LIDAR Peak Localization Using Correlation Convolution
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Solution Overview
Problem
LIDAR sensor systems face peak-fitting bias issues, particularly under low signal-to-noise ratio conditions, which affect the accuracy and reliability of range and velocity measurements.
Innovation Solution
The LIDAR sensor systems employ correlation data processing with predetermined functions such as Gaussian, Lorentzian, or polynomial functions to refine the peak location, reducing peak-fitting bias and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional peak-fitting methods are used to identify peak locations in correlation data, then the processing is simple and fast, but the measurement precision deteriorates under low signal-to-noise ratio conditions due to peak-fitting bias
Solution Approach 1:
The patent applies a predetermined function (such as Gaussian, Lorentzian, or polynomial) to the correlation data before performing peak fitting. This preliminary transformation of the data prepares it in a form that reduces peak-fitting bias, allowing subsequent peak location identification to achieve higher accuracy even under low signal-to-noise ratio conditions.
Solution Approach 2:
The patent transforms the correlation data by applying a predetermined function, which changes the parameters or characteristics of the data representation. This parameter transformation modifies the shape or distribution of the correlation data to eliminate systematic biases in peak fitting, thereby improving measurement precision without requiring fundamentally new processing methods.
2Reliability
If conventional peak-fitting methods are used, then the processing speed is fast, but the reliability of distance and velocity measurements deteriorates in noisy environments
Solution Approach 1:
By applying the predetermined function as a preliminary step to the correlation data, the patent prepares the data in advance to be more suitable for accurate peak fitting. This preliminary action ensures that subsequent peak identification produces reliable results even in noisy environments, while the overall process remains efficient because it builds upon existing fast Fourier transform and correlation techniques.
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 improves the accuracy and reliability of LIDAR systems by accurately identifying peak locations, even in noisy conditions, leading to more precise distance and velocity determinations.
Implementation Method 1
transmit a signal to an environment of the LIDAR sensor system; receive, from an object in the environment, a return signal in response to transmitting the signal
Implementation Method 2
determine correlation data between the signal and the return signal; identify, in the correlation data, an initial location of a peak
Data Source
AI summary
A LIDAR sensor system includes one or more processors. The processors transmit a signal to an environment of the LIDAR sensor system, receive, from an object in the environment, a return signal in response to transmitting the signal, determine correlation data between the signal and the return signal, identify, in the correlation data, an initial location of a peak, convolve the correlation data with a predetermined function to refine the initial location of the peak, and in response to the refined location of the peak, determine at least one of a distance to the object from the LIDAR sensor system or a velocity of the object.