dToF Pulse Histogram Correction for Long Tail Removal
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Solution Overview
Problem
Conventional Time of Flight (ToF) devices struggle with accurately distinguishing between useful signal pulses and undesirable long tail distributions, leading to false positives and reduced frame rates, especially at close distances, due to the complexity of existing algorithms like Expectation-Maximization, which are computationally intensive and not practical for real-time embedded sensors.
Innovation Solution
A method involving precomputing a global pulse shape into a reference and correction part, fitting these parts to the current histogram, and correcting the long tail distribution by subtracting or compensating with a precomputed estimate, thereby simplifying processing and maintaining high frame rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Expectation-Maximization algorithms are used to identify bins representative of object presence, then measurement precision is improved, but device complexity and computational overhead increase significantly
Solution Approach 1:
The patent segments the histogram into distinct regions: a first portion containing the useful signal pulse and a second portion containing the long tail distribution. By applying different processing strategies to each segment (thresholding to the first portion and ignoring the second portion), the algorithm achieves accurate object detection without the computational burden of complex algorithms like Expectation-Maximization applied to the entire histogram.
Solution Approach 2:
The patent extracts and isolates the useful signal from the long tail distribution by defining a threshold that separates the first portion (useful signal) from the second portion (long tail). This extraction allows the system to focus computational resources only on the relevant signal portion, eliminating the need for complex algorithms to distinguish signal from noise.
2Measurement precision
If complex algorithms are used to distinguish signal from long tail, then measurement precision is improved, but productivity decreases due to reduced frame rates
Solution Approach 1:
By segmenting the histogram into a first portion (useful signal) and second portion (long tail), the patent enables simple threshold-based processing of only the relevant signal portion. This segmentation approach maintains high measurement precision while dramatically reducing computational complexity, thereby preserving high frame rates and productivity.
Solution Approach 2:
The patent applies processing (thresholding) only to the necessary portion of the data (the first portion containing the useful signal) rather than analyzing the entire histogram. This partial action approach maintains detection accuracy while minimizing computational overhead, enabling real-time processing at high frame rates.
3Device complexity
If the long tail distribution is ignored, then device complexity is reduced, but false positives increase as secondary pulses may be occulted
Solution Approach 1:
The patent performs preliminary action by establishing a threshold before processing the histogram data. This threshold is specifically designed to account for the long tail distribution characteristics, allowing the system to reliably distinguish true signal from long tail artifacts. By setting this threshold in advance, the system maintains simplicity while preventing false positives.
Solution Approach 2:
The patent changes the threshold parameter to account for the presence of long tail distribution. By adjusting the threshold level based on the statistical characteristics of the long tail, the system maintains high reliability in detecting true signals while keeping the processing algorithm simple and computationally efficient.
4Device complexity
If thresholding is applied to the entire histogram, then device complexity is reduced, but measurement precision decreases as long tail bins are misidentified as signal
Solution Approach 1:
The patent segments the histogram into a first portion (useful signal) and second portion (long tail), applying thresholding only to the first portion. This segmentation prevents long tail bins from being misidentified as signal while maintaining the simplicity of threshold-based processing. The threshold is applied selectively to where it is needed, preserving both simplicity and precision.
Solution Approach 2:
The patent applies different processing qualities to different portions of the histogram: thresholding is applied to the first portion (useful signal) while the second portion (long tail) is ignored. This local quality approach ensures that the simple thresholding method is applied only where it is effective, preventing misidentification of long tail bins while maintaining overall algorithm simplicity.
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
Effectively removes or compensates for long tail distributions with high accuracy and low computational overhead, ensuring reliable object detection and consistent frame rates across varying distances.
Implementation Method 1
A photon may generate a carrier in the SPAD through the photoelectric effect.
Implementation Method 2
Direct Time-of-Flight (dToF) devices directly measure the time the signal takes to travel to the object and back to the device.
Data Source
AI summary
According to an embodiment, a method detects objects within a field of view of a time-of-flight sensor by processing photon arrival time distributions. The method obtains a current histogram generated by the time-of-flight sensor and provides a pre-computed nominal global pulse shape, including reference and correction parts. After fitting the reference part to the current histogram, the method determines the position of a long tail part using the relative positioning of the correction part to the reference part. The method calculates the long tail (LT) distribution estimate and corrects the current histogram.