Mobile Location Refinement via Sensor Intersection
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Solution Overview
Problem
Current mapping applications on mobile devices suffer from inaccurate location fixing, leading to phantom jumps and poor navigation due to limitations in sensor accuracy and interference from surroundings, which affects Geo-Fencing and other location-based services.
Innovation Solution
A system that forms subgroups of mobile devices based on proximity information to refine location accuracy by identifying intersections of sensor sensitivity areas and utilizing database information to set precise locations, reducing error radii and enhancing location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location is determined using sensors, WiFi and GPS individually, then location can be obtained, but accuracy is insufficient leading to phantom jumps
Solution Approach 1:
The patent combines location data from multiple mobile devices within a subgroup to determine the location of a target device. By merging proximity information from multiple sources (sensors, WiFi, GPS across different devices), the system creates a more accurate and stable location determination that eliminates phantom jumps caused by relying on a single device's sensors alone.
Solution Approach 2:
The system introduces an intermediary approach where other mobile devices act as mediators to verify and refine location data. Instead of directly trusting a single device's location fix, the system uses proximity information from multiple intermediary devices to cross-validate and accurately determine the target device's location, thereby improving both accuracy and stability.
2Loss of information
If multiple sensors and methods are used for location fixing, then more data is available, but error radius increases without refinement
Solution Approach 1:
The patent segments the location determination process into distinct components: collecting proximity information from multiple devices, forming subgroups based on this information, and then refining the location within these subgroups. This segmentation allows the system to manage the complexity of multiple data sources while systematically reducing error radius through iterative refinement processes.
Solution Approach 2:
The system implements feedback mechanisms where location data from multiple devices is continuously collected and used to refine the target device's location. The proximity information from each device provides feedback that helps adjust and narrow down the possible location, reducing the error radius progressively until an accurate location is determined.
Data Source
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AI summary
A method of increasing accuracy of location determination of mobile devices (101) within a location-based subgroup starts with server receiving location data and proximity information from each of the mobile devices (101, 3016, 3025, 3026). Location data received from first mobile device (101) includes first mobile device's fixed location. Proximity information received from first mobile device includes an identification of mobile devices (3016, 3025, 3026) within a proximity sensitivity radius (406,211) of first mobile device's location. Server forms subgroup of mobile devices based on proximity information from each of the mobile devices (101, 3016, 3025, 3026). Subgroup may include first mobile device (101) and mobile devices (3016, 3025, 3026) that have provided proximity information that identifies first mobile device (101) are being within the proximity sensitivity radiuses (402, 403, 404) of the mobile devices (3016, 3025, 3026), respectively. Server may then refine the fixed location of the first mobile device (101), which includes identifying an intersection (410) of the proximity sensor sensitivity (211, 402, 403, 404) of each of the mobile devices (101, 3016, 3025, 3026) that are in subgroup.