FMCW LiDAR Interpolation for Speckle-Affected Range Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing FMCW LiDAR systems face challenges with speckle, which leads to signal fading and increased measurement inaccuracy due to coherent light reflections, reducing the probability of detection and object classification reliability.
Innovation Solution
An FMCW LiDAR system that calculates missing measurement values through interpolation using valid pixels unaffected by speckle, rather than repeating measurements under different conditions, thereby reducing the need for additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple measurements are repeated under different conditions to mitigate speckle effects, then measurement reliability is improved, but device complexity and hardware requirements increase
Solution Approach 1:
The patent creates a computational copy of the physical measurement process by using interpolation to generate virtual measurement values for pixels affected by speckle. Instead of physically repeating measurements with different light conditions, the system copies valid measurements from neighboring pixels and mathematically reconstructs the missing data, achieving the same reliability improvement without the hardware complexity of repeated physical measurements
Solution Approach 2:
The patent replaces the mechanical/optical system of repeating physical measurements with a computational algorithm. Rather than using multiple laser sources or temporal modulation to eliminate speckle through physical means, the system uses software-based interpolation algorithms to substitute for the missing measurement data, thereby reducing hardware complexity while maintaining measurement reliability
2Measurement precision
If speckle mitigation approaches are implemented, then measurement accuracy is improved, but additional hardware components are required
Solution Approach 1:
The system creates computational copies of measurement data through interpolation algorithms, generating virtual measurement values for pixels affected by speckle. This allows the system to achieve improved measurement accuracy by filling in missing data without requiring additional optical components or hardware systems
Solution Approach 2:
The system uses its own existing valid measurements to compensate for invalid ones. By utilizing the measurement data already collected from neighboring pixels, the system self-corrects the speckle-induced errors without requiring external hardware assistance or additional measurement systems
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
Enhances measurement accuracy and reliability by effectively mitigating speckle effects, allowing for more reliable object detection and classification with reduced hardware complexity.
Implementation Method 1
a light source (16) configured to produce light having a varying frequency
Implementation Method 2
a scanning unit (18) configured to emit the light produced by the light source (16) into different directions
Implementation Method 3
detecting, for each pixel, an interference of a measuring portion of the light, which was reflected at the object, and a reference portion of the light
Implementation Method 4
the photodiode delivers a current that is proportional to the squared sum of the two optical waves ('homodyne detection')
Implementation Method 5
The computing unit (37) is configured to determine, for each pixel, whether the electrical signal produced by the detector (32) for the pixel is strong enough
Data Source
Figure 1~3
Figure 4~5
Figure 6~7
AI summary
A LiDAR system (14) for measuring a range and/or a relative velocity to an object (12) has a light source producing light having a varying frequency, a scanning unit (18) emitting the light into different directions that correspond to pixels in an image of the object. A detector (32) detects for each pixel an interference of a measuring portion of the light, which was reflected at the object, and a reference portion of the light, which was not reflected at the object, and produces, for each pixel, an electrical signal representing the interference. A computing unit (37) determines whether the electrical signal is strong enough so that it can be reliably distinguished from a noise floor. For first pixels for which the electrical signal is strong enough, the range and/or the relative velocity to the object (12) is computed based on the electrical signals that are associated with each of the first pixels. For a second pixel for which the electrical signal is not strong enough, this information is calculated in an interpolation step based on one or more electrical signals associated with one or more first pixels.