Crowdsourced Base Station Almanac Calibration for Positioning Accuracy
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Solution Overview
Problem
Current terrestrial positioning systems face challenges with cumbersome and resource-intensive calibration procedures, which are further exacerbated by changes due to network maintenance and reconfiguration, leading to reduced deployment and utilization interest.
Innovation Solution
A method involving the aggregation of measurement sets from mobile stations to update Base Station Almanac data, deriving spatially variable Forward Link Calibration values, and using these values to refine location estimates, thereby facilitating efficient calibration and optimization of positioning system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration procedures are performed to improve positioning accuracy, then positioning accuracy is improved, but resource consumption and time required for calibration increase significantly
Solution Approach 1:
The system enables self-service calibration by allowing mobile stations to autonomously perform measurements and contribute to calibration data collection without requiring manual intervention or specialized field operations. The calibration process becomes automated through network-based aggregation of measurement sets from multiple mobile stations.
Solution Approach 2:
The patent replaces manual field-based calibration operations with an automated electronic system that collects, aggregates, and processes measurement data through network infrastructure. The mechanical/physical field work is substituted with digital signal processing and data aggregation operations.
2Measurement precision
If field data collection is performed for calibration to improve positioning performance, then positioning accuracy is improved, but operational complexity and resource intensity increase
Solution Approach 1:
The network infrastructure acts as an intermediary between mobile stations and the calibration process. Instead of requiring direct manual calibration operations, mobile stations simply perform measurements and transmit data through the network, which then aggregates and processes the data automatically.
Solution Approach 2:
The system uses virtual copies of calibration data stored in the Base Station Almanac (BSA) rather than requiring physical recalibration operations. The BSA stores calibration information that can be updated through data aggregation without manual field work.
3Reliability
If calibration is performed frequently to account for network changes, then positioning accuracy is maintained, but resource consumption increases
Solution Approach 1:
The system continuously aggregates measurement sets from mobile stations and uses this feedback to update calibration data in the Base Station Almanac. This feedback mechanism allows the system to automatically adapt to network changes without requiring manual recalibration operations.
Solution Approach 2:
The calibration process becomes continuous through ongoing aggregation of measurement data from mobile stations rather than discrete periodic recalibration events. The useful action of calibration continues automatically as part of normal network operation.
Data Source
AI summary
Embodiments disclosed aggregate a plurality of crowdsourced measurement sets for antennas received from a plurality of Mobile Stations (MS) with a Base Station Almanac (BSA), based on a measurement location estimate and a measurement location uncertainty estimate associated with each measurement set. A map comprising a plurality of map layers may be obtained, where each map layer associates locations in the BSA with spatially variable Forward Link Calibration (FLC) values for the antenna derived from the updated BSA data, wherein each spatially variable FLC value is associated with a corresponding location in the updated BSA data. Map layers, which may also include multipath map and/or received signal strength layers, may be provided to MS′ as location assistance data.