Emitter Proximity Identification Using Electronic Signature Maps
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Solution Overview
Problem
Conventional Direction Finding (DF) and Geolocation (GEO) methods require special antennas, close-tolerance receiver components, and extensive processing resources, making it difficult and costly for moving platforms to effectively identify, track, and geolocate radio frequency (RF) emitters due to limitations in space and precision, as well as complex data flow and throughput requirements.
Innovation Solution
The Emitter Proximity Identification (EPI) methodology uses Electronic Signature Maps (ESM) with Emitter Closeness Measures (ECMs) such as Rate of Received Signal Strength Change (RRSSC) and Rate of Bearing Change (RBC) to quickly and efficiently identify and geolocate RF emitters, eliminating the need for special antennas and extensive processing resources, and allowing for real-time threat assessment and location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional DF and GEO methods are used, then measurement precision is improved, but device complexity and processing resources required increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for emitter detection (signal strength and bearing measurements) from the complex DF/GEO processing chain. By taking out just the proximity detection function and eliminating the need for special antennas, coherent sampling, and complex data processing, the system achieves the core goal of emitter identification with minimal complexity.
Solution Approach 2:
The patent replaces expensive, complex DF/GEO systems with simple, inexpensive measurements using standard antennas. The system uses basic signal strength and bearing data that can be collected with ordinary receivers, eliminating the need for specialized equipment while maintaining effective emitter detection capability.
2Measurement precision
If conventional DF and GEO methods are used, then measurement precision is improved, but processing resources and time required increase significantly
Solution Approach 1:
The patent extracts only the essential proximity detection function from the complex DF/GEO processing chain. By taking out just the necessary signal strength and bearing measurements and eliminating coherent sampling, FFT processing, and complex data analysis, the system achieves fast emitter detection with minimal processing resources.
Solution Approach 2:
The patent applies partial action by performing only the minimum necessary measurements (signal strength and bearing) rather than complete DF/GEO analysis. This partial approach provides sufficient emitter detection capability while dramatically reducing processing time and resource requirements.
3Measurement precision
If moving platform deploys special antennas for DF/GEO, then measurement precision is improved, but space constraints are violated
Solution Approach 1:
The patent extracts the essential emitter detection capability from the space-intensive DF/GEO system. By removing the requirement for special antennas and spatially displaced antenna arrays, the system achieves emitter proximity identification using minimal hardware space on the moving platform.
Solution Approach 2:
The patent replaces expensive, space-consuming special antennas with simple, space-efficient standard antennas. The system achieves effective emitter detection using minimal hardware footprint, making it feasible for mobile platforms with strict space constraints.
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 enables robust and efficient identification, tracking, and geolocation of RF emitters, reducing memory storage needs by over 10,000 times compared to direct power spectral density recording, and effectively mitigates ambiguities and multipath effects, allowing for real-time threat assessment and location determination without the need for high-precision components or extensive processing resources.
Implementation Method 1
detect a radio frequency energy emission... determine whether the new ECM should be considered a threat... geo-locate a position of an emitter represented by the new ECM
Data Source
AI summary
Systems and methods for monitoring and classifying RF emissions in the field include storing an electronic signature map (ESM) of a selected geographic area, where the electronic signature map includes previously detected emitter closeness measures (ECMs) in the selected geographic area. The ECMs are representative of detected sources of radio frequency energy. A RF energy emission is detected, a new ECM for that RF energy emission is created, and that the new ECM is compared with the ECMs in the ESM. That comparison may help to determine whether the RF energy emission should be considered a threat.