Positioning Database Management via Zone-Based Signal Processing
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Solution Overview
Problem
Existing positioning systems face challenges in efficiently processing large volumes of data from multiple user devices, leading to delays in updating electromagnetic signal source positions, especially in areas with significant gaps in existing databases, such as indoor zones without satellite coverage.
Innovation Solution
A method of managing a database by receiving signal data from user devices, associating electromagnetic signal sources with geographical regions, selecting a subset for prioritized processing, and updating the database with computed position estimates, allowing for real-time or timely updates in regions with poor coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from all user devices is processed in batches on a periodic basis, then the system can manage the vast amount of data, but the updating of the master database is slow and delays occur in regions with database gaps
Solution Approach 1:
The patent divides the database update process into segments by identifying and prioritizing void zones (regions with significant database gaps) versus already-covered regions. User device data is routed to different processing queues based on whether it originates from or relates to void zones, enabling selective real-time processing for critical areas while maintaining batch processing for stable regions.
Solution Approach 2:
The system applies different processing qualities to different geographical regions. Void zones receive high-priority real-time processing with detailed analysis, while regions with existing coverage receive standard batch processing. This local differentiation optimizes overall system performance by concentrating resources where they are most needed.
2Loss of energy
If the master database is updated slowly using batch processing, then system resources are conserved, but positioning accuracy deteriorates in areas with significant database gaps
Solution Approach 1:
The processing system is segmented into real-time and batch processing modes. Real-time processing is activated specifically for void zone data to improve positioning accuracy in those regions, while batch processing continues for other areas to conserve resources. This segmentation allows the system to optimize accuracy where needed without wasting resources everywhere.
Solution Approach 2:
The system dynamically changes processing parameters based on database coverage quality. When a region is identified as a void zone, the processing mode parameter switches from batch to real-time, increasing computational intensity and resource allocation for that specific region, thereby improving position estimation accuracy dynamically.
3Measurement precision
If detailed data is collected from all user devices, then more accurate position estimation can be achieved, but the volume of data to be processed increases vastly
Solution Approach 1:
The system extracts and isolates data from void zones for separate real-time processing, removing it from the general batch processing queue. This extraction allows detailed data collection to continue for accuracy, while the extracted void zone data receives focused processing attention, preventing the entire system from being overwhelmed by total data volume.
Solution Approach 2:
Different data processing intensities are applied to different regions. Data from void zones undergoes intensive real-time processing with full detail, while data from covered regions uses standard processing. This local quality differentiation maintains high accuracy where needed while reducing overall computational burden.
4Measurement precision
If an initial survey is required before positioning functions in a new zone, then positioning accuracy can be ensured, but users cannot use the system in areas with database gaps until survey is complete
Solution Approach 1:
The system performs preliminary identification of void zones and prepares real-time processing queues in advance. When user devices enter these identified zones, the system immediately activates real-time processing without requiring a complete initial survey, allowing positioning functionality to become available progressively as devices provide data, rather than waiting for full survey completion.
Solution Approach 2:
The system dynamically adapts its processing mode based on real-time detection of database gaps. When devices enter regions with significant gaps, the system automatically switches to real-time processing for those regions, enabling positioning functionality to emerge dynamically without pre-survey requirements, making the system adaptable to new zones as they are explored.
Data Source
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AI summary
There is disclosed a method of managing a database of positioning data, the positioning data including electromagnetic signal source data for use by a positioning system, and the method comprising: receiving signal data relating to signals received from a plurality of electromagnetic signal sources; associating an appropriate one of a plurality of zone identifiers with each of the electromagnetic signal sources, each zone identifier being associated with a respective geographical zone; selecting a subset of the plurality of electromagnetic signal sources in dependence on their associated zone identifiers; processing the signal data relating to the subset of the plurality of electromagnetic signal sources to compute position estimates of the electromagnetic signal sources; and updating the database of electromagnetic signal source data in dependence on the computed position estimates.