COMPRESSIVE SCANNING LIDAR

• US 20150378011A1
• Filed: 06/27/2014
• Published: 12/31/2015
• Est. Priority Date: 06/27/2014
• Status: Active Grant

• Current Assignee: HRL Laboratories LLC (The Boeing Co.)
• Original Assignee: HRL Laboratories LLC (The Boeing Co.)
• Inventors: OWECHKO, Yuri
• Granted Patent: US 9,575,162 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 17/02 Systems using the reflection of electromagnetic waves other than radio waves G01S17/66 takes precedence
• CPC Class Codes: G01S 17/34 using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/4817 relating to scanning
• CPC Class Codes: H01S 5/021 Silicon based substrates
• CPC Class Codes: H01S 5/0215 Bonding to the substrate
• CPC Class Codes: H01S 5/0262 Photo-diodes, e.g. transceiver devices, bidirectional devices H01S5/0265 takes precedence
• CPC Class Codes: H01S 5/12 the resonator having a periodic structure, e.g. in distributed feedback [DFB] lasers comprising a photonic bandgap structure H01S5/11; surface-emitting lasers H01S5/18

Abstract

A method for increasing resolution of an image formed of received light from an illuminated spot includes measuring a y vector for measurement kernels A₁to A_M, where M is a number of the measurement kernels, measuring the y vector including programming a programmable N-pixel micromirror or mask located in a return path of a received reflected scene spot with a jth measurement kernel A_j of the measurement kernels A₁ to A_M, measuring y, wherein y is an inner product of a scene reflectivity f(α,β) with the measurement kernel A_j for each range bin r_i, wherein α and β are azimuth and elevation angles, respectively, repeating programming the programmable N-pixel micromirror or mask and measuring y for each measurement kernel A₁ to A_M, and forming a reconstructed image using the measured y vector, wherein forming the reconstructed image includes using compressive sensing or Moore-Penrose reconstruction.

Claims

1. A method for increasing resolution of an image formed of received light from an illuminated spot comprising:

measuring a y vector for measurement kernels A₁ to A_M, where M is a number of the measurement kernels, measuring the y vector comprising;

programming a programmable N-pixel micromirror or mask located in a return path of a received reflected scene spot with a jth measurement kernel A_j of the measurement kernels A₁ to A_M;

measuring y, wherein y is an inner product of a scene reflectivity f(α

,β

) with the measurement kernel A_j for each range bin r_i, wherein α and

β

are azimuth and elevation angles, respectively;

repeating programming the programmable N-pixel micromirror or mask and measuring y for each measurement kernel A₁ to A_M; and

forming a reconstructed image using the measured y vector, wherein forming the reconstructed image comprises using compressive sensing or Moore-Penrose reconstruction.

2. The method of claim 1 wherein measuring y comprises:

illuminating the spot using a light source with frequency modulation continuous wave (FMCW) modulation;

using FMCW coherent detection; and

using Fourier analysis.

3. The method of claim 1 wherein forming a reconstructed image comprises:

using compressive sensing if M is less than N, and if θ

is sufficiently sparse to reconstruct f using an L₁ norm;

4. The method of claim 1 wherein forming a reconstructed image comprises:

if M is greater than or equal to N, using a Moore-Penrose inverse of matrix A to reconstruct f

{circumflex over (f)}=A⁺y

where

A⁺=(A^HA)<sup>−

1</sup>A^H

5. The method of claim 1 wherein the illuminated spot is illuminated by a scanning laser and wherein the method further comprises:

scanning the laser; and

repeating forming the reconstructed image for each spot illuminated by the laser;

wherein measuring y further comprises;

emitting a laser beam having triangular frequency modulation continuous wave (FMCW) from the scanning laser;

mixing a portion of the emitted laser beam with the received light at a photodiode detector for coherent detection;

wherein the scanning laser comprises a scanning micromirror; and

wherein the programmable N-pixel micromirror is synchronized with the scanning micromirror to maintain the received light focused on a photodiode detector.

6. The method of claim 1 further comprising:

illuminating the illuminated spot using a laser; and

detecting the received light using a photodiode detector;

wherein a photodiode output current is

i(t)=S(Φ

_lo+yΦ

_o+2√

{square root over (yΦ

_loΦ

_o)} cos(ω

_bt+φ

))+i_b

where y is an inner product of the scene reflectivity f(α

,β

) with a measurement kernel A_j, Φ

_o is an output laser power, Φ

_lo is an local oscillator power, W_b is a beat between the laser and the received light, φ

is a phase difference between the laser and the return light, S is a diode responsivity, i_b is a diode bias current, and t is time.

7. The method of claim 2 further comprising:

determining parameters for target components of the illuminated spot, including a range R_a, a range uncertainty Δ

R_a, and a velocity v_t for each target component using equations

8. The method of claim 1 wherein:

each measurement kernel A_j is binary.

9. The method of claim 1 wherein:

each pixel in the N-pixel micromirror or mask may be set to be on in order to pass the received light to a photodiode detector, or off in order to block the received light from the photodiode detector; and

further comprising;

modulating a pixel in the N-pixel micromirror or mask to be on or off over time; and

integrating passed or blocked received light for a pixel in the N-pixel micromirror or mask in a photodetector diode to provide multi-valued measurement kernels.

10. The method of claim 1 further comprising:

adapting an azimuth and elevation angular resolution for an illuminated spot in order to optimize resolution of regions of interest and update rates comprising;

varying an effective programmable N-pixel micromirror or mask resolution by programming the programmable N-pixel micromirror or mask to effectively reduce N; and

varying a number of measurements M.

11. A LIDAR system comprising:

a pulsed frequency modulated laser having an emitted beam with power Φ

_o;

a micromirror optically coupled to the laser for scanning the emitted beam across a scene to illuminate spots in the scene;

a photodiode detector;

a portion of the emitted beam with power Φ

_lo coupled to the photodiode detector; and

a programmable N-pixel mirror or mask array in an optical path of reflected received light from an illuminated spot, the programmable N-pixel mirror or mask array optically coupled to the photodiode detector.

12. The system of claim 11 further comprising:

means for measuring a y vector for measurement kernels A₁ to A_M, where M is a number of the measurement kernels, the means for measuring the y vector comprising;

means for programming the programmable N-pixel micromirror or mask with a jth measurement kernel A_j of the measurement kernels A₁ to A_M;

means for measuring y, wherein y is an inner product of a scene reflectivity f(α

,β

) with the measurement kernel A_j for each range bin r_i, wherein α and

β

are azimuth and elevation angles, respectively;

means for repeating programming the programmable N-pixel micromirror or mask and measuring y for each measurement kernel A₁ to A_M; and

means for forming a reconstructed image using the measured y vector comprising compressive sensing or Moore-Penrose reconstruction;

wherein the compressive sensing means is used if M is less than N, and if θ

is sufficiently sparse to reconstruct f using an L₁ norm;

13. The system of claim 11 wherein:

wherein a photodiode output current of the photodiode detector is

i(t)=S(Φ

_lo+yΦ

_o+2√

{square root over (yΦ

_loΦ

_o)} cos(ω

_bt+φ

))+i_b

where y is an inner product of the scene reflectivity f(α

,β

) with a measurement kernel A_j, Φ

_o is an output laser power, Φ

_lo is an local oscillator power, ω

_b is a beat between the laser and the received light, φ

is a phase difference between the laser and the return light, S is a diode responsivity, i_b is a diode bias current, and t is time.

14. The system of claim 11 further comprising:

means for determining parameters for target components of the illuminated spot, including a range R_a, a range uncertainty Δ

R_a, and a velocity v_t for each target component using equations

15. The system of claim 11 wherein each measurement kernel A_j is binary.

16. The system of claim 11 further comprising:

means for setting each pixel in the N-pixel micromirror or mask to be on in order to pass the received light to the photodiode detector, or off in order to block the received light from the photodiode detector; and

means for modulating a pixel in the N-pixel micromirror or mask to be on or off over time to provide multi-valued measurement kernels.

17. A LIDAR comprising:

a scanning laser for scanning a scene and illuminating a spot in the scene;

a photodiode detector for detecting received light reflected from the scene;

a programmable N-pixel mirror or mask array in an optical path of reflected received light, the programmable N-pixel mirror or mask array optically coupled to the photodiode detector; and

means for forming a reconstructed image comprising compressive sensing or Moore-Penrose reconstruction.

18. The LIDAR of claim 17 further comprising:

means for measuring a y vector for measurement kernels A₁ to A_M, where M is a number of the measurement kernels, the means for measuring the y vector comprising;

means for programming the programmable N-pixel micromirror or mask with a jth measurement kernel A_j of the measurement kernels A₁ to A_M;

means for measuring y, wherein y is an inner product of a scene reflectivity f(α

,β

) with the measurement kernel A_j for each range bin r_i, wherein α and

β

are azimuth and elevation angles, respectively;

means for repeating programming the programmable N-pixel micromirror or mask and measuring y for each measurement kernel A₁ to A_M; and

means for forming a reconstructed image using the measured y vector comprising compressive sensing or Moore-Penrose reconstruction.

19. The LIDAR of claim 18 wherein the means for forming a reconstructed image comprises:

a compressive sensing means and wherein the compressive sensing means is used if M is less than N, and if θ

is sufficiently sparse to reconstruct f using an L₁ norm;

20. The LIDAR of claim 18 wherein the means for forming a reconstructed image comprises:

a Moore-Penrose reconstruction, wherein the Moore-Penrose reconstruction is used to reconstruct f if M is greater than or equal to N, using a Moore-Penrose inverse of matrix A

{circumflex over (f)}=A⁺y

where

A⁺=(A^HA)<sup>−

1</sup>A^H

21. The LIDAR of claim 17:

wherein a photodiode output current of the photodiode detector is

i(t)=S(Φ

_lo+yΦ

_o+2√

{square root over (yΦ

_loΦ

_o)} cos(ω

_bt+φ

))+i_b

where y is an inner product of the scene reflectivity f(α

,β

) with a measurement kernel A_j, Φ

_o is an output laser power, Φ

_lo is an local oscillator power, ω

_b is a beat between the laser and the received light, φ

is a phase difference between the laser and the return light, S is a diode responsivity, i_b is a diode bias current, and t is time; and

further comprising;

means for determining parameters for target components in the illuminated spot, including a range R_a, a range uncertainty Δ

R_a, and a velocity v_t for each target component using equations