Tag Archives: NAWCAD

Next Generation Jammer

The U.S. Navy’s first AN/ALQ-249 Next Generation Jammer Mid-Band (NGJ-MB) production representative pods arrived at the Naval Air Warfare Center Aircraft Division (NAWCAD) Patuxent River, Maryland, July 7.

From left: Kennie Martinez and Marc Dannemiller, Raytheon Intelligence & Space employees, unbox the first of two Next Generation Jammer Mid-Band fleet representative pods that were delivered to the Airborne Electronic Attack Systems (PMA-234) pod shop at Naval Air Warfare Center Aircraft Division, Patuxent River, Maryland, July 7 (U.S. Navy photo)

The two fleet representative test articles, which make up an NGJ-MB shipset, were delivered to the Airborne Electronic Attack Systems Program Office (PMA-234) pod shop where they will be used to complete the Developmental Test (DT) program and commence Operational Test (OT) that requires the use of operationally representative hardware and software.

Lieutenant Alexander Belbin, AEA project officer with NAWCAD’s Air Test and Evaluation Squadron (VX) 23 said he’s most looking forward to being able to test what the fleet is getting.

«We will test the pods for everything we expect to encounter in the fleet», said Belbin. «For example, the power they generate, the frequency range they operate in, and the effects we can achieve against expected targets across the spectrum».

The remainder of DT will be conducted by VX-23 and VX-31, located at the Naval Air Warfare Center Weapons Division, China Lake, California, and OT will be conducted by VX-9 at Naval Air Weapons Station China Lake. To date, NGJ-MB has successfully completed more than 300 hours of developmental flight testing and has more than 5,000 hours of chamber and lab testing using the Engineering Development Models that were designed specifically for DT.

NGJ-MB is part of a larger system that will augment and ultimately replace the legacy ALQ-99 Tactical Jamming System currently used on the EA-18G Growler.

Belbin said NGJ-MB’s increased power and capacity to target multiple systems will be significant enhancements over the ALQ-99.

«I have flown the Growler in the fleet and will eventually be going back. I may one day fly missions with the very pods that we will be testing for the first time», Belbin said.

The U.S. Navy will receive six shipsets from Raytheon Intelligence & Space, the original equipment manufacturer. Once the flight test program is complete, the pods will be sent to the fleet in conjunction with the first Low Rate Initial Production (LRIP) shipsets for Initial Operational Capability (IOC), which is scheduled for fall 2023.

«It is imperative we deliver this game-changing electronic warfare capability to the warfighter as quickly as possible», said Captain Dave Rueter, PMA-234 program manager. «Receiving the production representative pods allows us to finish the flight test program and ensure we have a reliable product for the U.S. Navy and our Royal Australian Air Force cooperative partners».

Transwing VTOL

PteroDynamics, an aircraft design and manufacturing company that develops innovative Vertical Take-Off and Landing (VTOL) aircraft, is on August 23, 2021 announcing it has secured a contract with Naval Air Warfare Center Aircraft Division (NAWCAD) to deliver 3 VTOL prototypes for the Blue Water Maritime Logistics UAS (BWUAS) program.

Transwing VTOL
Transwing design solves the most critical challenge facing VTOL aircraft: substantially greater range for any given energy source and payload weight fraction. This is critical to unleash the market opportunity for advanced air mobility

In 2018, Military Sealift Command and Fleet Forces Command identified a need for the United States Navy to develop a capability to autonomously deliver cargo with an Unmanned Aerial System (UAS) to and from ships at sea. Their analysis found that 90% of critical repair cargo delivered at sea by helicopters and V-22 Osprey aircraft weighed less than 50 pounds/22.7 kg. A VTOL UAS can fill this critical need and free the manned aircraft to perform other higher priority missions.

«We are honored to be selected for this important project», said Matthew Graczyk, PteroDynamics’ CEO. «This contract is the start of an important partnership, and we look forward to delivering the prototypes to NAWCAD».

PteroDynamics is a US-based aircraft manufacturer headquartered in Southern California

«This is an exciting milestone for our distinctive VTOL aircraft», added Val Petrov, PhD, PteroDynamics’ founder and CTO. «Our design is well suited for operations on ships where windy conditions and tight spaces challenge other VTOL aircraft during takeoffs and landings».

«Using unmanned, autonomous aircraft for delivery of these critical payloads is an important capability for the Navy to have», said Blue Water’s project lead, Bill Macchione. «The innovative design of PteroDynamics offers significant potential for both military and civilian missions».

Transwing aircraft fold their wings during flight to transition between rotorcraft and fixed-wing configurations

Artificial Intelligence

An engineer at NAWCAD is developing an Artificial Intelligence (AI) system with the potential to teach itself how to recognize and remove external interference from radar signals.

A J-UCAS aircraft body sits on a minimally reflective target pylon for radar cross-section testing at the National Radar Cross Section Test Facility (U.S. Air Force photo)
A J-UCAS aircraft body sits on a minimally reflective target pylon for radar cross-section testing at the National Radar Cross Section Test Facility (U.S. Air Force photo)

The AI system is an outgrowth of Ph.D. research into pulsars and mysterious cosmic signals called fast radio bursts conducted by the Atlantic Test Range’s (ATR) electrical engineer Stephen Itschner.

«I’m hoping it will help us automate a process that’s now very time consuming because we have to do it all by hand», said Itschner, who works with ATR’s Advanced Dynamic Aircraft Measurement System (ADAMS) group.

If successful, Itschner’s system will be integrated into ADAMS, which provides radar cross-section data from aircraft during flight tests.

«Radar cross-section is just a measure of how big a target looks to a radar», he said. «It’s more related to electrical size than to actual physical size. Radar signals bouncing back from an aircraft can be contaminated with external Radio Frequency Interference or RFI. It’s essentially the same as the static you hear on a radio when there’s lightning nearby. It can come from other radar sites, walkie-talkies, military radios, boat radios, even garage door openers».

When plotted on an x-y graph, RFI appears as sharp peaks throughout the radar signal, making it hard to tell what represents the true radar return from an aircraft and what is coming from unwanted external sources.

«Radar cross-section post-analysis is very labor-intensive», said Jim Ashley, head of ATR’s Aircraft Signature and Avionics Measurement branch. «We’re hoping Steve’s research will lead to an 80 percent solution – letting the machine do 80 percent of the work before we turn it over to our human analysts».

Itschner presented his initial results with a limited set of data at a meeting last week to the country’s top radar experts at the National Radar Cross Section Test Facility (NRTF) managed by Holloman Air Force Base near Alamogordo, New Mexico.

According to Itschner, his system achieved 80 percent correct RFI classifications with almost no false positives, that is, virtually no misidentification of true radar returns as RFI when using a «proof-of-concept» set of radar data from a Learjet. He trained the AI system on 90 percent of the Learjet data, then tested it against the remaining 10 percent which the system had not encountered before.

«I’ve gotten it to train and test well on one class of target», he said. «But I haven’t yet looked at whether that type of training will extend to, say, a helicopter or other type of jet».

Ashley said the ADAMS equipment is being upgraded to handle new, more complex aircraft programs that will require far greater data analysis capability. «It’s simply not going to be practical to continue using people to do all of it», he said.

«NRTF engineers at the conference have come to similar conclusions», Itschner said. «They independently found they’re going to have the same type of problem for a slightly different application and would need a solution similar to the one we’re working on. It gave me a nice warm feeling to know we’re on a promising track».

The similarities between Itschner’s work with radar and his Ph.D. research in radio astronomy led him to develop the artificial intelligence system, or machine learning, as he calls it. For his Ph.D. Itschner’s working on instruments and signal-processing techniques to identify fast radio bursts, which are very powerful but extremely brief eruptions of energy from deep space.

«They’re very mysterious signals and no one knows quite what they are», he said. «They only last for a millisecond and they’re completely unpredictable».

Itschner is looking for commonalities among fast radio bursts, radar and RFI in order to develop machine learning systems to analyze them.

He’s come up with a machine learning algorithm – a series of computer instructions – called a Convolutional Neural Network (CNN). The CNN is able to identify whether a piece of radar data is corrupted with RFI or not. In his astronomy research, he uses a neural network to determine whether data captured by a radio telescope comes from a fast radio burst or not.

«People can learn to see the difference without too much training, and CNNs are really, really good at mimicking human vision performance», he said. «To hand-design an algorithm that can see the same differences people can, an engineer traditionally would choose features that would help discriminate between objects – two types of fish for example. I would say, ‘let’s look at the length of the fish and the number of fins it has’. I’d just try different things and then build a system around that. But that traditional approach restricts the algorithm’s discriminating ability. Its accuracy is limited by the engineer’s imagination».

So instead of telling his algorithm to look for specific characteristics of a real radar return data versus RFI, Itschner lets the CNN figure them out for itself.

«All you do is give the algorithm a bunch of examples and an answer key that says what class each example really belongs to, and the machine is able to learn the difference on its own», he said. «Eventually it learns to make correct decisions on new data so that a human doesn’t need to examine it».

Itschner’s initial results are encouraging, Ashley said. «The next step is to buy hardware for the higher processing power needed to train the system for a wider range of radar data», he said. The equipment is expected to arrive at ATR in time to begin running AI training algorithms next month.

«We’re not sure yet if it’s the right way forward», he said, «but Steve’s work will help us narrow down how best to apply it to ATR».