Mobile Device Localization Timeout-Based Data Fusion Switching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing indoor navigation systems for mobile devices face challenges in providing seamless and responsive positioning within indoor environments due to high resource consumption by wireless signal data processing, leading to delays, especially in lower quality infrastructure and older devices.
Innovation Solution
A method that localizes a mobile device by monitoring processing time and switching to a subset of data fusion inputs when the processing time exceeds a threshold, primarily excluding resource-intensive wireless signal data, to ensure timely and responsive positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wireless signal data processing is used for indoor localization, then positioning accuracy is improved, but processing time increases and device resources are consumed
Solution Approach 1:
The system dynamically adjusts the localization method based on processing time thresholds. When processing time exceeds the threshold, the system transitions from using all data fusion inputs (including wireless signal data) to using only sensor data, and further to using only drift compensation. This dynamic adaptation resolves the contradiction by adjusting the level of processing based on temporal constraints.
Solution Approach 2:
The system changes the parameter of data input composition based on processing time conditions. By monitoring processing time and adjusting which data sources are included (full data fusion vs. sensor data only vs. drift compensation only), the system optimizes the balance between positioning accuracy and processing speed.
2Measurement precision
If wireless signal data processing is used for indoor localization, then positioning accuracy is improved, but device power consumption increases
Solution Approach 1:
The system dynamically selects the appropriate localization strategy based on processing time thresholds, which correlate with power consumption. By transitioning between full data fusion, sensor-only localization, and drift compensation, the system adjusts power usage to match actual positioning needs and device capabilities.
Solution Approach 2:
The system extracts and removes the most resource-intensive component (wireless signal data processing) from the data fusion pipeline when processing time thresholds are exceeded. This extraction eliminates the primary source of high power consumption while maintaining acceptable positioning functionality through alternative methods.
3Measurement precision
If full data fusion inputs are used for localization, then positioning accuracy is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts the complexity of the localization pipeline by monitoring processing time. When thresholds are exceeded, it simplifies the data fusion process by excluding wireless signal data and eventually using only drift compensation, thereby reducing device complexity while maintaining functionality.
4Speed
If processing time threshold is set low for responsiveness, then localization speed is improved, but positioning accuracy may deteriorate
Solution Approach 1:
The system uses dynamic threshold-based switching to adapt between different localization strategies. By monitoring processing time against thresholds, the system can switch between full data fusion (higher accuracy), sensor-only (moderate accuracy), and drift compensation (lower accuracy but faster), optimizing the speed-accuracy tradeoff based on real-time conditions.
Data Source
AI summary
A method and system of localizing a mobile device having a processor and a memory. The method comprises localizing the mobile device along a sequence of positions describing a route being traversed in an indoor facility based on a set of data fusion inputs, monitoring a processing time associated with the localizing and when the processing time exceeds a time threshold, localizing the mobile device based on a subset of the set of data fusion inputs.