Distance Measurement Reliability Processing for Sunlight Noise
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Solution Overview
Problem
Existing distance measuring systems face challenges in accurately measuring distances due to noise clustering from environment light, such as sunlight, which affects the signal-to-noise ratio and reduces measurement accuracy.
Innovation Solution
A distance measuring device with an information acquisition circuit and a reliability-degree generation circuit that sets a reliability degree for each pixel based on contiguous distance and signal values, improving the signal-to-noise ratio by weighting and accumulating digital signals with similar characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple digital signals are accumulated to improve S/N ratio, then measurement accuracy is improved, but noise clustering occurs which deteriorates measurement accuracy
Solution Approach 1:
The patent segments the distance image into multiple regions and calculates reliability degrees separately for each region. By dividing the image into zones and evaluating noise characteristics independently in each segment, the system prevents global noise clustering while maintaining local measurement accuracy. This segmentation allows tailored noise filtering strategies for different spatial regions.
Solution Approach 2:
The patent applies partial accumulation by selectively accumulating only those signals that meet specific reliability criteria. Instead of accumulating all signals uniformly, the system performs partial accumulation on validated signals while excluding suspicious ones, thereby improving S/N ratio without introducing noise clustering from unreliable data points.
2Measurement precision
If digital signals are accumulated to improve S/N ratio, then signal quality is improved, but it becomes difficult to distinguish noise from valid signals
Solution Approach 1:
The patent implements feedback by calculating reliability degrees based on accumulated signals and using this reliability information to guide further signal accumulation and processing. The system continuously monitors signal characteristics, updates reliability assessments, and adjusts accumulation strategies accordingly, creating a closed-loop system that improves noise discrimination through iterative feedback.
Solution Approach 2:
The patent introduces reliability degree as an intermediary metric that mediates between raw digital signals and final distance measurements. This intermediary parameter acts as a filter, allowing the system to distinguish noise from valid signals by evaluating the reliability of each signal before incorporating it into the final measurement, thereby improving noise discrimination capability.
3Device complexity
If simple accumulation of digital signals is performed, then processing complexity is reduced, but noise clustering occurs reducing measurement reliability
Solution Approach 1:
The patent performs preliminary actions by calculating reliability degrees and identifying noise-prone regions before performing signal accumulation. By pre-processing the data to flag potentially noisy signals and regions, the system avoids the complexity of post-processing noise removal while maintaining measurement reliability. This preliminary classification enables simpler, more reliable accumulation strategies.
Solution Approach 2:
The patent introduces dynamic adaptivity by adjusting accumulation strategies based on locally calculated reliability degrees. Rather than using a fixed accumulation approach, the system dynamically modifies processing parameters according to the noise characteristics of each region, achieving high measurement reliability without requiring overly complex global processing algorithms.
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 by reducing noise and improving the signal-to-noise ratio, particularly in conditions with significant environmental light interference, such as daytime or bright environments.
Implementation Method 1
The distance measuring device irradiates laser light on a measurement target object and converts the intensity of reflected light reflected by the measurement target object into a time-series digital signal
Implementation Method 2
the distance to the measurement target object is measured on the basis of a time difference between a point in time of emission of the laser light and a point in time corresponding to a peak of a signal value of the digital signal
Implementation Method 3
Environment light such as sunlight scattered by an object is also made incident on the sensor. The environment light is noise that occurs at random.
Data Source
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AI summary
A distance measurement processing device according to an embodiment includes an information acquisition circuit and a reliability-degree generation circuit. The information acquisition circuit acquires a two-dimensional distance image having a measured distance as a pixel value and signal information concerning a signal value corresponding to the measured distance image. The reliability-degree generation circuit sets, for each of the pixels of the two-dimensional distance image, each of the pixels as a center pixel and generates a reliability degree based on information concerning the pixels having distance values equal to or smaller than a predetermined value from a distance value of the center pixel among the pixels contiguous within a predetermined range from the center pixel and a signal value corresponding to the center pixel.