Radar Emitter Model Learning via Finite State Machine
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Solution Overview
Problem
Radar warning systems struggle to detect and respond to unknown threat emitters due to the proliferation of digitally programmable radar and communication hardware, leading to potential undetected threats and unsuccessful military missions.
Innovation Solution
A system that uses natural language processing techniques, specifically finite state machines and machine learning, to learn and characterize the behavior of unknown threat emitters by analyzing pulse sequences, allowing for the hierarchical building of threat radar models and estimation of emitter intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If radar warning systems use traditional signal characterization and table lookup methods, then known emitters can be identified, but unknown emitters cannot be detected
Solution Approach 1:
The system performs self-learning by automatically analyzing observed pulse sequences to build threat radar models without human intervention. The machine learning algorithms process received signals, extract features, and generate emitter models autonomously, enabling the system to adapt to new threats dynamically
Solution Approach 2:
The system transforms fixed table lookup parameters into dynamic learned parameters. By using machine learning to extract features from pulse sequences and build models, the system adapts its detection parameters based on observed data, allowing it to recognize unknown emitters that would not be in a static database
2Adaptability or versatility
If the system learns unknown emitters in real-time, then detection capability improves, but processing time and computational resources increase
Solution Approach 1:
The learning process is segmented into hierarchical levels: pulse sequence analysis, feature extraction, model building, and validation. This segmentation allows the system to process information in manageable stages, improving computational efficiency while maintaining real-time capability
Solution Approach 2:
The system uses partial learning approaches where it analyzes only the most significant features from pulse sequences rather than processing every detail. This selective learning enables real-time adaptation without requiring complete analysis of all signal parameters
3Measurement precision
If the system uses complex machine learning models to characterize unknown emitters, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The complex machine learning system is segmented into modular components: signal processing module, feature extraction module, model building module, and validation module. This modular architecture reduces overall system complexity by allowing each component to be developed and optimized independently
Solution Approach 2:
The system uses universal machine learning algorithms that can handle multiple types of radar signals and emitter types through a single framework. This multi-functionality reduces complexity compared to having separate specialized systems for different emitter types
Data Source
AI summary
Methods and apparatus to receive radar pulses, process the received pulses using weighted finite state machine to learn a model of an unknown emitter generating the received radar pulses, and estimate a state/function of the unknown emitter based on the received radar pulses using the learned model, and predict the next state/function of the unknown emitter based on the received radar pulses and applying maximum likelihood estimation.