Pulse Train Deinterleaving Using DTOA Histograms and Phase Grouping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing pulse deinterlacing techniques struggle with high agility radar signals, interference from propagation environments, and sensor limitations, leading to inaccurate pulse separation and increased complexity, especially in dense radar environments.
Innovation Solution
A method that utilizes pattern repetition periods and phases by constructing a histogram of arrival time differences, grouping pulses into trains with constant periodicity and phase, and applying proximity constraints on primary parameters to achieve accurate pulse separation with reduced computational and memory costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classification techniques based on primary parameters are used for deinterlacing, then pulses can be distributed into clusters, but measurement accuracy deteriorates due to disturbances from reflections and sensor limitations
Solution Approach 1:
The patent introduces an intermediary classification based on pulse pairs and time differences of arrival (DTOA) as a mediator between raw pulse measurements and final deinterlacing. Instead of directly classifying pulses using unreliable primary parameters, the method uses pulse pairs to compute DTOA values, which serve as more robust intermediate features for classification and clustering, thereby resolving the contradiction between separation capability and measurement accuracy
Solution Approach 2:
The patent transforms the classification approach by changing from direct use of primary parameters (amplitude, duration, frequency) to using derived parameters based on time differences of arrival between pulse pairs. This parameter transformation makes the classification robust against measurement disturbances while maintaining high productivity in pulse separation
2Quantity of substance
If pulse deinterlacing is performed in dense radar environments with high sensitivity, then more pulses are detected, but the complexity of separating and characterizing pulses increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the deinterlacing process into distinct stages: pulse pair formation, DTOA calculation, histogram construction, and cluster identification. This segmented approach breaks down the complex task of separating numerous pulses in dense environments into manageable steps, reducing overall process complexity while maintaining the ability to handle high quantities of detected pulses
Solution Approach 2:
The patent uses partial action by initially forming pulse pairs and computing DTOA for subsets of pulses rather than processing all pulses simultaneously. The histogram of DTOA values allows incremental analysis, enabling the system to handle dense radar environments by processing pulses in structured groups rather than all at once, thereby reducing computational complexity
3Measurement precision
If agile radar emissions are sorted by frequency ranges, then some separation is achieved, but the ranges must be wide causing mixture or narrow causing breakdown into single-frequency clusters
Solution Approach 1:
The patent introduces another dimension by moving from one-dimensional frequency-based sorting to two-dimensional classification using time differences of arrival (DTOA) between pulse pairs. This dimensional transition allows simultaneous separation of agile radar emissions without the trade-off between wide and narrow frequency ranges, as pulses are now classified based on temporal relationships rather than frequency alone, resolving the contradiction between separation precision and grouping efficiency
Data Source
Figure 1~2
Figure 3a
Figure 3b
AI summary
The invention relates to a method for deinterleaving a series of pulses, comprising: - a step (601) of receiving a series of pulses, - a step (602) of determining pattern repetition periods and phases associated with the pulses, by constructing a histogram of the difference in arrival times between the pulses, and of grouping the pulses into pulse trains with a substantially constant pattern repetition period and phase, - a step (603) of characterising the parameters of the pulse trains, - a step (604) of forming groups of pulse trains from the parameters of the pulse trains. The invention also relates to a device configured to carry out the method and the associated computer program.