LIDAR Range Estimation via Noise-Adaptive Template Correlation
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Solution Overview
Problem
Prior LIDAR systems inaccurately estimate propagation delays due to non-Gaussian noise in received signals, which are not accounted for in their cross-correlation methods, leading to inaccurate range estimation.
Innovation Solution
Implementing maximum likelihood range estimation that accounts for multiple noise sources, including ambient shot noise, signal shot noise, electronic noise, and laser speckle, by using templates constructed based on signal and noise power parameters, rather than assuming Gaussian noise, and cross-correlating these templates with received data to determine accurate propagation delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-correlation methods are used to estimate propagation delays, then the processing is simple and fast, but the accuracy deteriorates due to non-Gaussian noise not being accounted for
Solution Approach 1:
The patent changes the parameters of the correlation method by incorporating signal and noise power parameters into the correlation calculation. Instead of using a simple cross-correlation, the system calculates a correlation value that is normalized by the signal power and noise power parameters, thereby adapting the correlation method to account for non-Gaussian noise characteristics while maintaining computational feasibility
Solution Approach 2:
The patent introduces signal power parameters and noise power parameters as intermediary variables that mediate between the raw correlation calculation and the final propagation delay estimation. These intermediary parameters characterize the noise sources and signal strength, allowing the system to compensate for non-Gaussian noise effects without completely redesigning the correlation algorithm
2Measurement precision
If multiple noise sources are accounted for in the estimation, then the accuracy improves, but the processing complexity increases
Solution Approach 1:
The patent segments the noise characterization by identifying and separately parameterizing different noise sources (ambient shot noise, signal shot noise, electronic noise, laser speckle). Each noise source is characterized by its own power parameter, allowing the system to account for multiple noise sources in a structured and computationally manageable way rather than treating noise as a single undifferentiated component
Solution Approach 2:
The patent transforms the complex problem of characterizing multiple noise sources into a set of manageable power parameters. By representing each noise source's impact through its power parameter and incorporating these parameters into the correlation calculation, the system achieves accurate range estimation under non-Gaussian noise conditions while maintaining reasonable processing complexity
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 provides accurate range estimation even under non-Gaussian noise conditions, improving the accuracy of LIDAR systems' range estimation and rendering of 3D images by accounting for various noise sources in the signal processing.
Implementation Method 1
measuring a received light beam reflected by the object
Implementation Method 2
A LIDAR system estimates the propagation delay between the received reflected light beam and the projected light beam
Data Source
AI summary
Example range estimation apparatus disclosed herein include a first signal processor to estimate a signal power parameter and a noise power parameter of a LIDAR system based on first data to be output from a light capturing device of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data to be output from the light capturing device, the second data associated with a modulated light beam projected by the LIDAR system, the templates generated based on the signal power parameter and the noise power parameter, and the second data to have a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor is also to determine, based on the templates, an estimated propagation delay associated with the second data.