Optical Wind Lidar-Based Multifunctional Instrument for Enhanced Measurements and Prediction of Clear Air Turbulence and Other Wind-Based Aviation Related Phenomena

Abstract

A multiple functional instrument is provided. The instrument includes an optical autocovariance function interferometer that can feature multiple fields of view to detect winds in the atmosphere. The instrument can include an infrared camera to detect atmospheric temperatures and the presence of clouds, and a detector assembly that detects the polarization of light returned to the interferometer. Data collected by the instrument can be provided to a deep and reinforcement learning algorithm for real-time prediction of clear air turbulence and other wind-based aviation safety phenomena. Moreover, predicted and actual conditions can be correlated and used to train a deep learning algorithm to enable more accurate predictions. The instrument can be carried by an aircraft or other platform and operated to detect clear air turbulence or other atmospheric phenomena, and to provide instructions regarding flight parameters including wind-aided navigation in order to minimize the effect of predicted turbulence.

1 . A multifunctional instrument, comprising:
 a laser source;   an interferometer;   a beam division mechanism, wherein the beam division mechanism directs light from the laser source to a first field of regard, wherein the beam division mechanism directs light from the laser source to a second field of regard, wherein the beam division mechanism directs light from within the first field of regard to the interferometer, and wherein the beam division mechanism directs light from within the second field of regard to the interferometer.   
     
     
         2 . The multifunctional instrument of  claim 1 , wherein the beam division mechanism directs light from within the first field of regard and light from within the second field of regard to the interferometer at different times. 
     
     
         3 . The multifunctional instrument of  claim 1 , wherein the laser source outputs light having a first wavelength that is directed to the first field of regard, wherein the laser source outputs light having the same or a second wavelength that is directed to the second field of regard, and wherein light from the first field of regard and light from the second field of regard are provided to the interferometer simultaneously. 
     
     
         4 . The multifunctional instrument of  claim 3 , wherein the interferometer provides a first optical path difference for light of the first wavelength and a second optical path difference for light of the second wavelength. 
     
     
         5 . The multifunctional instrument of  claim 1 , further comprising:
 an infrared camera, wherein the infrared camera has a field of view that encompasses at least the first field of regard of the optical autocovariance lidar.   
     
     
         6 . The multifunctional instrument of  claim 1 , further comprising:
 a plurality of detectors, wherein a first subset of the detectors receives light of the first wavelength, wherein a second subset of the detectors receives light of the second wavelength, wherein the light received at a first detector of the first subset of detectors is spaced in phase from the light received at a second detector of the first subset of detectors by a nominal 90 degrees, and wherein the light received at a first detector of the second subset of detectors is spaced in phase from the light received at a second detector of the second subset of detectors by a nominal 90 degrees.   
     
     
         7 . A multifunctional instrument, comprising:
 a lidar system, including:
 a first laser source; 
 an interferometer; and 
 detectors; and 
   a control system, including:
 memory, wherein a machine learning algorithm is stored in the memory; and 
 at least a processor, wherein the processor is operable to execute the machine learning algorithm, wherein wind measurement data is collected by the lidar system and provided to the machine learning algorithm, wherein the algorithm operates to predict turbulence or provide navigation information from the wind measurement data. 
   
     
     
         8 . The multifunctional instrument of  claim 7 , further comprising:
 an accelerometer, wherein actual turbulence measurement data is collected by the accelerometer and is provided as an input to the machine learning algorithm.   
     
     
         9 . The multifunctional instrument of  claim 8 , wherein the machine learning algorithm is trained using correlated wind measurement data and actual turbulence measurement data. 
     
     
         10 . The multifunctional instrument of  claim 7 , wherein the interferometer includes at least first and second fields of regard. 
     
     
         11 . The multifunctional instrument of  claim 7 , wherein the interferometer includes:
 a first, forward looking field of regard;   a second, upward looking field of regard; and   a third, downward looking field of regard.   
     
     
         12 . The multifunctional instrument of  claim 7 , further comprising:
 an infrared camera.   
     
     
         13 . The multifunctional instrument of  claim 7 , wherein the machine learning algorithm is a deep neural network. 
     
     
         14 . A method of detecting turbulence in the atmosphere, comprising:
 making wind speed measurements along a series of angles centered around the direction of travel of an aircraft;   detecting turbulence experienced by the aircraft;   correlating the wind speed measurements to the detected turbulence experienced by the aircraft; and   training a machine learning algorithm using the correlated wind speed measurements and detected turbulence.   
     
     
         15 . The method of  claim 14 , wherein the machine learning algorithm is a deep neural network. 
     
     
         16 . The method of  claim 14 , wherein the wind speed measurements are obtained from a plurality of different ranges along the direction of travel of the aircraft. 
     
     
         17 . The method of  claim 16 , wherein the turbulence experienced by the aircraft is detected by sensors carried by the aircraft, and wherein correlating the wind speed measurements to the detected turbulence includes at least one of spatial correlation, temporal correlation, and amplitude correlation. 
     
     
         18 . The method of  claim 15 , further comprising providing an output from the deep neural network, wherein the output is a turbulence prediction, wind-aided navigation information and a suggestion to alter a flight parameter of the aircraft. 
     
     
         19 . The method of  claim 14 , further comprising:
 taking wind measurements along a plurality of look angles, wherein at least some of the look angles do not correspond to the direction of travel of the aircraft.   
     
     
         20 . The method of  claim 14 , further comprising:
 determining a strength of a return signal;   in response to determining that the strength of the return signal is low, increasing the range gate length along which the wind speed measurements are made.