Geolocating RF Emitters Using LOB Clustering
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Solution Overview
Problem
Conventional geolocation algorithms for radio frequency emitters face challenges with computational complexity and accuracy, particularly as the number of lines of bearing (LOBs) increases, leading to exponential runtime and erroneous results due to random variance being mistaken for cluster quality.
Innovation Solution
A computer-implemented method that iteratively selects the most LOB-dense cluster as the best cluster, reducing the number of clusters to evaluate and avoiding quality scoring, while also using a Quick-Select approach to take a representative sampling of LOBs to improve processing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional geolocation algorithms process all clusters of lines of bearing to improve accuracy, then measurement precision improves, but computational complexity increases exponentially
Solution Approach 1:
The patent extracts and evaluates only the most promising cluster (the one with the greatest number of LOBs) rather than processing all clusters. This selective approach removes unnecessary computational burden while maintaining geolocation accuracy by focusing resources on the most informative data subset.
Solution Approach 2:
The patent segments the set of LOBs into multiple clusters based on spatial intersections, then further segments these clusters by evaluating only the most populous one. This hierarchical segmentation allows the system to manage computational complexity by dividing the problem into manageable parts and focusing on the most significant segment.
2Measurement precision
If conventional algorithms evaluate all clusters to ensure accuracy, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent extracts only the most populous cluster for detailed evaluation, eliminating the time-consuming step of assessing all clusters. This extraction strategy maintains accuracy by focusing on the cluster most likely to contain the emitter while dramatically reducing processing time.
Solution Approach 2:
The patent performs preliminary grouping of LOBs into clusters based on spatial intersections before selecting the most populous cluster for evaluation. This preliminary organization allows the system to quickly identify the most promising cluster without having to fully process all possible clusters, thereby reducing overall processing time.
3Measurement precision
If the algorithm uses quality scoring to identify the best cluster, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent replaces complex quality scoring mechanisms with a simple count-based metric (number of LOBs per cluster). This simplified approach uses a 'cheap' metric that is computationally inexpensive to calculate while still effectively identifying the most promising cluster, thereby reducing algorithmic complexity without sacrificing selection accuracy.
4Measurement precision
If the algorithm processes a large number of LOBs to improve geolocation precision, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent extracts the essential information needed for geolocation by focusing only on the most populous cluster rather than processing all LOBs from all clusters. This extraction maintains measurement precision by concentrating on the most informative data while significantly improving processing efficiency.
Solution Approach 2:
The patent applies partial action by processing only a subset of the available data (the most populous cluster) rather than all possible clusters. This partial processing approach is sufficient to achieve accurate geolocation results while dramatically improving productivity compared to exhaustive processing of all clusters.
Data Source
AI summary
Geolocating one or more emitters includes obtaining a set of lines of bearing (LOBs) indicative of location(s) of emitter(s), determining intersections of LOBs of the set and generating clusters informed by those intersections, assigning the LOBs of the set to cluster(s) based on proximity, identifying a cluster having the greatest number of assigned LOBs from the set; determining an emitter location area based on a best point estimate for the cluster, and indicating a location of an emitter as the emitter location area. Additional emitters can be located by removing from the set of LOBs those LOBs assigned to the identified cluster, and repeating aforementioned aspects. Initially, the set of LOBs can be selected from a larger collection as a representative subset thereof.