Multi-Level Probability Map for Mobile Positioning
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Solution Overview
Problem
Existing location tracking systems for mobile devices face challenges in urban areas due to intermittent satellite signal reception and high computational and power demands, especially with limited data transfer and processing capacities, which can lead to positioning delays and inaccuracies.
Innovation Solution
A multi-level probability map model structure is constructed using satellite and cellular data from multiple devices, allowing for real-time positioning by traversing through candidate paths based on environmental data, with data grouping and merging to enhance accuracy and reduce computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS satellite signal reception is used for location determination, then positioning accuracy is improved, but signal reception reliability deteriorates in urban areas due to landscape obstructions
Solution Approach 1:
The positioning system is segmented into multiple independent data sources: GPS satellite signals, cellular network data, and WiFi environment data. Each data source operates independently and contributes to the overall positioning result, allowing the system to maintain functionality even when one source is obstructed or unavailable in urban environments
Solution Approach 2:
The patent combines multiple positioning data sources (GPS, cellular, WiFi) and environment data into a unified probabilistic model. By merging these diverse data sources and processing them together through the multi-level probability map, the system achieves reliable positioning in urban areas where GPS alone would fail due to building obstructions
2Measurement precision
If detailed multi-level probability map model structure is constructed and traversed for positioning, then positioning accuracy is improved, but computational load and processing time increase
Solution Approach 1:
The probability map model is segmented into multiple hierarchical levels, where each level represents a different resolution of spatial probability distribution. This segmentation allows the system to process data at appropriate granularities, reducing the overall computational burden while maintaining positioning accuracy
Solution Approach 2:
The system performs partial traversal of the multi-level probability map structure by selectively processing only the necessary levels and candidate paths based on available data and positioning requirements. This partial action approach reduces computational load by avoiding unnecessary processing of the entire model structure while still achieving accurate positioning results
3Measurement precision
If real-time positioning calculations are performed continuously, then positioning accuracy is improved, but power consumption increases for mobile devices
Solution Approach 1:
The positioning system uses periodic action by updating position estimates at appropriate intervals rather than continuously calculating in real-time. The multi-level probability map structure allows the system to maintain accurate position estimates by periodically processing environment data and updating the probabilistic model, thereby reducing power consumption while preserving positioning accuracy
Solution Approach 2:
The system performs preliminary action by pre-computing and maintaining the multi-level probability map model structure in advance. This preliminary model construction enables faster real-time positioning updates by reducing the computational complexity of actual positioning calculations to simple probability comparisons and traversals of the pre-built structure
Data Source
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AI summary
Electronic arrangement for positioning a mobile device, including a mapping entity configured to obtain positioning data, from a plurality of measuring mobile devices present in an area of interest, establish and maintain, based on the obtained data, a multi-level probability map model structure for the area, wherein each higher level covers the area with lower spatial resolution by a plurality of determined sub- areas, each having a unitary character, and each lower level correspondingly co¬ vers, for each the sub-area of the adjacent upper level, a plurality of determined sub-areas thereof with higher spatial resolution, the lowest level determining the highest spatial resolution location elements of the model, optionally coordinates, a locating entity configured to obtain data provided by the mobile device, determine an estimate of the position of the mobile device by traversing through at least a portion of a number of vertical candidate paths of the multi-level probability model structure.