LiDAR Histogram Processing for SPAD Noise and Peak Detection
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Solution Overview
Problem
LIDAR systems face challenges in providing robust distance accuracy down to a few centimeters, particularly under varying ambient conditions, due to the limited dynamic range and sensitivity to background noise of single photon avalanche diodes (SPADs), which are susceptible to ambient light levels.
Innovation Solution
The system employs a method to estimate background noise levels and aggregate total photon counts with improved temporal resolution by using a photosensor with SPADs, subdividing time intervals into bins, and employing arithmetic logic circuits to generate histograms and remove background noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SPADs are used to detect photons, then sensitivity to ambient light levels is improved, but susceptibility to background noise increases
Solution Approach 1:
The patent segments the detection process into multiple time bins within each time interval, allowing separate accumulation of photon counts for signal and background noise. By subdividing the time interval into discrete bins, the system can distinguish between photons arriving during the laser pulse window and those arriving during background intervals, thereby reducing noise susceptibility while maintaining SPAD sensitivity.
Solution Approach 2:
The patent extracts background noise from the total photon count by measuring it separately during background time intervals (when no laser pulse is present) and then subtracting it from the total signal measurement. This extraction of the harmful background component allows the system to maintain high sensitivity while eliminating noise interference.
2Measurement precision
If time intervals are subdivided into bins for histogram accumulation, then temporal resolution is improved, but device complexity increases
Solution Approach 1:
The patent divides each time interval into multiple discrete time bins, creating a histogram structure that accumulates photon counts in each bin. This segmentation provides fine temporal resolution for distinguishing signal from noise while organizing data in a structured manner that facilitates efficient processing through systematic accumulation and subtraction operations.
Solution Approach 2:
The patent employs periodic laser pulse trains with regular time intervals, where each pulse train corresponds to a specific time interval with associated time bins. This periodic structure allows systematic accumulation of photon counts across multiple pulses, improving signal-to-noise ratio through averaging while maintaining manageable data processing through repeated, predictable measurement cycles.
3Reliability
If multiple pulse trains are transmitted over time intervals, then measurement robustness is improved, but measurement time increases
Solution Approach 1:
The patent transmits multiple pulse trains periodically over successive time intervals, accumulating photon counts in histogram bins for each pulse train. This periodic transmission allows robust statistical averaging across multiple measurements, improving reliability and noise rejection. The systematic repetition enables efficient use of time by continuously acquiring data in structured intervals rather than performing separate measurements.
Solution Approach 2:
The patent combines multiple photon count measurements from successive pulse trains into a single histogram accumulation process. By merging the data from multiple pulse trains into unified time-bin histograms, the system achieves robust statistical measurements more efficiently than sequential processing, reducing total measurement time while improving reliability through combined data analysis.
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 enhances the accuracy of distance measurements by reducing noise and improving temporal resolution, enabling more precise LIDAR operations under diverse conditions.
Implementation Method 1
a photosensor comprising one or more photodetectors configured to detect photons from the one or more pulse trains and photons from ambient light
Implementation Method 2
A LIDAR system measures the distance to an object by irradiating a landscape with pulses from a laser, and then measuring the time for photons to travel to an object and return after reflection
Data Source
Figure 1A~1B
Figure 2
Figure 3
AI summary
An optical measurement system may improve the accuracy with which it estimates distances to surrounding objects by upgrading various aspects of its data path. Spatial resolution may be increased by subdividing histogram buckets or integration registers based on spatial location. Saturation at any point in the data path can be detected and used to stop counting photons in individual pixels, which can then be normalized after a measurement is over. Multiple peaks can be detected using recursive or iterative techniques to identify a largest remaining peak at each stage. Instead of iterating through the histogram memory multiple times, a threshold can be pre-calculated based on an estimated ambient noise level, and peaks can be detected in a single pass.