ADS-B Geolocation Using RSSI Differences and Bayes Filtering
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Solution Overview
Problem
Current methods for geolocating assets on the Earth's surface using ADS-B signals are relatively inaccurate, with existing techniques providing location estimates with radii of about two miles and requiring additional information or complex calculations.
Innovation Solution
The method involves receiving ADS-B signals from multiple airborne aircraft, interpolating state information using Bayes filters, determining RSSI or Doppler Shift differences, and employing likelihood functions to estimate the asset's location, with a search unit identifying the most accurate position among the estimated locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing geolocation methods using ADS-B signals are employed, then the location can be determined with a circle of possibilities radius of about two miles, but the accuracy is relatively low and additional information or complex calculations are required
Solution Approach 1:
The patent transforms the geolocation approach by changing from using absolute position data to using relative position differences between multiple aircraft. This parameter transformation enables more accurate location estimation without requiring complex additional information, as the differential approach naturally cancels out common errors and uncertainties
Solution Approach 2:
The patent introduces an intermediary computational framework that processes ADS-B signals from multiple aircraft to derive relative position information. This intermediary processing layer transforms raw signal data into meaningful geometric relationships, achieving high accuracy while maintaining operational simplicity
2Measurement precision
If multiple ADS-B signals from different aircraft are received and processed, then geolocation accuracy is significantly improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the geolocation problem into independent pairwise comparisons between the asset and each aircraft. By breaking down the complex multi-aircraft positioning problem into simpler bilateral geometric relationships, the system achieves high accuracy while maintaining computational efficiency through modular processing
Solution Approach 2:
The patent utilizes more ADS-B signals than the minimum required for basic triangulation by incorporating data from multiple aircraft simultaneously. This excessive use of available signals strengthens the geometric configuration and improves accuracy through redundancy, while the segmented processing approach prevents computational overload
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves geolocation accuracy by refining the estimation process, reducing the uncertainty in asset positioning and providing a more precise determination of the asset's location.
Implementation Method 1
determining a Doppler shift in the received ADS-B signals
Implementation Method 2
determining differences in received signal strength indicator (RSSI) values (RSSI-difference values) of successive aircraft-specific ADS-B signals
Data Source
AI summary
A method of geolocating comprises: receiving wirelessly, at an asset located on the Earth's surface and from at least two airborne aircraft, ADS-B signals, respectively; interpolating, using a Bayes filter, at least some state information of the at least two airborne aircraft based on the ADS-B signals, respectively; determining differences in received signal strength indicator (RSSI) values (RSSI-difference values) of successive aircraft-specific ADS-B signals, respectively; estimating, using a likelihood function, locations of the asset based on the RSSI-difference values, the ADS-B signals and the interpolated state information, respectively, thereby producing a set of estimated locations; and searching amongst the set to find one of the estimated locations that is regarded as being most likely to most accurately describe an actual position of the asset.