Simulations are meant to teach airmen about these new weapons and help the Services develop new tactics and procedures.
Supply/Fire support teams can operate without having to deploy artillery units out to the field or have tank hulls to shoot at, they can put on those goggles and they can send a call for fire. It will insert targets, potentially even moving targets – which we typically don’t get, we’re usually just shooting at old rusty tank hulls that are sitting on the ground.
In this particular Warfighter exercise, the division observed that the terrain limited options for ground maneuver. A single penetration, though conservative and often effective, would not achieve the commander’s intent. The penetration presents the enemy with one problem—a problem that other units have presented repeatedly. Dilemmas are not the same as problems. A problem is a situation regarded as unwelcome or harmful that must be dealt with and overcome.
A dilemma, by contrast, is a situation in which a difficult choice has to be made between two or more alternatives, especially equally undesirable ones. To present the enemy with multiple dilemmas across multiple domains and in multiple locations, the division combined penetrations with audacious turning movements and tactical deceptions, complemented and reinforced with nonlethal effects.
The turning movements were achieved by conducting air assaults across the coordinated fire line and up to the fire support coordination line. To avoid enemy air defenses, these air assaults were often offset by several kilometers and at least a major terrain feature away from their intended target.
The targets were often key points of overwatch for particular underground facilities suspected of housing long-range artillery, or points of domination that could cover major avenues of approach. Timely execution of these air assaults forced the enemy to divert resources and attention from the advance of our armored formations along heavily defended avenues of approach and thereby dislocated the main enemy defenses.
In the cases where we were successful, the division forced the enemy to react to our operations and enter the fight on our terms. More importantly, we were able to achieve tempo not just through the sustained geographical advance of the forward line of troops.
By persistently presenting complementary dilemmas to the enemy in unexpected ways to diminish adversaries decision space and disrupted their understanding of its own plan. By the time the enemy observed and oriented on one dilemma, the division sought to present another, thereby causing the enemy to not render a decision on the initial dilemma.
Sustaining both momentum and tempo against a capable enemy required the division to reframe how we achieved integration of digital battlespace during sustained and dynamic combat operations. Too often our decision-making process in combat operations mirrors the activity of a football team on the gridiron.
In the midst of a long offensive drive, we seek to impose periods of planning i.e., the huddle, an approach march, a decisive operation where synchronization is optimized, and a culminating point that leads into a period of disengagement and another planning session.
This “battle period” model, thankfully obsolete at our brigade training centers with the advent of open phasing, is equally inappropriate in a Warfighter exercise. Instead, we needed to think like a rugby team, where synchronization occurred rapidly and unexpectedly with fleeting moments of opportunity quickly identified and exploited by individual players who then become the supported effort as the team synchronizes around them.
This required a different and more dynamic approach with near/long term tactical planning and targeting cycles all occurring constantly as conditions changed on the ground.
Every so often, after a weapons system is fielded, we’re bringing it back into the building and reviewing all of those integrated product support elements. What did the Marines demand in terms of design work and performance? How did the system perform in tests? And how’s it actually proving out in the operational theatre.
Through the simulations and wargames, the Services hopes to educate airmen on what to expect, as well as learn more from them about how the military should be adjusting to fight with and against these weapons.
“Distributed wargames provide a method of working with warfighters to develop tactics, techniques and procedures—TTPs—and concept of employment—CONEMP—to utilize these new technologies to meet the warfighter needs and gaps.
Program must develop digital models that run at accelerated speeds while maintaining real-world conditions and results. The digital models should use artificial intelligence and machine learning to improve over time.
Using those digital models, simulations will be developed for airmen to train on, including scenarios that enable users to mix and match tools, techniques and resources to different effects.
Digital tools should collect data and return results on trainee performance, as well as the fidelity of the models and simulations to real-world situations and physics.
Participate in mock digital missions to further research into future weapons. The evaluations will look to “identify what questions need to be answered, what modules are required, what simulations need to be run for specific missions and what analyses need to be performed to answer digital mock-up questions.”
All this culminates in “distributed wargames” that will be used to determine whether emerging technologies are ready to be transitioned to the battlefield. Services expected to “facilitate and participate in” these events, as well as manage the upkeeping and development of the platform.
While the concept of operations for digitally driven autonomous vehicles are still very much under development, the general idea is the vehicles could expand not only the fleet’s sensor reach by adding more nodes to provide data to commanders but also deepening the fleet’s magazines by fielding additional missile cells that could fire on remote at the direction of a manned vehicle.
It is now possible to deploy a multi-sensor intelligence, surveillance and reconnaissance [ISR] capability thousands of miles from its home base. With the only requirement being a small team of technicians on the deployment field, there’s no longer a need to dismantle the aircraft and ship the entire system. This facilitates the availability and initial ISR capability in emergency missions.
Autonomous vehicles can be equipped with an airborne detect and avoid system that includes an air-air radar and a traffic collision and avoidance system that offers a significant alternative to the traditional rule of see and avoid.
The redundancy of the primary beyond line of sight BLOS link with a secondary satellite link operating in another frequency band ensures the continuation of the mission by permanently maintaining the piloting capabilities, even in the event of interference. Satellite data links are used to control the vehicle, operate on-board sensors, and disseminate the ISR data collected from the aircraft to the cockpit.
The disconnection of this link, although rare, reveals a true weakness, especially when the aircraft operate in a non-segregated environment or during bad weather. However, with a second satellite link, the aircraft will now remain in control of the remote pilot and will either continue its mission safely or land without issue.
Modern autonomous vehicles will allow more digital modular sensors to be integrated according to customer needs. The ISR omni-role platform will be plug-and-play and “sensors agnostic.” As aircraft allow for constant monitoring of a target and its digital battlespace, it is necessary to capitalize on that through the modularity of sensors ideally without hampering endurance.
The challenge is to provide the operator with the opportunity to be able to quickly obtain integration of his own weapons and sensor suites. This flexible plug-and-play capacity to perform missions with a wide variety of sensors will be a considerable step forward.
Services have tested a digital robot kit that can turn virtually any plane into a self-piloting drone, through a program called ROBOpilot. Systems interacts with flight controls just like a human pilot, pushing all the correct buttons, flipping the switches, manipulating the yoke and throttle and watching the gages.
“At the same time, the system uses sensors, like GPS and an Inertial Measurement Unit --essentially a way for a machine to locate itself in space without GPS for situational awareness and information gathering. A computer analyzes these digital details to make decisions on how to best control the flight. Once the flight is done, the kit can be pulled out and the plane reconverted to one requiring a human pilot.
Small drones are becoming a big problem. Here’s how next-generation digital networking techniques could help.
Pentagon has been highlighting the difficulties of fending off small unmanned aircraft. The farther away you can spot them, the better. But as drones get smaller, detecting at distance isn’t easy.
It’s now possible to detect incredibly small disturbances in radar returns that could indicate the presence of small drones, perhaps as far as three kilometers away — enough to give militaries a big hand in stopping them.
An active electronically scanned array, or AESA, radar is paired with a digital networking tool. AESA radars, which steer its multiple radar beams electronically instead of using physical gimbals, have been around for years. The real innovation lies in training digital tools to detect objects, including objects in radar imagery.
But there’s very little imagery data to train a machine learning algorithm on how to see something that small. What’s needed is a dataset of extremely small modulations in the echoes of radar signals.
Team turned a small bit of available training data into an abundance by pitting “Digital Twin” networks against one another. For instance, one network might learn how to recognize an object by looking at many slightly varying examples. The second network reverses that process.
So if a conventional network learns that a certain combination of white pixels against a dark background represents a particular objects, the system starts with the finished image and then learns about the combination of white or dark pixels that the first network to its determination.
As the second network does its work, it creates slightly varying versions of the data — which themselves can be used for training. That’s how the researchers turned their small mini-Doppler dataset into something robust enough to be useful.
“To train a deep digital network, we need to use a large training data set that contains diverse target features. If the data is lacking, then an overfitting issue occurs. The system was used to augment the data set by producing fake data that have similar distributions with the original data.
“It is reasonable to say that our system can ‘detect’ a drone more than 3km, However, it will not be a fact if we say we can identify a drone with the help pf this system at this point. The fact is that we have constructed a platform/idea to be used for the classification, but diverse tests are needed to be done in the future.”
Modern networked logistics systems would also go a long way in helping warfighters be more efficient. In the longer term, that could look like a digital barcode system that in real time tracks people and goods on the battlefield.
Since significant levels of loitering time is common for helicopter lift time, a clearer sense of where people and things are could cut down on that.
If you can track everybody moving around the battlefield with a networked system that cant be compromised you could create digital movement tables for people and cargo nearly real time.”
Simulations must consistently implement mandated system digital security controls for safeguarding operational information.
Requirements include digital controls for user authentication, user access, media protection, incident response, vulnerability management, and confidentiality of information.
Security incidents included unauthorised access to mission-critical networks, stolen equipment, such as laptops and cellular phones; inadvertent disclosure of information; data exfiltration; and the exploitation of network and system vulnerabilities.
We identified deficiencies related to:
1. Using multifactor authentication
2. Enforcing the use of strong passwords
3. Identifying network/system vulnerabilities
4. Mitigating network/system vulnerabilities
5. Protecting info stored on removable media
6. Overseeing network and boundaries
7. Configuring user accounts to auto lock after extended periods
8. Implementing physical security controls
9. Creating and reviewing system activity reports
10 Granting system access based on user’s assigned duties.