Sparse Partial Fourier Transform for Laser Radar Signal Processing
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Solution Overview
Problem
Current coherent photon-counting laser radar systems face limitations in sensitivity due to self-blocking detectors and struggle with rapid signal processing in low-signal scenarios, necessitating improved methods for transforming sparse, binary-valued time domain data into frequency domain data efficiently.
Innovation Solution
The method involves performing a Sparse Partial Fourier Transform (SPFT) on sparse, binary-valued time domain data from laser radar systems, utilizing calculated matrices from time and frequency vectors to achieve efficient transformation, thereby reducing computational complexity and enhancing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional Fourier Transform methods are used to transform time domain data to frequency domain data in laser radar systems, then frequency domain analysis can be performed, but the calculation time is excessively long and processing speed is insufficient
Solution Approach 1:
The patent segments the full Fourier Transform process into a partial transform that operates only on relevant frequency components. By dividing the frequency domain into regions of interest and computing only those portions, the method achieves faster processing while maintaining necessary analysis accuracy for laser radar applications.
Solution Approach 2:
The patent applies partial action by performing Fourier Transform only on the necessary portion of the frequency spectrum rather than the entire spectrum. This partial transform approach computes only the frequency components needed for target detection and analysis, significantly reducing calculation time while preserving essential signal information.
2Productivity
If sparse, binary-valued time domain data from photon-counting detectors is processed using conventional methods, then frequency domain information can be obtained, but computational complexity is too high for real-time processing
Solution Approach 1:
The patent applies local quality by adapting the transform method to the specific characteristics of sparse, binary-valued laser radar data. The partial Fourier Transform is tailored to process this specific data type efficiently, using properties of sparsity to reduce the number of computations needed while maintaining frequency domain accuracy.
Solution Approach 2:
The patent changes key parameters of the Fourier Transform process to match the characteristics of sparse binary data. By modifying the transform algorithm to exploit data sparsity and binary properties, the computational complexity is reduced from O(N log N) to a much lower complexity suitable for real-time processing of photon-counting detector outputs.
3Measurement precision
If full Fourier Transform is performed on laser radar signals, then complete frequency domain information is obtained, but the processing time prevents rapid signal processing in low-signal scenarios
Solution Approach 1:
The patent performs partial Fourier Transform on only the necessary frequency components rather than the complete spectrum. This partial action maintains measurement precision for the frequency ranges relevant to target detection while dramatically reducing processing time to enable rapid signal processing in low-signal laser radar scenarios.
Solution Approach 2:
The frequency domain is segmented into regions of interest and regions that can be skipped. By identifying and computing only the frequency segments containing relevant signal information, the patent maintains analysis accuracy where needed while reducing overall processing time through selective computation.
Data Source
AI summary
A method for transforming data from the time domain to the frequency domain. The method including receiving time domain input data, the time domain input data being sparse and binary-valued, obtaining at least one time vector corresponding to times of non-zero entries in the time domain input data, obtaining a frequency vector corresponding to frequencies of interest, determining at least one matrix corresponding to the at least one time vector and the frequency vector, performing a Sparse Partial Fourier Transform (SPFT) computation using the at least one matrix, and providing frequency domain output data corresponding to the time domain input data.