Mobile Location Tracking With Dwell-Point GNSS Switching
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Solution Overview
Problem
Existing location determination methods in mobile devices, such as GNSS, are energy-intensive and can lead to battery drain and reduced responsiveness, especially when frequent location updates are required, and switching between location technologies can result in inaccurate location estimation due to instability.
Innovation Solution
A mobile device uses an auxiliary processor to collect inertial and wireless fingerprint data during low-power mode, classifies this data to detect dwell points, and performs GNSS measurements only when leaving dwell points, while fusing location data from multiple sources like GPS, cell, and Wi-Fi to create accurate location estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS location determination is performed frequently, then location accuracy is improved, but battery consumption increases and responsiveness decreases
Solution Approach 1:
The patent segments location determination into two distinct modes: GNSS-based high-precision location for mobile states and Wi-Fi-based location for stationary states. This segmentation allows the system to use the appropriate method for each scenario, avoiding unnecessary battery consumption from continuous GNSS usage while maintaining location accuracy when needed.
Solution Approach 2:
The patent implements dynamic switching between different location determination methods based on the device's movement state. The system continuously monitors movement characteristics and dynamically adjusts the location determination strategy, transitioning from GNSS to Wi-Fi when the device is stationary and vice versa, thereby optimizing battery usage while maintaining accuracy.
2Measurement precision
If GNSS location determination is performed continuously, then location tracking is improved, but processing time increases and responsiveness decreases
Solution Approach 1:
The patent segments location tracking into continuous Wi-Fi-based monitoring and periodic GNSS-based updates. Wi-Fi provides continuous location information with minimal processing overhead, while GNSS provides periodic high-precision updates. This segmentation eliminates the processing delays associated with continuous GNSS while maintaining accurate location tracking.
Solution Approach 2:
The patent introduces Wi-Fi location determination as an intermediary method between continuous monitoring requirements and GNSS processing capabilities. Wi-Fi provides a lightweight, continuous location signal that bridges the gap between the need for ongoing location tracking and the computational intensity of GNSS processing, reducing overall processing time.
3Adaptability or versatility
If location determination method is switched between GNSS and other technologies, then adaptability is improved, but location stability decreases due to inaccuracies
Solution Approach 1:
The patent implements dynamic method selection based on real-time movement state detection. The system uses accelerometer and gyroscope data to dynamically determine whether the device is mobile or stationary, then selects the appropriate location method. This dynamic approach ensures smooth transitions between methods based on actual device state, preventing the instability that would result from arbitrary or frequent method switching.
Solution Approach 2:
The patent incorporates feedback mechanisms where location data from both GNSS and Wi-Fi methods is continuously monitored and compared. The system uses this feedback to detect discrepancies and adjust method selection accordingly, ensuring stable location output even during transitions. The feedback loop helps identify when switching methods would cause instability and prevents such transitions.
Data Source
AI summary
The mobile device can preserve battery capacity by periodically determining locations only when the mobile device is moving between locations where the mobile device is relatively static. To conserve power, the mobile device's application processor may be in a low-power mode. Until a wake event, the device's low power auxiliary processor can store inertial information in a buffer. At each wake event, the inertial information can be classified to determine a distance, a direction, and a movement type (e.g., driving or walking). The mobile device may also record a wireless fingerprint. This wireless fingerprint and the classified inertial information can be used to detect that the mobile device has left a dwell point. Until the mobile device reaches a new dwell point, the mobile device's application processor can log the device's GNSS location at each wake event to create the log of the device's location during its journey.