Mobile Device Location Accuracy via Environmental Parameter Confidence
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Solution Overview
Problem
Conventional location determination methods, such as GPS, become inefficient in indoor environments, leading to delayed emergency response due to the inability to accurately determine a mobile device's location within buildings or covered structures.
Innovation Solution
A method that combines environmental and wireless communication parameters, such as background noise, temperature, and signal strength, to determine a mobile device's location by calculating a confidence factor based on deviations from pre-stored data, allowing for accurate location estimation even in challenging GPS environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS signal strength is used for location determination, then location accuracy is improved outdoors, but location determination becomes inefficient and inaccurate indoors
Solution Approach 1:
The patent combines multiple location determination methods including GPS, cellular tower triangulation, Wi-Fi positioning, and indoor sensors (accelerometer, gyroscope, magnetometer) into a unified location determination system. This merging of multiple data sources allows the system to maintain high location accuracy both outdoors where GPS is available and indoors where GPS signals are unavailable, resolving the contradiction between GPS-based accuracy and indoor reliability.
Solution Approach 2:
The patent dynamically changes the parameters and weighting of different location determination methods based on environmental conditions and signal availability. When GPS signals are strong, the system weights GPS data heavily; when GPS signals are weak or unavailable indoors, the system switches to weighting cellular, Wi-Fi, and sensor data more heavily. This dynamic parameter adjustment maintains location determination reliability across different environments.
2Device complexity
If conventional location methods are used indoors, then device complexity is reduced, but location determination accuracy deteriorates
Solution Approach 1:
The patent segments the location determination process into multiple independent modules: GPS module, cellular tower module, Wi-Fi module, and sensor fusion module. Each module operates independently and processes specific types of data, then the results are integrated through a coordination mechanism. This segmentation allows the system to achieve high location accuracy indoors without requiring a single complex system, as each module remains relatively simple but their combined output provides accurate indoor positioning.
3Measurement precision
If multiple parameters are combined for location determination, then location accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and filtering data from multiple sources before final location determination. The system pre-calculates and stores reference data from cellular towers, Wi-Fi networks, and environmental sensors, and applies preliminary filtering algorithms to identify and remove erroneous data points. This preliminary action reduces the computational burden during actual location determination, as the system only needs to process and integrate pre-processed data rather than raw data from multiple sources in real-time.
Data Source
AI summary
Locating a mobile device in the event of an emergency may be based on common location techniques, such as GPS and geolocation. Additional location techniques may be performed in a sample method of operation which may include determining location information and environmental factor information at a mobile device, recording the location information and the environmental factor information, calculating deviations for the location information and the environmental factor information based on pre-stored location information and pre-stored environmental information, and calculating a confidence factor based on the calculated deviations, determining the mobile device location based on the confidence factor.