Calibration Data Modification for Mobile Station Location
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Solution Overview
Problem
Current methods for generating calibration databases for mobile station location estimation are laborious, time-consuming, and prone to errors due to inaccurate ground truth measurements, especially during signal degradation or drop-out, which can lead to poor location estimates.
Innovation Solution
A method and system that enhance calibration data accuracy by using a location information database with latitude and longitude information to modify and interpolate data points, incorporating a street database for comparison and correction, and employing autonomous test and measurement equipment for efficient data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional calibration data collection methods are used, then location data can be obtained, but the process is laborious, time-consuming, and expensive
Solution Approach 1:
The system enables autonomous calibration data collection where the mobile station itself performs the measurement and reporting functions. The mobile station autonomously measures signal strengths from multiple base stations, determines its location, and reports calibration data without requiring external collection devices or manual intervention, thereby eliminating the laborious and time-consuming traditional collection process
Solution Approach 2:
The mobile station is designed to perform multiple functions: it serves as both the device being located and the calibration data collection device. The same mobile station that needs location services also performs the calibration measurements, signal strength recording, and data reporting, eliminating the need for separate dedicated collection equipment
2Measurement precision
If GPS receivers are used to collect ground truth data, then location measurements can be obtained, but signal degradation or drop-out occurs due to poor satellite visibility or high dilution of precision
Solution Approach 1:
The system uses base station signal strength measurements as an intermediary to determine location. Instead of directly relying on GPS satellites, the mobile station measures signal strengths from multiple base stations and uses these intermediate measurements to calculate its position, providing a reliable alternative when GPS signals are unavailable or degraded
Solution Approach 2:
The system changes the measurement parameter from GPS satellite-based position directly to base station signal strength-based position. By measuring signal strengths from multiple base stations and using these parameters to calculate location through triangulation or multilateration, the system achieves reliable location data without depending on GPS signal availability
3Productivity
If dead reckoning is used to estimate location during signal degradation, then location data can be collected, but location error increases and estimated position becomes erroneous
Solution Approach 1:
The system continuously monitors signal strength measurements from multiple base stations and uses this feedback to dynamically determine the mobile station's location. Rather than relying on dead reckoning estimates that drift over time, the system continuously updates the position calculation based on current signal measurements, maintaining high accuracy throughout the data collection process
4Measurement precision
If calibration data is collected without modification algorithms, then collection process remains simple, but data accuracy and integrity are compromised
Solution Approach 1:
The system employs data modification algorithms that use feedback from multiple signal strength measurements to correct and enhance calibration data. The algorithms process the raw measurements, identify and correct errors, and generate enhanced calibration data that maintains high accuracy while managing processing complexity through systematic approaches
Data Source
AI summary
A system and method modifies calibration data used to geo-locate a mobile station. Calibration data measured via a calibration data collection device may contain errors due to the physical limitations of the collection device and/or the collection process. Any data collection device may produce some degree of signal degradation or drop-out. Dead reckoning provides a remedy for signal drop-out, however, it often produces data results that may be unsatisfactory to perform an accurate location estimate. To ensure the integrity of the collected calibration data, a data modification and/or data replacement algorithm may be implemented to enhance the accuracy of the collected data. In addition, current collection procedures used to generate a calibration database may be laborious, time-consuming and expensive. Simplifying the test and measurement equipment needed, and the procedures for obtaining calibration data may save time and expenses.