Geolocation Fidelity Assessment Using Ancillary Signal Parameters
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Solution Overview
Problem
Current geolocation determination methods rely solely on error ellipses and sigma values, which can be inadequate under conditions like undetected interferers, non-traditional signal shapes, signal movement, or sub-optimal receiver geometry, leading to inaccurate confidence assessments.
Innovation Solution
A method and system that process ancillary parameter values, such as signal characterization data and secondary measurements, using a weighted matrix to determine a confidence or fidelity value, incorporating parameters like Cross Ambiguity Function (CAF) roughness, CAF Signal-Noise Ratio, number of signal collectors, duty cycle, and demodulation processing quality, to enhance geolocation confidence assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geolocation determination uses only error ellipses and sigma values, then the system complexity is low, but the confidence assessment accuracy deteriorates under conditions like undetected interferers, non-traditional signal shapes, signal movement, or sub-optimal receiver geometry
Solution Approach 1:
The patent transitions from two-dimensional confidence assessment (error ellipses and sigma values) to a multi-dimensional evaluation framework by incorporating additional parameters such as signal-to-noise ratio, signal duration, receiver geometry metrics, and processing quality indicators. This dimensional expansion enables comprehensive confidence assessment across multiple aspects of geolocation determination, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent creates a composite confidence metric by combining multiple different types of parameters (signal quality metrics, geometric parameters, temporal parameters, and processing parameters) into a unified confidence value. This composite approach integrates diverse data sources and measurement aspects, achieving high accuracy confidence assessment while managing system complexity through structured integration.
2Reliability
If additional ancillary parameters are processed to determine confidence value, then the confidence measure comprehensiveness improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary processing of ancillary parameters during the geolocation determination process itself, rather than separately afterward. By incorporating these parameters into the existing processing workflow and utilizing already-computed data from signal collection and analysis, the system achieves comprehensive confidence assessment without significant additional processing time overhead.
Solution Approach 2:
The patent merges the computation of confidence values with the existing geolocation determination process by integrating ancillary parameter processing into the same computational framework. This consolidation allows multiple functions (signal analysis, geometry calculation, confidence assessment) to share processing resources and execution time, reducing overall processing time while maintaining comprehensive confidence evaluation.
Data Source
AI summary
In a geolocation system, ancillary parameter values, i.e., those produced during the geolocation process but not directly related to the calculation of the geolocation, are recorded and stored. These “ancillary” parameter values include signal characterization data and secondary measurements produced in the calculation of the geolocation. These parameter values are processed, in one approach, with respect to a weighted matrix in order to determine a confidence or fidelity value of the determined geolocation. Confidence is an indication of the likelihood that the geolocation result produced is in fact the desired target and that the identified location has a reasonable degree of quality so as to be practical or accurate.