En los \u00faltimos conflictos como Nagorno- Karabaj y otros, se ha ratificado la importancia que tienen en la decisi\u00f3n de la batalla, los UAS actuando en multiplicidad de funciones tanto de ISR como letales. Esto ha motivado, que las principales potencias como EUA, China y Rusia,\u00a0impulsen la \u201ccarrera\u201d por la entrada en servicio de sistemas aut\u00f3nomos cada vez m\u00e1s sofisticados y letales. La tendencia observada: m\u00e1s autonom\u00eda (full autonomy), menor dependencia del operador humano (\u201cMen out-of the\u2013loop\u201d) y los \u201cenjambres de drones\u201d operando sin dependencia de sistemas GPS. Todo ello genera\u00a0 tambi\u00e9n grandes desaf\u00edos para los sistemas de defensa \u201canti drone\u201d.<\/p>\n
As drones become smarter, cheaper, more nimble, easier for rogue adversaries to acquire and more advanced adversaries to evolve, they pose a unique threat for the\u00a0U.S.\u00a0military that grows in importance as the objects themselves diminish in size. This year, trends in autonomy will reshape drone capabilities and concepts, making them more offensively useful and even harder to defend against.<\/p>\n
\u201cDrones and most likely drone swarms are something you\u2019re going to see on a future battlefield\u2026I think we\u2019re already seeing some of it,\u201d said Army Gen.\u00a0John Murray, who leads Army Futures Command. \u201cCounter drone, we\u2019re working the same path everybody else is working in terms of soft skills and hard kills via a variety of different weapons systems. It just becomes very hard when you start talking about swarms of small drones. Not impossible but harder.\u201d<\/p>\n
The\u00a0U.S.\u00a0military plans to spend $83 million this year to buy lasers, electromagnetic devices, and other means to take down small drones. By year\u2019s end, the destroyer Preble will\u00a0get<\/a>\u00a0a 60-kilowatt laser and an optical dazzler, while the\u00a0Air Force\u00a0will deploy a\u00a0Tactical High Power Microwave Operational Responder<\/a>, or THOR. But the Pentagon will spend $404 million \u2014 almost four times as much \u2014 to develop new anti-drone defenses, the\u00a0Congressional Research Service\u00a0reported<\/a>\u00a0Jan. 11.<\/p>\n Future counter drone efforts will be coordinated by the year-old Joint Counter Small Unmanned Aerial System Office, or JCO, which released its\u00a0first strategy document<\/a>\u00a0on\u00a0Jan. 7. The office was established after individual services had spent \u201ca couple billion dollars\u201d to develop and deploy counter-drone tech, Army Maj.\u00a0Gen. Sean Gainey, who leads the 60-person JCO, told a CSIS audience recently.<\/p>\n Such efforts managed to field a few systems, like the Marine Air Defense Integrated System that the\u00a0Navy\u00a0used in\u00a0July 2019\u00a0to down an Iranian drone. (The system was mounted on a truck on the deck of the USS Boxer.) But the services\u2019 hurried, disorganized efforts produced \u201cseveral redundant systems\u201d and \u201cnot all of it worked as advertised,\u201d Gainey said. Even the best and promising solutions couldn\u2019t meet their fullest potential in such an environment. \u201cWe never followed up\u201d on maturing the technology that worked, he said. He said the JCO\u2019s \u201centerprise\u201d approach should help to fix that, allowing a much more organized development of counterdrone tech that\u2019s better matched to current intelligence and technology trends.<\/p>\n The real problem will be staying ahead of these trends. The JCO\u2019s new strategy looks ahead to an era when commercial drones will fill the skies over cities, and defenders will have to spot the ones that are acting strangely. The military must aim to \u201cadopt a posture of anomaly detection by seeking ways to highlight abnormal behavior,\u201d the strategy says. In the\u00a0U.S., at least, this will eventually be aided by the Federal Aviation Administration\u2019s efforts to build a next-gen Aircraft System Traffic Management System. But that is years away, and in any case, doesn\u2019t apply overseas. \u201cUntil they are implemented, the burden of tactically detecting and identifying anomalous systems in the vicinity of\u00a0U.S.\u00a0forces and facilities remains the responsibility of installation commanders,\u201d it reads.<\/p>\n Jamming drones, or even blasting them out of the sky, might work fine over the strait of Hormuz or the desert sands of\u00a0Syria, but it\u2019s a tricker proposition in the cities where the military expects to fight. Just finding them is a big problem. Small drones are often too small for radar, too cool for thermal sensors, and too soft for sound detectors.<\/p>\n One promising approach combines detection and defense: hijacking radio control signals.<\/p>\n \u201cWith our system, we surgically take control of the connection between the remote control operator and the drone and essentially hijack that session. Then we own the drone,\u201d said\u00a0Josh Montoya, a pre-sales engineer with the Israeli company\u00a0D-Fend Solutions<\/a>. \u201cThe drone takes commands from our system. What we tell the drone to do is take a safe route from where you\u2019re at and get out of the area, land, in a location we determine you can land safely.\u201d<\/p>\n We were given an exclusive demonstration of the technology at a small farm just outside of\u00a0Washington, D.C.\u00a0The system picks up the signal of any drones in the area and assembles a list. Press a button and the drone you were fretting over is now yours to land where you like \u2014 and without interfering with other radio-connected devices.<\/p>\n \u201cThat\u2019s kinda what federal law enforcement likes, the border force folks like, the VIP protective detail people like\u2026all of them who have the concern of \u2018if the drone crashes, what the collateral impact could be,\u2019\u201d said Montoya.<\/p>\n But you can\u2019t hijack a radio connection if it doesn\u2019t exist \u2014 that is, if a drone can operate autonomously. Smarter drones are the next big challenge for defenders, Gainey said.<\/p>\n \u201cWhere we see the threat going in the future is autonomous,\u201d the JCO commander said. \u201cMassing swarming capability and integrating AI and potentially leveraging 5G out in the future.\u201d These, he said, \u201care the areas we\u2019re looking to address.\u201d<\/p>\n Yet autonomous drones are also the next big opportunity for the Army, Murray said.<\/p>\n The Army Futures Command leader highlighted an experiment involving a small drone swarm last September, part of the Army\u2019s fledgling Project Convergence experiment in the\u00a0Arizona\u00a0desert.<\/p>\n \u201cI think we got up to about eight to ten out at\u00a0Yuma\u00a0and what we primarily use them for was to extend our mesh network,\u201d he said. \u201cSo we were replicating a division headquarters which, by today\u2019s doctrine, has the ability to cover about 25 to 30 kilometers. We extended it out to almost 70 kilometers through the use of aerial mesh networks with our drones.\u201d<\/p>\n Murray said the Army is experimenting with a variety of payloads, but declined to be more specific.<\/p>\n \u201cYou can think of it from a non-lethal and lethal perspective. We\u2019re able to swarm right now and we\u2019ll continue to try and expand the number,\u201d he said.<\/p>\n Greater autonomy is going to force militaries \u2014 and their civilian masters \u2014 to rethink the idea of meaningful human control over weapons like drones, Murray predicted. An incoming drone swarm may be too much for any human to deal with; effective defenses might require firing decisions made by artificial intelligence, with no\u00a0human in the loop<\/a>\u00a0after the initial decision to fight. That, in turn, could have international and policy implications. \u201cI was talking about artificial intelligence, where there might not be a C2 [communications] node in the net. The\u00a0policy\u00a0<\/a>of a human on-the-loop, when you\u2019re defending against a drone swarm, a human may be required to make that first decision but I\u2019m just not sure any human can keep up with a drone swarm, so that\u2019s an area where I think, in the\u00a0U.S., we can have some conversations going forward in terms of how much human involvement do you actually need when you\u2019re talking about non-lethal decisions from a human standpoint,\u201d he said.<\/p>\n Since 2016, drone maker Shield AI, working with the Defense Innovation Unit, has been providing small drones to\u00a0special operations\u00a0<\/a>with the ability to detect their location and maneuver without GPS signalling. Shield AI co-founder\u00a0Brandon Tseng\u00a0compared imbuing drones with autonomy to making a self-driving car, teaching software to measure and make decisions about objects in physical space. \u201cGPS is not reliable in dense urban environments, so the cars have to build their own maps of the world,\u201d Tseng said in a phone interview.<\/p>\n In about two months, Shield AI aims to release an upgraded version of its signature Nova drone with \u201cvision-based autonomy,\u201d a system designed to perform better at night than the current LIDAR sensors.<\/p>\n But the company\u2019s most significant work is less about selling specific drones and more about developing autonomic systems that can work on a wide assortment of devices and weapons. \u201cWe\u2019ve actually been doing a lot of the work in the\u00a0DOD\u00a0on training fixed-wing aerial vehicles to breach integrated air defense systems,\u201d Tseng said.<\/p>\n He said the company would demonstrate autonomous behaviors and maneuvers on a drone, perhaps from a different drone maker, sometime this year.<\/p>\n \u201cOnce you have a highly intelligent system, you can start to swarm,\u201d he said.<\/p>\n From there, stopping the drones is someone else\u2019s problem.<\/p>\n Importantly, the same technology that is enabling more autonomy in small drones has big implications for larger drones and the way the two work together in future battlefields. In October, Shield AI entered into a partnership with large UAV maker Textron. The two are making a \u201cproof-of-concept work to integrate Shield AI technology into Textron Systems\u2019 proven air, land and sea unmanned systems,\u201d according to\u00a0a release from Textron.<\/a><\/p>\n The slow merging of small and large drones in exercises and in autonomy software is particularly relevant for a military facing an adversary like\u00a0China.\u00a0China\u00a0is a market leader in small consumer drones and they\u2019re rapidly moving into larger types. Earlier this month, the\u00a0China Aerospace Science and Industry Corporation\u00a0claimed that the country\u2019s first jet-powered long-endurance UAV had\u00a0completed<\/a>\u00a0its maiden voyage.<\/p>\n Russia\u00a0is applying lessons from the decisive use of drones in\u00a0Azerbaijan<\/a>\u00a0to new drones and operating concepts, said\u00a0Sam Bendett, a research analyst with the\u00a0Center for Naval Analyses.<\/p>\n \u201cGoing forward, the Russian military will obtain multifunctional long-range drones that can carry different types of munitions. The [Ministry of Defense] is developing UAV swarm and loyal wingman tactics; and is working on testing and procuring loitering munitions,\u201d as well as imbuing drones with greater autonomy. (The\u00a0U.S.\u00a0military has its own loyal wingman program. In December, the Air Force\u2019s experimental Kratos XQ-58 Valkyrie, took its\u00a0first<\/a>\u00a0flight in formation with other jets.)<\/p>\n Russia\u00a0already\u00a0trains<\/a>\u00a0military units to counter small drones. They will soon be moving larger drones into the mix. Around September,\u00a0Russia\u00a0will work cruise missile and drone defenses into its largest annual military exercise,\u00a0Zapad.\u00a0<\/a>The Russian military is \u201calso starting to train in countering larger, heavier drones \u2013 its domestic industry has fielded several targeting models whose flight characteristics approximate larger Western UAVs,\u201d said Bendett. \u201cPreviously, Russian armed forces mostly trained in countering smaller UAS, with their own smaller UAVs acted as adversarial assets during training.\u201d<\/p>\n