Video: Raytheon Engineers Develop New Infrared-Acoustic Sensor to Stop RPGs & ATGMs
By Kris Osborn - Warrior Maven
(Washington D.C.) Traveling at supersonic speeds, a bullet exiting a gun barrel generates acoustic “shock waves” propagating through the air from the tip of the projectile, producing a sound “signature” which can be detected by specially engineered sensors, according to Raytheon BBN engineers.
This technical process, simply put, saves lives…. as it enables soldiers to instantly know the exact location of incoming enemy small arms fire, offering an opportunity for a precise and lethal counterattack amid high-intensity combat. A technology which does this, made by a Raytheon subsidiary called BBN, already exists and has been deployed with U.S. Army soldiers. It's called Boomerang, and a set of six different sensors can instantly find the source of incoming bullets from moving vehicles and stationary locations.
“The way a shock wave works is it generates and propagates at the speed of sound. While the bullet is moving, a stream of waves comes off the tip of the bullet that propagates through the air. From six sensors I can locate exactly where it came from,” Brad Tousley, President at Raytheon BBN, told Warrior in an interview.
The Next Frontier...What about tracking anti-tank missiles and RPGs?
What if this kind of technical application could be used to track the source of even larger, more dangerous enemy attacks from RPGs and even anti-tank missiles? Taking the success of Boomerang to the next step to accomplish this is now the focus of Raytheon BBN to advance the mission functionality of source detection sensing.
The concept, now underway and being demonstrated by Raytheon BBN, is to engineer a multi-mode sensor able to synthesize acoustic detection with infrared detection to precisely identify the sound and heat signature of various forms of hostile fire such as attacking anti-tank missiles and RPGs. RPGs and anti-tank weapons naturally generate a larger heat signal than small arms fire, and some of them operate at very long ranges….much farther than small arms fire in some instances. Merging acoustic and infrared detection, therefore, could compare or analyze an acoustic signature in relation to a heat emission to offer new levels of precision tracking.
“Some in the market are focused on acoustics only and others do infrared. We are doing R&D to integrate our infrared technology with acoustic sensing,” Tousley said.
Merging, analyzing and organizing otherwise disparate pools or streams of sensor data, such as those generated by infrared or acoustic sensors, aligns in a seemingly optimal way with ongoing AI and Machine Learning applications.
“Almost everything we are doing is being threaded with AI and ML to make our current systems even better,” Tousley explained.
Advanced, AI-enabled algorithms can now, in near real time, discern multiple streams of sensor data, feed them into a vast database and instantly perform analyses, answer questions and generate possible solutions. Perhaps an infrared signature offers higher fidelity in a particular kind of attack? Better yet, perhaps renderings from a series of small acoustic sensors can combine with infrared signals to bring unprecedented levels of precision to a long-range incoming missile attack from miles away? What if an attacking round fires from a weapon which generates a heat signature much larger and more distinct than an acoustic signal? Or, a weapon might also be engineered with some kind of advanced IR suppressor and, by contrast, only generate a detectable acoustic signature? If an attacking weapon generates both, and the data is synergized, then perhaps detection can reach previously unavailable levels of precision.
Analysis can only be as effective as the database it operates with or draws from, a circumstance which explains why AI-empowered information systems can perform procedural searching and data-mining functions in seconds. All of this can be done by bouncing new, arriving information off of or against a seemingly limitless data repository. Not only that, but the computers can draw upon previous scenarios and analyze a number of otherwise disconnected, yet highly relevant variables in relation to one another such as wind speed, terrain, climate or the trajectory of an incoming projectile. This is where Machine Learning can bring new dimensions, as it involves a technical effort to perform near real-time analytics upon newly arriving, yet previously unrecognized information. Advanced algorithms can quickly integrate, analyze and help identify new details with the aim of placing them in context for presentation to a human decision-maker.
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Kris Osborn is the new Defense Editor for the National Interest. Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army—Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel, and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.