Complex Waveform Correction via Real Imaginary Partitioning
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Solution Overview
Problem
Current waveform design algorithms face challenges in achieving accurate and efficient interference mitigation in complex and dynamic RF environments, often trading off between latency and precision, and are impractical for low size, weight, and power (SWaP) applications due to lengthy computing times and suboptimal convergence.
Innovation Solution
The method involves separating complex waveforms into real and imaginary components and using a neural network to analyze and adjust these components based on specific loss functions that account for mean square error, frequency domain power, and phase differences, allowing for incremental and iterative correction without increasing neural network size, thus overcoming the tradeoff between latency and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current waveform design algorithms are used for interference mitigation, then accuracy can be improved, but computing time increases and latency increases
Solution Approach 1:
The patent separates the complex waveform analysis into distinct real and imaginary components, processing each independently through separate neural network paths. This segmentation allows parallel processing that reduces overall computing time while maintaining the accuracy of the combined waveform analysis.
2Manufacturing precision
If high-accuracy algorithms like RUWO and ERA are used, then waveform tuning accuracy is improved, but convergence speed decreases
Solution Approach 1:
By dividing the waveform tuning process into separate real and imaginary component analyses, the patent enables independent optimization of each component's convergence. This segmented approach allows faster overall convergence while maintaining the high tuning accuracy characteristic of algorithms like RUWO and ERA.
Solution Approach 2:
The patent implements dynamic adjustment of waveform parameters through iterative neural network processing, where the system adapts and converges on optimal values. This dynamic approach achieves high precision waveform tuning while improving convergence speed compared to static traditional algorithms.
3Measurement precision
If neural network size is increased to improve accuracy, then waveform analysis precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent divides the neural network processing into separate real and imaginary component pathways, allowing each network to be smaller and simpler while collectively achieving the accuracy of a larger monolithic network. This segmentation reduces individual network complexity and power consumption.
Solution Approach 2:
The patent transforms the complex waveform analysis problem into two separate real-valued dimension problems instead of one complex dimension problem. This dimensional transformation allows use of simpler, more efficient neural network architectures while maintaining analytical precision.
Data Source
AI summary
A method of analyzing and correcting a complex dynamic waveform, such as a radar wave or communication wave. The method comprises dividing a complex waveform into its real and imaginary components, then separately analyzing each such waveform in a dedicated neural to determine respective real and imaginary loss functions as the difference between the actual characteristics and desired characteristics of the mean squared error and at least one of frequency-domain power, time-domain envelope, and frequency-domain phase. The real waveform and imaginary waveform are independently corrected, then recombined into a single corrected complex waveform. These differences are fed into a neural network to improve prediction correction to bring the actual waveform closer to a benchmark waveform. The method of the present invention displays increased accuracy over the prior art without increased computing time or sacrificing notch depth.