Method and system for noise simulation analysis useable with systems including time-of-flight depth systems

• US 20110295562A1
• Filed: 07/30/2010
• Published: 12/01/2011
• Est. Priority Date: 08/04/2009
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Canesta Incorporated (Microsoft Corporation)
• Inventors: Mehta, Swati, Bamji, Cyrus
• Granted Patent: US 8,849,616 B2
• Time in Patent Office: Days
• US Class Current: 702/189
• CPC Class Codes: G01S 17/36 with phase comparison between the received signal and the contemporaneously transmitted signal
• CPC Class Codes: G01S 7/4915 Time delay measurement, e.g. operational details for pixel components signal extraction and conditioning G01S7/493; Phase measurement
• CPC Class Codes: G01S 7/493 Extracting wanted echo signals
• CPC Class Codes: H04N 17/002 for television cameras
• CPC Class Codes: H04N 25/60 Noise processing, e.g. detecting, correcting, reducing or removing noise

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.

Claims

1. A 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 less pessimistic than if statistical noise models for independent noise sources at each sequence in operations Oi were combined using RMS values.

2. The method of claim 1, wherein step (b) yields signal and noise estimates exceeding accuracy of a closed form expression of noise following operation Oi.

3. The method of claim 1, wherein accumulated noises are derived from at least one noise component selected from a group consisting of (i) shot noise, (ii) KT/C noise, (iii) flicker noise, (iv) ADC quantization noise, (v) digital accumulation noise, and (vii) thermal noise.

4. The method of claim 1, further including accumulating at least one value of at least one of signal and noise, before carrying out step Oi.

5. The method of claim 1, wherein step (a) is carried out using at least one of (i) hardware and (ii) software.

6. The method of claim 1, wherein step (b) is carried out using at least one of (i) hardware and (ii) software.

7. The 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.

8. The 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.

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 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 sub-system including:

means for computing 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;

means for 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 yielded by said means for combining is less pessimistic than if statistical noise models for independent noise sources at each sequence in operations Oi were computed.

10. The sub-system of claim 9, wherein said means for combining yields signal and noise estimates exceeding accuracy of a closed form expression of noise following operation Oi.

11. The sub-system of claim 9, wherein accumulated noises are derived from at least one noise component selected from a group consisting of (i) shot noise, (ii) KT/C noise, (iii) flicker noise, (iv) ADC quantization noise, (v) digital accumulation noise, and (vii) thermal noise.

12. The sub-system of claim 9, further including means for accumulating at least one value of at least one of signal and noise, before carrying out step Oi.

13. The sub-system of claim 9, wherein said means for computing includes at least one of (i) hardware and (ii) software.

14. The sub-system of claim 9, wherein said means for combining includes at least one of (i) hardware and (ii) software.

15. The sub-system of claim 9, wherein said means for computing models 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.

16. The sub-system of claim 9, 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.

17. A method to estimate signal to noise (S/N) for 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) determining accumulated signals and noises prior to at least one said operation Oi;

and

(b) combing new accumulated signals and noises after said operation Oi in terms of signals before said operation Oi and in terms of system operation conditions;

wherein a final statistical noise estimate resulting from step (b) is less pessimistic than if statistical noise models for independent noise sources at each sequence in operations Oi were combined.

18. The method of claim 17, wherein accumulated noises are derived from at least one noise component selected from a group consisting of (i) Shot noise, (ii) KT/C noise, (iii) flicker noise, (iv) ADC quantization noise, (v) digital accumulation noise, and (vii) thermal noise.

19. The method of claim 17, wherein step (b) includes at least one of (i) computing analytically at least one of additional noise and additional signal, and (ii) 20. The method of claim 17, said electrical system includes at least one of (i) an imaging system, (ii) a depth system, and (iii) a time-of-flight depth system.

20. The method of claim 17, wherein at least one of step (a) and step (b) is carried out using at least one of (i) hardware, and (ii) software.