GmAPD Data Normalization for Low-Intensity LiDAR Signal Detection
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Solution Overview
Problem
Lidar systems using Geiger-mode avalanche photodiode (GmAPD) detectors are susceptible to background noise, which complicates signal processing and limits the differentiation of low-intensity signals from noise.
Innovation Solution
Transform raw data from GmAPD detectors into a sequence of Bernoulli trials, apply binomial confidence estimation, and correct waveform distortion to normalize the data, enabling effective signal extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GmAPD detectors are used to detect laser signals, then detection sensitivity is improved, but susceptibility to background noise increases
Solution Approach 1:
The patent segments the detection process into multiple independent trials (Bernoulli trials) rather than treating each photon detection event as a single measurement. By dividing the detection into N independent trials and analyzing the distribution of detection events across these trials, the system can statistically distinguish true signals from background noise, thereby maintaining high sensitivity while reducing noise susceptibility.
Solution Approach 2:
The patent implements feedback through iterative statistical analysis of detection patterns. By continuously analyzing the distribution of detection events across multiple trials and comparing against expected statistical distributions, the system provides feedback to distinguish signal from noise, enabling adaptive filtering that maintains sensitivity while rejecting background noise.
2Quantity of substance
If multiple photons are detected to improve signal intensity, then signal strength increases, but waveform distortion occurs
Solution Approach 1:
The patent changes the parameter being measured from raw photon count to a normalized statistical parameter (number of trials N where at least one photon was detected). This parameter transformation preserves the intensity information while eliminating the waveform distortion that occurs with direct photon counting, as the normalized parameter linearly scales with intensity without saturation effects.
3Ease of operation
If traditional photon counting methods are used, then detection simplicity is maintained, but ability to distinguish low-intensity signals from noise deteriorates
Solution Approach 1:
The patent introduces dynamics by performing multiple independent detection trials rather than a single static measurement. The system dynamically varies the trial index i from 1 to N, collecting statistics on how often photons are detected in each trial. This dynamic approach enables statistical differentiation of low-intensity signals from noise while maintaining operational simplicity through automated processing.
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 the ability to distinguish low-intensity signals from background noise, improving the accuracy and reliability of lidar systems in detecting objects.
Implementation Method 1
The lidar receiver may be equipped with a Geiger-mode avalanche photodiode (GmAPD) type of single-photon detector, which absorbs incident photons and generates a current according to the photoelectric effect.
Implementation Method 2
When an APD is operated above its breakdown voltage, in a high-gain mode, it is referred to as a Geiger-mode APD.
Data Source
AI summary
A LiDAR apparatus including a light emitter system configured to emit laser pulses toward a target, a photon detector configured to detect laser signals reflected from the target by sensing an accumulation of single photons, and a controller coupled to the light emitter system and the photon detector, the controller configured to create an avalanche histogram from the detected laser signals, transform the avalanche histogram into an avalanche probability histogram by framing raw data from the photon detector as a sequence of Bernoulli trials within a timestamp interval and applying a binomial confidence estimation, transform the avalanche probability histogram into a linearized intensity histogram by correcting waveform distortion, and determine a photon intensity of the reflected laser signals based on an average count rate and an average photon flux rate associated with the linearized intensity histogram.