Itt Manufacturing Enterprises, Inc.

Douglas Lent Cohen

David James Crain

Donald David Gregory

John B Abbiss

Richard J Hertel

Peter Michael Mantica

Stephen Charles Cain

David Stanley Smith

An optical system measures scene inhomogeneity. The system includes a mirror for receiving radiance of a field-of-view (FOV) of a scene, and reflecting a portion of the radiance to an optical detector. A controller is coupled to the mirror for changing the FOV. The optical detector provides a signal of the reflected portion of radiance of the scene. A processor determines scene inhomogeneity, based on amplitude of the signal provided from the optical detector. The controller is configured to modulate the FOV at a periodic interval, using a sinusoidal waveform, a pulse code modulated waveform, or a pseudo-random waveform.

Using a fixed-frequency oscillation to detect and measure scene inhomogeneity

A device for calibrating a sensor using solar radiation includes a sensor configured to measure electromagnetic radiation received through a field of view (FOV) having a normal line of sight and at least two baffles removably insertable across the FOV of the sensor and inclined to the line of sight. Each baffle has first and second opposing surfaces, with the first surface disposed to face the solar radiation and the second surface disposed to face the sensor. One of the first or second surface is configured as a diffused surface, and the other of the first or second surface is configured as a specular surface.

Use of solar illumination and baffles to calibrate visible sensors

A method for measuring scene inhomogeneity includes the steps of directing radiance of a scene into a dispersive spectrometer, and changing the field-of-view (FOV) of the spectrometer, while directing the radiance of the scene into the spectrometer. The method then processes the radiance of the scene to obtain a signal. The method also includes measuring an amplitude of the signal and determining scene inhomogeneity based on the measured amplitude of the signal. The method may include uniformly oscillating the FOV of the spectrometer and, next, obtaining a sinusoidal signal, based on uniformly oscillating the FOV of the spectrometer.

Using a fixed-frequency oscillation in a dispersive spectrometer to measure scene inhomogeneity

A method for measuring scene inhomogeneity includes directing radiance of a scene into an interferometer; and oscillating a field-of-view (FOV) of the interferometer, while directing the radiance of the scene into the interferometer. A Fourier transform of signals emerging from the interferometer is obtained with magnitude values of the Fourier transform as a function of wavelength. The magnitude values are separated into (1) component values occurring within a predetermined wavelength band of the interferometer and (2) a component value occurring outside the predetermined wavelength band. The component value occurring outside the predetermined wavelength band is used to measure scene inhomogeneity.

Using a fixed-frequency of oscillation in an FTS system to measure scene inhomogeneity

A method of measuring a field-of-view (FOV) of an optical sensor includes the steps of: (a) uniformly illuminating a FOV of the optical sensor; (b) moving, at an angle of motion, a slit across the FOV and illuminating the optical sensor through the slit; (c) measuring an optical response of the sensor through the slit, at evenly spaced positions of the moving slit; (d) changing the angle of motion and repeating steps (b) and (c); (e) repeating step (d) for equally spaced angles of motion; and (f) constructing a two-dimensional map of the FOV of the optical sensor, after completing step (e). Constructing the two-dimensional map of the FOV includes filtering the measured data using a filtered backprojection algorithm, and interpolating among the filtered measured data to form the two-dimensional map.

System and method of measuring field-of-view of an optical sensor

In an optical system having a detector means and processor means in which imaging data is obtained comprising noisy blurred scene data containing an object to be reconstructed, and noisy blurred background data of the same scene, a method for increasing the spatial resolution of the imaging data produced by the optical system, comprising the steps of converting the imaging data into a first matrix, regularizing the first matrix by performing nth order Tikhonov regularization to the first matrix to provide a regularized pseudo-inverse (RPI) matrix and applying the RPI matrix to the first matrix to provide a reconstructed image of the object.

Super resolution methods for electro-optical systems

There is disclosed in an optical system having a predetermined Numerical aperture which provides a corresponding level of spatial resolution and having detector means and processor means in which image data is obtained comprising noisy blurred scene data containing an object to be reconstructed, and noisy blurred background data of the same scene, an improved method for increasing the spatial resolution of the imaging data produced by the diffraction limited optical system. The improvement comprises filtering the noisy blurred background data of the same scene to obtain noise suppressed data; applying estimations of point spread functions associated with the noise suppressed data and optical system to estimates of the noise suppressed data to obtain a reconstructed background image (I,(x)); and low pass filtering the noisy blurred scene data containing the object to be reconstructed (D,) and using the reconstructed background image (I,(x)) to eliminate the background data from the image data to obtain a reconstructed image of an object with increased spatial resolution.

A method of estimating expected errors of environmental data parameters based on radiance measurements obtained from visible infra red radiometric satellite sensors (VIIRS) orbiting the earth, comprising the steps of: obtaining N radiance measurements of a surface body I.sub.I, . . . ,I.sub.N defining matrix I depending on p unknown surface and atmospheric parameters T.sub.i, . . . T.sub.p, defining a matrix T; generating a forward model I=f(T) for obtaining the I radiance measurements from the p parameters; choosing an initial set of values for the p parameters and linearizing f(T) about the initial values to obtain a linearized forward I=s+H.theta. as f.sub.i (T.sub.01, T.sub.02, . . . T.sub.0p)+.SIGMA.H.sub.ij .theta..sub.j where I=1,2, . . . N and .theta..sub.j =T.sub.oj and H.sub.ij =.delta.f.sub.i /.delta.T.sub.j and where .theta. is a column matrix .theta..sub.l, . . . .theta..sub.p and H is a matrix of H.sub.ij values; adding measurement noise vector w of noise values to the forward model; determining the covariance of the measurement noise w to obtain a covariance matrix C; and manipulating the matrices H and C according to the equation C.sub.EDR =(H.sup.T C.sup.-1 H).sup.-1 to obtain matrix element C.sub.EDR indicative of the expected errors in the values of T.sub.1, . . . T.sub.p parameters.

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Fort Wayne, IN, United States of America

Douglas Lent Cohen