RF Pulse Correlator for Maritime Emitter Tracking
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Solution Overview
Problem
Existing radar systems and RF pulse deinterleaving methods struggle to efficiently process and associate RF pulses in real-time, leading to data drops and reduced accuracy in identifying and tracking RF emitters, particularly in congested maritime environments.
Innovation Solution
An RF pulse correlator system that includes a track database, antenna, receiver, tracker, and processor, which sorts RF pulses by time, associates them with existing tracks or creates new ones using kinematic models and scoring algorithms, and leverages a Kalman filter to improve geolocation data and reduce data drops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional RF pulse deinterleaving methods are used, then device complexity is reduced, but productivity decreases due to data drops and inability to keep up with data processing rates
Solution Approach 1:
The correlator processes RF pulses by segmenting them into discrete pulses with individual parameters (time of intercept, pulse width, frequency, geolocation). Each pulse is evaluated independently through a series of gating tests, allowing the system to handle high data rates without dropping data while maintaining manageable processing complexity through modular evaluation steps.
Solution Approach 2:
The system performs preliminary gating tests (time-of-intercept gating, pulse width gating, frequency gating, geolocation gating) before full correlation analysis. This preliminary filtering eliminates obviously incompatible pulses early in the process, reducing the computational burden of subsequent detailed analysis and enabling the system to keep up with high data processing rates.
2Measurement precision
If traditional pulse association methods are used, then device complexity is lower, but measurement precision decreases due to reduced accuracy in RF emitter identification
Solution Approach 1:
The correlator implements feedback through track management, where previously processed pulses establish tracks that inform the evaluation of subsequent pulses. The system uses track establishment, track update, and track validation feedback loops to continuously refine emitter identification accuracy while managing system complexity through structured feedback mechanisms.
Solution Approach 2:
The system evaluates pulses across multiple dimensions simultaneously (time of intercept, pulse width, frequency, geolocation coordinates). By adding these additional evaluation dimensions beyond simple pulse detection, the system achieves higher emitter identification accuracy while managing complexity through systematic multi-dimensional gating tests.
3Productivity
If RF pulses are processed quickly, then productivity increases, but loss of information increases due to data drops from unable to process spikes
Solution Approach 1:
The correlator maintains continuous processing capability by evaluating each incoming RF pulse through the complete sequence of gating tests and association procedures without interruption. This continuous action ensures no pulses are dropped even during high-data-rate periods, preventing information loss while maintaining high productivity through uninterrupted processing flow.
Data Source
AI summary
An RF pulse correlator comprising a track database, an antenna, a receiver, and a processor. The track database is configured to store established tracks of RF emissions. The antenna and receiver are configured to receive RF pulses. The tracker is configured to generate improved geolocation data for every received RF pulse based on kinematics of the received RF pulses. The processor is communicatively coupled to the database, the receiver, and the tracker. The processor is configured to associate each received RF pulse with an existing track in the track database or to create a new track.