Radar Pulse Train Separation Using Histogram-Based Frequency Search
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Solution Overview
Problem
Existing methods for separating radar pulse trains are inefficient when pulse repetition periods are close or fluctuate randomly, leading to difficulties in distinguishing between nested radar signals and increasing computational costs due to the need for high-frequency sampling in discrete Fourier transforms.
Innovation Solution
A method that determines a search range for pulse repetition frequencies using a histogram of arrival time differences, calculates a discrete Fourier transform within this range, and thresholds the results to separate pulse trains efficiently, reducing computational complexity while maintaining resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the discrete Fourier transform is calculated over the entire frequency range to identify pulse repetition frequencies, then the separation accuracy of nested radar signals is improved, but the computational cost increases significantly
Solution Approach 1:
The frequency range is segmented into multiple sub-ranges based on histogram analysis of arrival time differences. Instead of calculating the Fourier transform over the entire frequency range, the method divides the search space into smaller segments where transforms are calculated independently, reducing the overall computational burden while maintaining detection accuracy for pulse repetition frequencies
Solution Approach 2:
A histogram of arrival time differences is constructed before performing the Fourier transform calculation. This preliminary analysis identifies the most likely frequency ranges where pulse repetition frequencies are located, allowing the subsequent Fourier transform to be focused only on these relevant segments rather than computing across the entire frequency spectrum
2Reliability
If the histogram classes are widened to reduce sensitivity to PRI fluctuations, then the robustness to jitter is improved, but the ability to separate locally overlapping radar signals is lost
Solution Approach 1:
The histogram is divided into multiple fine-grained classes rather than using few wide classes. This segmentation allows the system to maintain sensitivity to small PRI variations needed for separating overlapping signals, while the subsequent Fourier transform provides the robustness to handle jitter by concentrating energy at the true pulse repetition frequencies
Solution Approach 2:
The Fourier transform acts as an intermediary between the histogram analysis and the final pulse train separation. The histogram provides initial frequency estimates, the Fourier transform refines these estimates and provides robustness to jitter through its concentration property, and the result is used to separate the overlapping pulse trains with high precision
Data Source
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AI summary
The method involves constructing a histogram of arrival time differences comprising classes, and determining a search range bound by terminals. Average arrival time differences are determined as an average value of the range. Setpoint frequency equal to reverse of the average arrival time differences is defined. Discrete Fourier transformation is calculated on the range based on a specific equation comprising frequency, number of pulses in radar signal and pulse arrival instants. Pulse trains are separated from pulse repetition frequency determined by the transformation.