Atmospheric channel characterization system and method using target image information
A system and method involve transmitting, using an optical device, a plurality of optical pulses into an atmospheric propagation channel towards a target object. Using an imaging device, more than one optical signals and an image of the target object are detected from the atmospheric propagation channel. The optical signals are produced by interaction between the transmitted optical pulses and the atmospheric propagation channel and include elastic backscatter return signals, inelastic backscatter return signals, and polarization signals. A processor simultaneously processes the elastic and inelastic backscatter return signals, the polarization signals, and information contained within the image of the target to determine estimates of one or more physical parameters of the atmospheric propagation channel. The processor uses machine learning algorithms to enhance/restore the image of the target, to perform pattern-recognition and classification of the target, and to extract additional atmospheric propagation channel physical characteristics.
1. A system comprising:
an optical transmitter subsystem configured to transmit a plurality of hyperspectral optical pulses into an atmospheric propagation channel, the optical transmitter subsystem comprising a plurality of optical transmit channels each tuned to a separate wavelength and transmit optical components configured to combine the output of the optical transmit channels to produce the plurality of hyperspectral optical pulses;
an optical receiver subsystem configured to detect optical signals from the atmospheric propagation channel, the optical signals produced by interaction between the transmitted hyperspectral optical pulses and the atmospheric propagation channel, the optical receiver subsystem comprising a plurality of optical receive channels and receive optical components configured to direct the detected optical signals to separate optical receive channels, depending on at least one of wavelength and polarization;
a storage device, operatively connected to the optical receiver subsystem, configured to store the output of the plurality of optical receive channels; and
a processor, operatively connected to the storage device, configured to retrieve the stored output of the plurality of optical receive channels from the storage device and determine estimates of the optical turbulence parameter of the atmospheric propagation channel using the retrieved output, wherein the processor is further configured to use the estimates of the optical turbulence parameter of the atmospheric propagation channel to perform blind image deconvolution and restoration using an inverse Werner filter of a target image obtained from a target within the atmospheric propagation channel.
2. A system comprising:
an optical transmitter subsystem configured to transmit a plurality of hyperspectral optical pulses into an atmospheric propagation channel, the optical transmitter subsystem comprising a plurality of optical transmit channels each tuned to a separate wavelength and transmit optical components configured to combine the output of the optical transmit channels to produce the plurality of hyperspectral optical pulses;
an optical receiver subsystem configured to detect optical signals from the atmospheric propagation channel, the optical signals produced by interaction between the transmitted hyperspectral optical pulses and the atmospheric propagation channel, the optical receiver subsystem comprising a plurality of optical receive channels and receive optical components configured to direct the detected optical signals to separate optical receive channels, depending on at least one of wavelength and polarization;
a storage device, operatively connected to the optical receiver subsystem, configured to store the output of the plurality of optical receive channels; and
a processor, operatively connected to the storage device, configured to retrieve the stored output of the plurality of optical receive channels from the storage device and determine estimates of the optical turbulence parameter of the atmospheric propagation channel using the retrieved output, wherein the processor is further configured to use the estimates of the optical turbulence parameter of the atmospheric propagation channel to perform blind image deconvolution and restoration using an inverse Werner filter of a target image obtained from a target within the atmospheric propagation channel, wherein the processor is further configured to use the machine learning algorithms on information contained within the target image to perform pattern-recognition and classification of the target based upon the optical turbulence parameter of the atmospheric propagation channel.