Researcher Finds Way To Identify MIM-104 Patriot Battery Positions Using Sentinel-1 SAR Multi-temporal Imagery

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MIM-104 Patriot Battery positions can be identified using open source Sentinel-1 SAR Multi-temporal Imagery.

According to a report by Harel Dan, posted on October 22^nd, one of the most used is ESA’s Copernicus program Sentinel 1 pair of satellites, S1A and S1B, giving a combined average revisit time of 1.5 days in a best-case scenario.

This high resolution and high revisit time, as well as open access approach, provide essential data in various fields such as emergency response, marine monitoring, vegetation analysis, wildfire quantification, and urban planning.

Furthermore, the data can be freely downloaded and analyzed on many platforms, including Copernicus Open Data Hub, Sentinel EO Browser and Google Earth Engine.

The report points out that normal optical imagery can receive interference by clouds and dust, whereas radar images can mostly “see” through water vapor and other particulate matter. However, radars are susceptible to interference from other sources of radiation on the ground and in the air. They simply need to transmit at the same wavelength.

If you simply use one of the abovementioned platforms to free roam with the Sentinel-1 data you will encounter many types of interferences “blips, bloops, speckles, and waves.” The idea is to aggregate several images and thus create a smoother one, removing some, if not all of the interferences.

Examples of the interferences – Screenshots from Sentinel-Hub EO Browser

According to the report, these artifacts are the result of higher return signals. They have various polarizations, dimensions and locations. They have one thing in common – they will always have a main angle perpendicular to the satellite flight direction, thus having two distinct tilt angles based on the orbit type.

ESA, 2016

Harel Dan then attempted to correct some of the noise by using forms of image aggregation or multi-temporal analysis, where for each image pixel the lowest value is selected.

When the author used Google Earth Engine to perform the abovementioned. A combination of VH and VV polarizations were displayed. “These lines, the result of overlapping ascending and descending orbit interferences, consistently converge.”

These converging lines are AN/MPQ-53/65 phased array radars that form a Patriot missile battery C². Looking at official documentation, the military G-band is the civilian C-band. Sentinel-1 central frequency is 5.405 Ghz, well within this range.

Thus, anywhere in the world where this convergence is present there would be a patriot battery or other early warning system.

Harel Dan presented several additional screenshots from Google Earth Engine, as well as a zoom in of the convergence using Google Satellite imagery.

Ascending and Descending orbits converge over Al-Udeid Base in Qatar

Al-Udeid MIM-104 Patriot battery

Ascending and Descending orbits converge over Isa Airbase in Bahrain

Isa Airbase MIM-104 Patriot battery

There is also corroboration to the findings. Other GEOINT analysts, using the Strava Faux Pas, pinpointed Emirati Patriot deployment in Aden. This exact location is once again the exact center of the X.

Ascending and Descending orbits converge over Aden, Yemen

You can literally spend less than a minute on Stravas new data service and find sensitive sites. Nice patriot position you have there pic.twitter.com/eYS8TOuT0F

— Lost Weapons (@LostWeapons) 27 January 2018

There was also a screenshot of an Israeli patriot deployment which has since been redacted.

There is high confidence that these are the result of the AN/MPQ-53/65 Radar. There are, however, other sources of interference, such as the Swedish STRIL Array.

STRIL Array around Sweden

One of the STRIL locations in Sweden

All of the the above screenshots are from Harel Dan’s GEE script.

“The script collates a defined time span of images, performs the necessary filters, and displays the result. The longer the time span, the more “noise” gets added to the result, and the convergence lines become more robust. Theoretically, if you limit the time span, one could infer deployments of forces based on the time the convergence appears and disappears, give or take a few days.”

The democratization and easy diffusion of EO data is clearly presenting new challenges to organization and governments that deal in classified information.

It is also interesting to note, in regard to the abovementioned Strava Faux Pas. Earlier, in January 2018, a fitness tracking app by Strava that records the movement of its users accidentally revealed sensitive information about the location and staffing of military bases and spy outposts around the world.

In November 2017, Strava released a data visualization map that shows all the activity tracked by users of its app, which allows people to record their exercise and share it with others.

Strava’s suggested solution to the military was simple: “opt out” of the heatmap and the classified information would possibly not be accidentally revealed.

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