Wireless Positioning Using Relevance Indicators to Reduce Computational Load
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Solution Overview
Problem
Existing positioning techniques in wireless communication environments face high computational loads, which can lead to increased battery drain and reduced accuracy, due to the need to process data from multiple sample points.
Innovation Solution
A method that reduces computational load by maintaining a data model with relevance indicators to determine a subset of relevant sample points based on observations, allowing for more efficient location estimation and potentially improved accuracy or security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sample points are processed for positioning, then positioning accuracy is improved, but computational load increases
Solution Approach 1:
The patent divides the complete set of sample points into multiple subsets, where each subset contains only the relevant sample points needed for positioning at a given location. This segmentation allows the system to process only necessary data portions rather than all available sample points, thereby reducing computational load while maintaining positioning accuracy through selective processing of relevant subsets.
Solution Approach 2:
The patent applies local quality by creating location-specific subsets of sample points tailored to each positioning scenario. Each subset contains sample points that are locally relevant to the target object's position, ensuring that processing resources are concentrated on the most pertinent data rather than uniformly processing all sample points across the entire environment.
2Measurement precision
If all sample points are processed for positioning, then positioning accuracy is improved, but battery drain increases
Solution Approach 1:
By segmenting the sample points into location-specific subsets, the patent reduces the total number of calculations required for positioning. This reduction in computational operations directly decreases energy consumption, thereby reducing battery drain while still achieving accurate positioning through processing of the relevant subset of sample points.
3Device complexity
If a reduced set of relevant sample points is used, then computational load is reduced, but positioning accuracy may deteriorate
Solution Approach 1:
The patent ensures that the reduced set of sample points maintains positioning accuracy by making each subset locally optimized for its specific location. Each subset contains precisely the sample points that are relevant to that location, ensuring that no critical data is omitted while still reducing the overall computational burden compared to processing all sample points universally.
Solution Approach 2:
The patent performs preliminary classification and organization of sample points into location-specific subsets before the actual positioning calculation. This preliminary action ensures that when positioning is performed, the system already has pre-organized, location-appropriate data ready for processing, eliminating the need for unnecessary calculations and preserving accuracy with reduced computational effort.
Data Source
AI summary
A technique for positioning a target object in a wireless communication environment. A data model models several sample points. Each sample point includes a location and a set of expected signal values therein. A set of relevance indicators indicates one or more sets of relevant sample points, which are subsets of the sample points in the data model. Signal values are observed at the target object's location. Based on the signal value observations and the set of relevance indicators, a current set of relevant sample points is determined and used, along with the signal value observations, to estimate the target object's location. Computational burden is reduced because sample points not included in the current set of relevant sample points can be omitted from calculations.