Wavelet Anti-Jam Processing for Agile Interference Mitigation
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Solution Overview
Problem
Traditional anti-jamming techniques, such as those relying on Fourier transformations or FIR filters, are ineffective against agile jammers with changing frequencies or pulsed signals, as they fail to accurately identify and mitigate jamming signals due to fixed block/observation intervals, leading to compromised performance.
Innovation Solution
A method and apparatus utilizing wavelet transformations with adaptive time and frequency resolution levels to generate and weight wavelet transformations, evaluating jammer suppression performance and selecting optimal resolution levels for inverse wavelet transformation to effectively mitigate jamming signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional Fourier transformations or FIR filters are used with fixed block intervals, then processing is simple and deterministic, but the system cannot adapt to agile jammers with changing frequencies or pulsed signals
Solution Approach 1:
The patent applies wavelet transformation instead of fixed Fourier transformation, enabling dynamic time-frequency analysis that adapts to changing jammer characteristics. The wavelet transform provides flexible time-frequency resolution that can track agile jammers with varying frequencies and pulse patterns, resolving the contradiction between adaptability and complexity by introducing a more flexible mathematical framework.
Solution Approach 2:
The patent changes the fundamental parameter of signal analysis from fixed frequency bins (Fourier) to variable time-frequency representations (wavelet). This parameter change allows the system to adapt to different jammer behaviors by adjusting the time-frequency resolution characteristics, thereby improving adaptability while maintaining manageable complexity through standardized wavelet processing.
2Measurement precision
If fixed block/observation intervals are used, then processing is efficient and deterministic, but the bandwidth of agile jamming signals appears inflated and time resolution is insufficient
Solution Approach 1:
The patent segments the signal processing into multiple time-frequency cells using wavelet transformation, allowing independent analysis of different time intervals and frequency components. This segmentation enables precise measurement of agile jammer characteristics without being constrained by fixed block intervals, improving frequency measurement precision while maintaining processing efficiency through parallelizable operations.
Solution Approach 2:
The patent transitions from one-dimensional frequency analysis (Fourier) to two-dimensional time-frequency analysis (wavelet). This dimensional change provides simultaneous time and frequency resolution, allowing accurate measurement of agile jammers' frequency variations and pulse characteristics without inflating bandwidth estimates, while preserving processing efficiency through structured multi-resolution analysis.
3Loss of time
If anti-jam processing is tuned for short observation intervals, then time resolution is improved, but frequency resolution becomes insufficient for continuous wave jammers
Solution Approach 1:
The patent applies local quality by providing different time-frequency resolution characteristics at different locations in the time-frequency plane. Through wavelet transformation, the system achieves high time resolution for pulsed jammers in certain time cells while maintaining adequate frequency resolution for continuous wave jammers in other regions, resolving the contradiction through spatially varying resolution properties.
Solution Approach 2:
The wavelet transformation provides dynamic time-frequency resolution that adapts to the characteristics of the signal being analyzed. The system can adjust the balance between time and frequency resolution based on the local signal properties, achieving high time resolution when needed for pulsed jammers while maintaining frequency resolution for continuous signals, thereby resolving the contradiction between these two resolution requirements.
Data Source
AI summary
Wavelet transformation based anti-jam processing methods and systems are disclosed. An anti-jam processing method may include: generating a wavelet transformation for a unit of a received signal; weighting each element in the wavelet transformation to generate a weighted wavelet transformation; evaluating jammer suppression performances of a plurality of time and frequency resolution levels defined for an inverse wavelet transformation; selecting a particular time and frequency resolution level among the plurality of time and frequency resolution levels; and applying the inverse wavelet transformation to the weighted wavelet transformation, wherein the inverse wavelet transformation is applied based on the particular time and frequency resolution level selected.