sensor to be calibrated against GPS references at intervals. This periodic calibration enables the inertial sensor to maintain accuracy over time without requiring continuous GPS co-location, reducing overall power consumption while ensuring continuous reliable estimation.
3Device complexity
If inertial sensors are used alone to reduce device size, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system implements feedback mechanisms where motion models generated during GPS-available periods serve as reference standards for calibrating and validating inertial sensor measurements. The processing system continuously refines motion models using feedback from GPS data when available, and uses these refined models to correct and enhance inertial sensor readings during GPS-unavailable periods. This feedback loop maintains measurement precision by constantly improving the accuracy of the inertial sensor-based estimates.
Solution Approach 2:
The patent applies parameter changes by transforming raw inertial sensor signals into processed motion parameters through motion models. The system changes the representation of motion data from raw accelerometer/gyroscope signals to calibrated motion parameters (speed, distance, orientation) by applying transformation parameters derived from motion models. This parameter transformation enables the system to achieve GPS-level precision using only inertial sensors, as the transformed parameters reflect accurate motion states even when GPS is unavailable.
Data Source
AI summary
A system for estimating motion parameters corresponding to a user. The system may generally include a receiver operable to receive a signal from an external source, an inertial sensor operable to be coupled with the user and arbitrarily oriented relative to the direction of user motion for generation of a signal corresponding to user motion, and a processing system in communication with the receiver and inertial sensor. The processing system can be operable to utilize the receiver signal to estimate a first parameter corresponding to a first motion parameter type, utilize the inertial sensor signal to estimate a second parameter corresponding to a second motion parameter type, generate a user-specific motion model to correlate the first parameter type and second parameter type using at least the first and second estimated parameters, utilize the inertial sensor signal to estimate a third parameter corresponding to the second parameter type, and utilize the motion model and the third parameter to estimate a fourth parameter corresponding to the first parameter type independent of the receiver signal.