LIDAR Beat Signal Processing via Super-Resolution FFT
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Solution Overview
Problem
Current LIDAR systems face challenges in rapidly and accurately processing large amounts of data for real-time generation of high-resolution three-dimensional images, often requiring expensive computational hardware and suffering from low-resolution processing inaccuracies due to high signal-to-noise ratios and quantization errors.
Innovation Solution
The implementation of a Fourier Transform (FT) or Fast Fourier Transform (FFT) method to convert low-resolution data into high-resolution frequency domain information, enabling fast and accurate signal processing by generating low-resolution FT data and performing signal processing techniques to enhance resolution, thereby estimating distance and velocity of objects with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If low-resolution signal processing techniques are used to rapidly analyze imaging data, then processing speed is improved, but measurement precision deteriorates due to high signal-to-noise ratios and quantization errors
Solution Approach 1:
The patent divides the signal processing into two distinct stages: first performing low-resolution FFT for rapid initial analysis, then applying super-resolution algorithms specifically to enhance the frequency domain data. This segmentation allows each stage to optimize for its specific purpose - speed in the first stage, precision in the second stage - thereby resolving the contradiction between processing speed and measurement precision.
Solution Approach 2:
The patent performs preliminary low-resolution FFT processing to obtain initial frequency domain data before applying super-resolution enhancement. This preliminary action provides a computationally efficient starting point that captures the essential signal characteristics, which are then refined through super-resolution techniques to achieve high precision distance and velocity estimation without requiring full high-resolution processing from the beginning.
2Measurement precision
If high-resolution signal processing is used to accurately estimate distance and velocity, then measurement precision is improved, but processing time increases making real-time generation difficult
Solution Approach 1:
The patent applies super-resolution algorithms selectively to the frequency domain data obtained from low-resolution FFT, rather than performing full high-resolution processing on the entire signal. This partial action approach focuses computational resources on enhancing the critical frequency information needed for accurate distance and velocity estimation, achieving high measurement precision while avoiding the excessive processing time required for complete high-resolution signal processing.
3Productivity
If expensive computational hardware is used to process large amounts of imaging data rapidly, then productivity is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the need for expensive high-performance computational hardware with a software-based super-resolution processing approach. By implementing sophisticated signal processing algorithms that can achieve high-resolution results from low-resolution input data, the system substitutes complex hardware requirements with computationally efficient algorithms, thereby maintaining high productivity while reducing device complexity and cost.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient and accurate conversion of low-resolution data into high-resolution information, enhancing the accuracy of distance and velocity estimation in LIDAR systems, facilitating rapid and computationally efficient processing of large data sets for real-time three-dimensional image generation.
Implementation Method 1
light signals reflected-off of a scanned object may be analyzed rapidly and accurately by the LIDAR system to estimate a distance and/or relative velocity of the object
Implementation Method 2
Various methods described herein may utilize a Fourier Transform (FT) or Fast Fourier Transform (FFT) to convert time domain information obtained from the reflected light signals into frequency domain information that can be used to estimate the distance and/or velocity information
Data Source
AI summary
Systems and methods described herein are directed to computationally fast and accurate processing of data acquired by a remote imaging system, such as a Light Detection and Ranging system (LIDAR). Example embodiments describe processing of scanned target data based on performing a low-resolution Fourier Transform (FT) of a beat signal that may be a function of distance and/or velocity of objects associated with the scanned target. Various methods described herein can effectively convert the low-resolution FT data into high-resolution frequency domain data that can be used to accurately estimate a frequency of the beat signal. The system may use the beat signal frequency to determine the distance and/or velocity of the corresponding object and generate point-cloud information associated with a three-dimensional image construction of the scanned target.