Accelerometer-Assisted Navigation Reducing GPS Power Consumption
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Solution Overview
Problem
GPS receivers in vehicles are power-hungry and require an unoccluded line of sight, making them unsuitable for effective navigation in many scenarios, particularly in urban areas, where they can drain battery quickly and provide inaccurate location data due to shadowing and multipath interference.
Innovation Solution
A method for accelerometer-assisted navigation that collects and associates GPS data with supplementary data such as accelerometer, barometer, and gyroscope data to reduce GPS usage, allowing navigation in areas with poor GPS reception by creating associations between position and motion data, enabling navigation using these supplementary data sources alone or in combination with GPS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS receiver is used continuously for navigation, then location data accuracy is maintained, but power consumption increases and battery life decreases
Solution Approach 1:
The system uses periodic GPS updates combined with continuous accelerometer data processing. GPS is activated at intervals to provide location fixes, while the accelerometer continuously tracks motion patterns between GPS updates, reducing overall power consumption while maintaining navigation accuracy.
Solution Approach 2:
The accelerometer serves as an intermediary device that bridges GPS updates. It captures motion data between GPS fixes and uses this information to estimate position changes, allowing the system to maintain location accuracy without relying solely on continuous GPS operation.
2Ease of operation
If GPS receiver operates in urban areas with buildings, then navigation is provided, but signal accuracy deteriorates due to shadowing and multipath interference
Solution Approach 1:
The accelerometer acts as an intermediary that compensates for GPS signal degradation in urban environments. By tracking vehicle motion patterns, it provides supplementary position information that corrects GPS errors caused by building shadowing and multipath interference.
Solution Approach 2:
The system changes the operational parameters of GPS reception by using intermittent rather than continuous GPS fixes. This allows the system to adapt to urban environments where continuous GPS signals are unreliable, using accelerometer data to bridge gaps between successful GPS acquisitions.
3Duration of action of stationary object
If GPS usage is reduced to conserve power, then battery life is extended, but navigation reliability in poor reception areas decreases
Solution Approach 1:
The accelerometer serves as a reliable intermediary that maintains navigation functionality when GPS is unavailable or unreliable. It continuously tracks vehicle motion and uses this data to estimate position, ensuring navigation reliability even with reduced GPS usage.
Solution Approach 2:
The system performs preliminary association between accelerometer data and GPS location data when GPS signals are available. This pre-established relationship allows the accelerometer to provide accurate position estimates during periods when GPS is turned off or signals are poor, maintaining navigation reliability.
Data Source
AI summary
A method for primarily sensor-based navigation includes: in a first time period, collecting geophysical position data using a GPS receiver of a navigation device; in the first time period, collecting a first set of accelerometer data using an accelerometer of the navigation device; analyzing the first set of accelerometer data to produce a first set of vertical vehicular motion data; generating a mapping association between the first set of vertical vehicular motion data and the geophysical position data; in a second time period after the first time period, collecting a second set of accelerometer data using the accelerometer; analyzing the second set of accelerometer data to produce a second set of vertical vehicular motion data; and calculating an estimated location of the vehicle by analyzing the second set of vertical vehicular motion data in light of the mapping association.