NY Times UFO Explained

The rotating Gimbal UFO promoted by The New York Times is here shown to be a nearly perfect match to the rotating hot-engine exhaust of a jet recorded on the very same ATFLIR gimbal-camera system. Minor differences are shown to be attributable to circumstantial differences such as distance.

If the contents seen in grainy footage alleged to be extraterrestrial (ET) technology are even fairly similar to human technology albeit not perfectly discernible, they are not evidence of ET technology. In other words, if we cannot rule out the ordinary we have no grounds to propose the extraordinary. Yet the similarity to human technology in the Gimbal Video exceeds a ‘fairly similar’ criterion. The Gimbal Video therefore presents no evidence of ET technology, it’s just ordinary evidence of an ordinary phenomenon, jets.

As I reported in my last video, the ATFLIR-induced jet-engine-rotation hypothesis was first proposed by Mick West @ and, no less, on the very same day the NY Times published the footage!

The New York Times report:

Comparator jet video is @  and is a Russian Su-27 imaged on June 18, 2017 over Syria by an ATFLIR infrared camera on a Navy F-18. The same camera that shot the gimbal UFO video. More about that incident where an F-18 shot down a Russian Su-22 is @ and more about the Su-27’s presence during the incident is @ The ATFLIR footage of the Su-27 switches from black=hot to white=hot midway, but I invert it to keep it black=hot consistently for the comparison, which is perfectly reasonable since the settings difference is nothing but an inversion.

Rotation of hot flare recorded by an ATFLIR infrared camera on a Navy F-18 fighter jet is @

The ATFLIR has three Field of View (FOV) settings of which NAR, or narrow field of view, is 0.7˚, as given @ Additionally, the screen data shows the zoom (Z) setting is 2.0, so that on top of an FOV of 0.7˚ gives a 0.35˚ FOV.

The ATFLIR paper cited is @

Excerpted quotation therefrom: “Another challenge our tracker faced was image rotation. While our inertial stabilization maintained the sensor fixed on the designated target as the aircraft maneuvered, the image on the display was optically rotated for the operator in order to achieve the effect of looking out the cockpit window. As the aircraft maneuvers, therefore, the image that the tracker receives rotates accordingly. While the centroid track algorithm is able to compensate for the rotation with relative ease, the correlation track algorithm relies on a recursively smoothed reference map to compute track errors. If the scene is rotating, the reference map will smear and degrade(2) . To resolve this issue, we needed to de-rotate the input image before it was smoothed into the reference map. We accomplished this by passing the rotation angle from the Servo Controller to the Image Processor (via the Command and Data Processor), and then de-rotating the input image with respect to the reference image during computation of the track metrics.”
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