Aguadilla Infrared Footage of 'UFOs' - Probably Hot Air Wedding Lanterns

Mick West

Administrator
Staff member
A new analysis, published by 3AF (also has some minor descriptions of the Chilean and US Navy cases)
https://www.3af.fr/global/gene/link.php?doc_id=4234&fg
Section start on page 48. PDF is in French, I attach an auto-translated version

They consider three hypotheses that match the data - an object close to the ground/ocean (the SCU hypothesis), a slow-moving object close to the airport at around 1,000 feet (the mundane hypothesis), and an object following the plane (the "just for completeness" hypothesis). They narrow it down to the first two.

 

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jarlrmai

Active Member
Hard to tell if it's that translation but the Go Fast analysis seems a bit off.

Object is not hot, it's cold and they seem to ignore parallax and camera rotation to determine a fast object, they do seem to get the altitude correct.

Go Fast is a good litmus test of analyses as it has relatively easily determinable factors that are counter to the initial conclusions one might have based on the video alone.
 

sakurashinken

New Member
I noticed in the SCU report that the Airforce conveniently refused to give them radar data from the airport itself. It seems like this case remains conveniently ambiguous until we can get that radar data (if indeed the unit was capable of resolving such a small object). As usual, the organizations that are supposed to serve us aren't transparent and allow the myth to live on in the murky low information zone created by classification of information.
 
I noticed on the spec sheet for the MX-15D camera that the thermal imager has several fixed fields of view: 26.7°, 5.4°, 1.1°, and 0.36°.

(Low-res image from the SCU pdf)

The last three settings correspond to the focal length figure at the top centre of the video overlay - which switches between 135, 675 and 2024. Assuming those are horizontal fields-of-view, the maths works out for a 16mm 4:3 sensor (12.8mm x 9.6mm), which is not an uncommon size.



I was able to verify this by taking a measurement across the "peaked building" in this frame of the video - it's 311px from the left hand edge of the sloping roof, to the right hand edge of the flat roof adjacent:


The image resolution itself is 704x480, which seems to be some old NTSC CCTV format. The YouTube 480p has small black bars.


So the building's width occupies 311/704 of the image's horizontal pixels.

Taking a real-world measurement in Google Earth, the building's width is 34.5m, near enough:


So the horizontal extent of the image at the distance of that building is (704/311) * 34.5m.

Punching in the GPS coords from that video frame shows that the camera was 4054 meters away:


Also, the camera aircraft's altitude is shown as 2208ft, which is 673m, and the building's roof is 68m above sea level.


So the total distance via Pythagoras is 4099m. Dividing the horizontal extent of the image by this distance gives the tan of the angle of view. Here are my workings:


So it looks like the calculated FOVs for a 16mm sensor are bang on, and the ones in the brochure are rounded down to 2 significant figures.

135 = 5.43°
675 = 1.09°
2024 = 0.362°

The next step is to pick some candidate frames from the video, compare with Flarkey's KML to calculate the distance from the camera to the hypothesised path, measure the object's width in pixels, and see if these two values stay in agreement. Vertical FOV could be used too.

Notably, that the SCU couldn't get this to work:
Calculations of the object's size were done on multiple frames whenever the object was a known distance from the ground which allowed accurate values of the object's distance and its angular size. These values varied significantly from a minimum size of 3.0 feet to a maximum size of 5.2 feet. The variation in size is believed to be due to either varied angular sides of the object as it is tumbling or temperature variations that are reflected in the shape that the object presents to the IR camera.
Or it could be because their distance measurements are wrong due to misunderstanding parallax.

It's a bit hard to say whether this will work, since extremely long lenses flatten out perspective dramatically. There may not be enough discrepancy between the SCU's path and the straight-line path for the numbers to falsify one or the other, especially with such low quality video to take the pixel measurements from.
 

flarkey

New Member
I think the problem we'll find here is that the the range to the object and the object's size are the two unknown variables in this. And the number of pixels that the object appears as is dependent upon (guess what!?!) the objects range and size. The SCU got over this problem as they knew the exact position of the object "when it went in the water" !! This is where they initially went wrong. They stated that the object was 'conclusively shown to be between 3 and 5.2ft" - not very conclusive!

Am i right in what you're thinking, we could check the size and range against my suggested line and then the size should remain much more constant....?
 
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Am i right in what you're thinking, we could check the size and range against my suggested line and then the size should remain much more constant....?

That's the idea, yes. The distance from the camera to the object is the 'adjacent' side on a right-angle triangle where the 'opposite' is the object's unknown width. At the 675mm focal length, the angle theta can be found by counting the object's pixel width and multiplying by (1.09/704). (Since the horizontal FOV is 1.09º, each pixel has an angular size of 1.09º/704 - or use the advertised 1.1º if preferred, we're well into small angle approximation territory anyway).

Then the solution is: opposite = adjacent * tan(θ).


(B in this example is the 3D distance from camera to hypothesised object location, A is the object's resulting width).

Since tan θ ≈ θ very closely for these small angles, this is basically saying that (object width in pixels * distance from camera) should be constant - and the same is true for its height in pixels. The tough part would be consistently measuring a fuzzy blob that's less than 10 pixels wide in a low-grade video. And if the object isn't rotationally symmetric (e.g. those heart-shaped lanterns), the width won't be consistent from all viewing angles anyway (although the height ought to be).
 
Quick example using this frame, corresponding to ACFT2 / TGT2 in the KML:



Horizontal distance to intercept the model path is 2102 m:


Height where camera vector intersects the path is 270 m:



Aircraft height is 1807ft or 550.8 m, and object pixel width is... well... 13-ish? Maybe 12?


It's possibly a good idea to measure across 3-4 subsequent frames and take an average pixel count.

Anyway, this gives an object width of 75+/- 5cm depending on measuring 12-14 pixels.


Meanwhile, if we believe the object is skimming the ground further along the camera vector, the size works out as 125+/- 10cm:


That's a decent discrepancy, but I'd have to check the SCU model to see where exactly they believe the object is at that moment. Then repeat for a few frames with the camera at different distances.

EDIT: Here's another for the ACFT 5 point, it's consistent:



Here's the SCU's "Speed of object at known positions" table, which should probably be the next line of enquiry, since all the data is there to reconstruct the calculation for each (video timestamp, putative object coordinates and altitude).


(The asterisks mean either "in water" or "in water and air", so effectively at 0m altitude).
 
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flarkey

New Member
this is the best i can do with the SCU's flight path. The kml also includes their radar data, and overlay of their timings at each location. Taking the speed data from this will also give more points of reference.

kml link
 

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this is the best i can do with the SCU's flight path. The kml also includes their radar data, and overlay of their timings at each location. Taking the speed data from this will also give more points of reference.

kml link

Yeah I tried a few of their points and found some of their co-ords don't even put the object in the frame at the supposed timestamp. Interestingly the last few are on the 2204 focal length, so a larger number of pixels are covered by the object. Unfortunately all of the plottable SCU points are where the object is relatively close to where the straight-line model predicts, and the camera is far away - meaning the effect of the discrepancy is lessened. The effect would be more pronounced earlier in the video when the camera is nearer to the hypothetical path of drift.

After some quick trials I've found the calculated object size using the SCU objects locations varies by about 12%, while using the straight-line locations it varies by about 4%. But it's from from watertight so far.
 
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