Mobile Device Positioning Algorithm Selection
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Solution Overview
Problem
Existing mobile device positioning technologies face inaccuracies when switching between walking and driving states, as they often use inappropriate positioning algorithms, leading to suboptimal positioning results.
Innovation Solution
Implement a method where mobile devices use a first positioning algorithm (e.g., vehicle dead reckoning) when detecting consecutive driving states exceeding a threshold (M) and a second algorithm (e.g., pedestrian dead reckoning) when detecting consecutive walking states exceeding a different threshold (N), thereby ensuring accurate status determination and improved positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the mobile device uses a single positioning algorithm regardless of status, then the device complexity is reduced, but the positioning accuracy deteriorates when the algorithm does not match the actual status
Solution Approach 1:
The patent applies dynamics by making the positioning algorithm selection adaptive to changing device statuses. The system dynamically switches between different positioning algorithms (pedestrian dead reckoning, vehicle dead reckoning, or wireless positioning) based on the detected status, rather than using a static single algorithm. This resolves the contradiction by allowing complexity to increase only when necessary for accuracy.
Solution Approach 2:
The patent changes the parameter of algorithm selection based on status parameters detected by sensors. When the device status changes (detected through acceleration, velocity, or wireless signal characteristics), the system changes which positioning algorithm is active. This parameter-based adaptation resolves the contradiction between simplicity and accuracy.
2Measurement precision
If the mobile device frequently switches positioning algorithms to match status changes, then the positioning accuracy is improved, but the CPU resource consumption increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors device status through sensors and adjusts algorithm selection accordingly. The feedback loop detects status changes and triggers algorithm switching only when necessary, avoiding unnecessary CPU consumption while maintaining positioning accuracy. The system uses detected status information as feedback to control algorithm selection.
Solution Approach 2:
The patent applies partial action by not switching algorithms for every minor status fluctuation. Instead, it uses threshold-based detection (e.g., acceleration thresholds, velocity thresholds) to determine when status changes are significant enough to warrant algorithm switching. This partial switching approach reduces CPU consumption while maintaining sufficient positioning accuracy.
3Measurement precision
If the mobile device uses threshold-based status detection, then the positioning accuracy is improved by reducing false status changes, but the device may fail to respond to rapid status transitions
Solution Approach 1:
The patent applies preliminary action by pre-setting multiple thresholds and conditions for status detection. Instead of reacting to each individual status change, the system has pre-configured criteria (acceleration thresholds, velocity thresholds, signal strength thresholds) that anticipate different status scenarios. This allows rapid response to genuine status changes while filtering out noise through pre-established criteria.
Solution Approach 2:
The patent segments the status detection process into multiple independent detection dimensions (acceleration-based detection, velocity-based detection, wireless signal-based detection). Each dimension operates with its own thresholds and criteria, allowing the system to comprehensively determine device status without relying on a single threshold that might cause false positives or miss rapid changes.
Data Source
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AI summary
This application discloses a positioning method and a mobile device. The method is applied to the mobile device, and the method includes: periodically detecting a status of the mobile device, where the status of the mobile device includes a driving state and a walking state; positioning the mobile device by using a first positioning algorithm when detecting that a quantity of times that the driving state consecutively occurs reaches M, where M is an integer greater than 1; and positioning the mobile device by using a second positioning algorithm when detecting that a quantity of times that the walking state consecutively occurs reaches N, where N is an integer greater than 1. It can be learned that the status of the mobile device can be more accurately determined by implementing this technical solution, so as to position the mobile device by using a positioning algorithm matching an actual status of the mobile device, thereby improving accuracy of a positioning result.