Mobile Location Estimation via Geometric Median
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Solution Overview
Problem
Existing methods for locating a target mobile device in a crowd are inefficient and imprecise, relying on iterative and complex calculations that make it difficult for users to quickly and accurately determine the device's geographical location.
Innovation Solution
A method involving the collection of measurement data, determination of intersection points, and calculation of the geometrical median of these points to estimate the target mobile device's location, using a combination of distance estimates and spherical coordinate conversion to a cartesian coordinate system, with a dynamic queue mechanism to filter and validate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative methods (e.g., Conjugate Gradient) are used to find best fitting points, then measurement precision is improved, but calculation time and device complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-converting spherical coordinate measurements to Cartesian coordinates before processing, and by pre-establishing the geometric median calculation methodology. This allows the system to avoid complex iterative optimizations during real-time operation, instead using direct geometric calculations that are computationally simpler and faster.
Solution Approach 2:
The patent replaces the iterative mathematical optimization process (Conjugate Gradient method) with a direct geometric approach using circle intersections and geometric medians. This substitution eliminates the need for complex numerical iterations, reducing computational complexity and processing time while maintaining location estimation accuracy.
2Measurement precision
If iterative methods are used to locate target mobile, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces complex iterative mathematical computations with straightforward geometric operations (circle intersection and median calculation). This makes the system easier to operate and interpret, as the geometric median provides an intuitive representation of the target location that can be directly visualized on maps without requiring users to understand complex optimization algorithms.
Solution Approach 2:
The patent changes the mathematical parameters from iterative optimization variables to direct geometric elements (circle centers and radii). By formulating the problem in terms of geometric medians of circle intersections, the system provides more intuitive and easily interpretable results, improving ease of operation while maintaining precision.
3Measurement precision
If spherical coordinate systems are used for location estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by performing the spherical to Cartesian coordinate conversion in advance, before the main location estimation process. This pre-processing step simplifies subsequent calculations, as the system can then work with simpler Cartesian coordinates and direct geometric relationships rather than complex spherical trigonometry throughout the calculation process.
Data Source
AI summary
A solution for estimating the geographical location of a target device is presented. The solution comprises obtaining measurement data from individual locations, the measurement data comprising a distance estimate to the target device from an apparatus and the individual locations; adding measurement data to a measurement data queue; determining intersection points of circles having radiuses of measured distances and locations of the corresponding individual location as center points, when a given number of measurement data is present in the queue; determining intersection points closest to each other and determining estimated location of the target device as center point of a vector connecting the intersection points closest to each other.