Polysweep Engine Radar Signal Deinterleaving
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Solution Overview
Problem
Existing systems face challenges in effectively deinterleaving multiple radar signals to identify known and unknown signal sources, particularly due to noise limitations, unknown direction of arrival, and complex radar systems.
Innovation Solution
A method and system utilizing a polysweep engine that compares radar pulse data against a library of known radar models, extracting matching pulse sequences and leaving residue signals for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple radar signals are intercepted and analyzed, then electronic intelligence is gathered, but the complexity of deinterleaving and identifying individual signal sources increases
Solution Approach 1:
The patent segments the complex task of deinterleaving radar signals by dividing the signal processing into distinct stages: initial pulse classification, candidate signal identification, and verification through parameter comparison. This segmentation reduces the overall complexity by breaking down the monolithic deinterleaving problem into manageable sub-tasks that can be processed sequentially
Solution Approach 2:
The patent applies preliminary action by pre-classifying pulses into candidate signals before full deinterleaving occurs. Pulses are initially grouped based on basic parameters (frequency, pulse width, time of arrival), and only these pre-processed candidate signals undergo the computationally intensive deinterleaving process. This preliminary classification reduces the search space and simplifies the main deinterleaving operation
2Productivity
If radar signal analysis is performed manually, then detailed analysis is possible, but the time and effort required increases significantly
Solution Approach 1:
The patent implements self-service by creating an automated system that performs deinterleaving and signal identification without requiring continuous manual intervention. The system automatically compares signal parameters, identifies candidate signals, verifies matches against known radar signatures, and outputs results. This automation enables the system to serve itself in the analysis process, dramatically increasing throughput while reducing the time investment required per signal
Solution Approach 2:
The patent substitutes mechanical/manual analysis with an automated computational system. Instead of analysts manually examining radar signals, the system uses computer-based algorithms to perform parameter comparison, pattern recognition, and signal identification. This replacement of manual mechanical analysis with automated electronic processing significantly increases productivity while reducing analysis time
3Measurement precision
If known radar models are compared against intercepted signals, then identification accuracy improves, but the processing complexity increases
Solution Approach 1:
The patent applies local quality by focusing model comparison only on specific, relevant parameters rather than analyzing entire signal waveforms. The system compares localized features such as pulse repetition intervals, frequency offsets, and pulse width variations against known radar model characteristics. This localized parameter-based approach maintains high identification accuracy while reducing the overall complexity of the comparison process
Solution Approach 2:
The patent utilizes parameter changes by transforming the radar signal data into a standardized parameter space that facilitates comparison with known models. intercepted signals are characterized by extracting key parameters (frequency, pulse width, time of arrival, repetition intervals), and these parameters are then compared against the parameter sets of known radar models. This parameter-based transformation simplifies the comparison complexity while maintaining precision
Data Source
AI summary
Radar intercept hardware is capable of receiving and storing multiple radar signals in any context including air, land, sea or space domains as radar pulse data. However, without a priori knowledge of all of the radar emitters captured by such radar interceptors, the information or intelligence that has been gathered is of little use until such radar signals are separated or deinterleaved from the radar pulse data. The present invention is directed to model-based radar signal deinterleaver systems and methods. The characteristics of known radar models, particularly pulse repetition intervals, pulse width, time of arrival and radio frequency are used by the various embodiments of the system and method of the present invention to extract out the known signals and leave the unknown or residue signals for adjudication by a subject matter expert.