Method and system for noise simulation analysis useable with systems including time-of-flight depth systems
First Claim
1. A processor implemented method to estimate signal to noise (S/N) of an electrical system that has a sequence of operations Oi, each of said operations Oi combining a signal and statistical noise from prior operations in said sequence with signal and statistical noise at a current operation in said sequence of operations Oi, the method including the following steps:
- (a) at each sequence in said operations Oi, combining effects of each independent statistical noise source independently of other independent statistical noise sources such that each independent statistical noise source is self-correlating;
(b) combining final noise contribution of each independent statistical noise source at an output of said system to yield a final statistical noise estimate for said system;
wherein said final statistical noise estimate resulting from step (b) is more accurate than if statistical noise models for independent noise sources at each sequence in operations Oi were combined using RMS values.
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Abstract
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.
5 Citations
14 Claims
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1. A processor implemented method to estimate signal to noise (S/N) of an electrical system that has a sequence of operations Oi, each of said operations Oi combining a signal and statistical noise from prior operations in said sequence with signal and statistical noise at a current operation in said sequence of operations Oi, the method including the following steps:
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(a) at each sequence in said operations Oi, combining effects of each independent statistical noise source independently of other independent statistical noise sources such that each independent statistical noise source is self-correlating; (b) combining final noise contribution of each independent statistical noise source at an output of said system to yield a final statistical noise estimate for said system; wherein said final statistical noise estimate resulting from step (b) is more accurate than if statistical noise models for independent noise sources at each sequence in operations Oi were combined using RMS values. - View Dependent Claims (2, 3, 4, 5, 6)
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7. The processor implemented method of step 1, wherein at step (a) each said independent statistical noise source is modeled as being self-correlating such that computation is carried out absent recourse to use of absolute RMS magnitudes.
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8. The processor implemented method of step 1, wherein said electrical system includes at least one of (i) an imaging system, (ii) a depth system, and (iii) a time-of-flight depth system.
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9. A sub-system to estimate signal to noise (S/N) of an electrical system that has a sequence of operations Oi, each of said operations Oi to combine a signal and statistical noise from prior operations in said sequence with signal and statistical noise at a current operation in said sequence of operations Oi, the sub-system including:
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a processor; and a memory including code to instruct the processor to compute effects, at each sequence in said operations Oi, of each independent statistical noise source independently of other independent statistical noise sources such that each independent statistical noise source is self-correlating; to combine final noise contribution of each independent statistical noise source at an output of said system to yield a final statistical noise estimate for said system; wherein said final statistical noise estimate yielded by said combining is more accurate than if statistical noise models for independent noise sources at each sequence in operations Oi were computed. - View Dependent Claims (10, 11, 12, 13, 14)
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Specification