Single-Ship Geolocation Cluster Analysis for Radar Emitter Location
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Solution Overview
Problem
Single-ship geolocation methods face challenges in accurately determining the position of a radar emitter due to significant sources of error and disparate results from multiple geolocation estimates, making it difficult to identify the best estimate for defensive actions.
Innovation Solution
A method that identifies clusters of geolocation data points in a coordinate system, selects the main cluster with the largest population, and computes average coordinate values to determine the optimal emitter location, which can be refined through sub-clustering and conversion to another coordinate system, providing a more accurate and reliable solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple geolocation estimates are generated from single-ship sensor data, then the quantity of available location information increases, but the accuracy and reliability of the determined emitter position deteriorates due to significant sources of error and disparate results
Solution Approach 1:
The patent segments the geolocation data into clusters based on spatial proximity and statistical characteristics. By dividing the data into distinct clusters, the system can identify the most reliable cluster (main cluster) and compute the optimal emitter location from that specific subset, thereby improving measurement precision while utilizing the quantity of available estimates.
Solution Approach 2:
The patent merges multiple geolocation estimates by computing the average coordinate values from the main cluster. This combining approach aggregates the information from multiple estimates into a single optimal location determination, leveraging the quantity of data to improve accuracy while filtering out erroneous estimates through the clustering process.
2Loss of time
If the last generated geolocation result is used, then the response time is minimized, but the accuracy deteriorates due to potential wild point solutions and failure to identify the best estimate
Solution Approach 1:
The patent performs preliminary clustering and analysis of geolocation estimates before final location determination. By pre-organizing the data into clusters and identifying the main cluster in advance, the system can quickly determine the optimal emitter location without delay, achieving both fast response time and high accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously analyzes geolocation estimates, identifies clusters, and determines optimal locations. This feedback loop allows the system to learn from the data patterns and consistently identify the most accurate emitter positions, improving accuracy while maintaining responsive update rates.
3Device complexity
If geolocation data is processed without clustering, then the processing complexity is reduced, but the reliability of the determined location deteriorates due to inclusion of wild point solutions
Solution Approach 1:
The patent segments the geolocation data into clusters, which organizes the processing into manageable groups. This segmentation approach, while adding some processing steps, significantly improves reliability by separating valid estimates from wild point solutions, and the modular nature of clustering makes the complexity manageable and systematic.
Solution Approach 2:
The patent introduces clustering as an intermediary step between raw geolocation estimates and final location determination. This intermediary process filters and organizes the data, improving reliability by eliminating wild point solutions, while the clustering algorithm itself provides a structured approach that manages processing complexity through well-defined computational steps.
Data Source
AI summary
Techniques are disclosed for selecting a closest to optimal radar/emitter location for single-ship applications. In accordance with some embodiments, given single-ship geolocation estimates are organized so that clusters of those estimates can be identified, wherein optimal solutions may be found in consecutive, adjacent segments of distance (bins) along each axis of given a coordinate system. Once the clusters are identified in each axis, an optimal cluster can be selected for each. To determine the closest answer to optimal, the coordinate data points in each of the optimal clusters can be averaged (or other sound mathematical process) for each axis in the coordinate system, so as to provide an optimal 3-D coordinate in the given coordinate system. In other embodiments, the optimal 3-D coordinate can be further used to establish an origin in a second coordinate system (e.g., for conversion from 3-D to 2-D coordinate system).