TOF Depth Noise Analysis Using Correlated Source Modeling
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Solution Overview
Problem
Current noise simulation analysis tools for time-of-flight (TOF) systems are inadequate in accurately predicting system performance, often resulting in overly pessimistic or optimistic design predictions, leading to over-design or under-design, due to the inability to accurately account for noise contributions from multiple sources in complex processing pipelines.
Innovation Solution
A computational tool that breaks down TOF system operations into manageable chunks, allowing for the independent calculation and combination of noise contributions, accounting for correlation or non-correlation between noise sources, and providing a more accurate estimation of depth data quality, characterized by pixel field of view and jitter uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RMS approach is used to account for noise from independent sources, then noise contribution is always additive and increases at each stage, but this yields inaccurate overly pessimistic results when noise sources are correlated
Solution Approach 1:
The patent segments the noise analysis by separating signal and noise into distinct components that are tracked independently through each processing stage. Instead of combining them into a single variable, the method maintains separate noise variables for each independent noise source, allowing accurate tracking of their individual contributions and correlations through the processing pipeline.
Solution Approach 2:
The patent introduces an intermediary covariance matrix that tracks the correlation between different noise sources as they propagate through the processing pipeline. This covariance matrix acts as a mediator that captures the statistical relationships between noise sources, enabling accurate combination of correlated noise contributions without using the oversimplified RMS approach.
2Measurement precision
If simulation based model is used to accurately model noise at every stage, then statistical data-set of noise can be obtained, but it is challenging to separately model noise at every stage and prior art techniques do not maintain separation of noise and signal
Solution Approach 1:
The patent segments the complex noise modeling task by maintaining separate noise variables for each independent noise source throughout the processing pipeline. This segmentation allows each noise source to be modeled independently with its own statistical properties, while still capturing their combined effect through the covariance matrix, thereby reducing the overall modeling complexity.
Solution Approach 2:
The patent changes the parameters being tracked from a single combined signal-noise variable to multiple separate parameters including individual noise variables and their covariance matrix. This parameter transformation enables accurate statistical analysis of each noise source while maintaining computational tractability through efficient matrix operations.
3Reliability
If more illumination power is generated to compensate for pessimistic performance prediction, then system may meet specifications, but cost and operating power consumption increase
Solution Approach 1:
The patent employs feedback by using the accurately predicted system performance information to adjust the illumination power level. The noise analysis model provides feedback about the actual noise contributions at each stage, allowing the system to optimize illumination power to the minimum level needed to meet specifications, avoiding both over-power and under-power conditions.
Data Source
AI summary
An analytical tool useable with complex systems receives as input various system parameters to predict whether sufficiently accurate quality depth data will be provided by the TOF system. Depth data quality estimates involve dividing system operation into smaller operations whose individual depth data quality contributions can be more readily computed. The effect of the individual operations is combined and the tool outputs a depth data quality estimate accounting for the net result of the various unique operations performed by the system. When used with a TOF system, input parameters may include magnitude and angular distribution of TOF emitted optical energy, desired signal/noise, sensor characteristics, TOF imaging optics, target object distances and locations, and magnitude of ambient light. Analytical tool output data can ensure adequate calculation accuracy to optimize the TOF system pre-mass production, even for TOF systems whose sequence of operations and sensor operations are flexibly programmable.