N-Dimensional Enrichment for Radar Pulse Deinterleaving
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Solution Overview
Problem
Current radar signal processing algorithms face challenges in efficiently deinterleaving nested pulse trains with high pulse density and agility, leading to increased computational complexity and inability to analyze data over long durations due to quadratic or higher complexity, which is not feasible for real-time processing in dense electromagnetic environments.
Innovation Solution
A method of non-supervised deinterleaving using N-dimensional enrichment, involving constructing histograms for multiple parameters, extracting modes, forming groups of interest, enriching pulse descriptions, and cross-classifying modes to reorganize pulses by similarity, reducing computational complexity to linear and enabling analysis of high pulse density over long times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deinterleaving algorithms are used to process nested pulse trains, then deinterleaving capability is achieved, but computational complexity increases quadratically or higher, making real-time processing infeasible
Solution Approach 1:
The algorithm segments the pulse train processing into independent histogram construction for each parameter and mode extraction steps, allowing parallel processing of multiple parameters (frequency, time, amplitude, etc.) without quadratic complexity growth. Each parameter is processed separately through histogram construction and mode extraction, then combined through cross-classification.
Solution Approach 2:
The patent introduces N-dimensional histogram enrichment by adding multiple parameter dimensions (frequency, time, amplitude, pulse width, etc.) to the traditional single-parameter processing. This dimensional expansion allows the algorithm to separate pulses based on multiple characteristics simultaneously, achieving linear complexity while maintaining deinterleaving effectiveness.
2Measurement precision
If sensitivity of ESM sensors is increased to detect more pulses, then pulse detection capability improves, but pulse density increases leading to higher computational burden
Solution Approach 1:
The histogram-based approach allows the system to automatically adapt to varying pulse densities without manual parameter adjustment. The histogram construction and mode extraction process self-adjusts to the input data characteristics, maintaining linear complexity regardless of pulse density increases from higher sensor sensitivity.
Solution Approach 2:
The patent changes the processing parameters from individual pulse analysis to histogram-based statistical representation. By transforming the data into histogram bins and extracting modes, the system handles high-density pulses efficiently, as the histogram aggregation reduces the computational burden proportional to the number of bins rather than the number of pulses.
3Measurement precision
If analysis duration is extended to reveal statistical discriminants, then detection accuracy improves, but data volume increases making processing infeasible
Solution Approach 1:
The patent performs preliminary histogram construction and mode extraction on incoming pulse data before full analysis. This preliminary processing identifies dominant pulse patterns and parameters early, allowing the system to focus subsequent analysis on relevant modes only, thereby maintaining linear complexity even when analyzing extended time periods for statistical discriminants.
Solution Approach 2:
The algorithm discards redundant pulse information by representing data through histogram bins and extracted modes rather than individual pulse details. This compression discards fine-grained temporal information that can be recovered through the histogram representation, enabling long-duration analysis with constant memory usage and linear processing complexity.
Data Source
AI summary
A method of non-supervised deinterleaving of pulse trains comprises at least one N-dimensional enrichment step, N being an integer greater than 1.