During the Cold War, the U.S. Navy was at the forefront of employing some of the earliest machine learning and computer assisted search systems to hunt for Soviet submarines. These programs used parametric descriptions of how submarines tended to operate to predict the future location of target submarines and assign search forces in an optimum manner. These systems were wildly successful, compared to those that did not utilize computational assistance.[i]
However, the collapse of the Soviet Union confined the Russian submarine fleet to port and led to a drastic reduction in defense spending in the former NATO countries. The last five years, however, have witnessed powers such as Russia and China dramatically expand their submarine operations and refocused the U.S. military on great power competition.[ii] The Navy is once again turning to machine learning and computer assisted search systems to hunt down foreign submarines and sift through the massive amounts of information generated by modern ships, submarines, and aircraft.[iii]
Hunting submarines poses two distinct problems for search forces. First, planners must make use of all information at their disposal to predict where the target might go in order to maximize the chance that the limited number of ships and aircraft find their target. Second, intelligence analysts must analyze staggering amounts of data to glean clues to where a submarine might be. To get a sense of how much data is generated by modern systems, consider that a submarine hunting airplane churns out roughly 900 GB of information on a typical flight.[iv]
Two additional strategic pressures impact the Navy’s submarine hunters. First submarines are becoming more numerous and increasingly stealthy as technology advances and various countries build out their fleets. Second, the massive amount of data produced by modern sensor systems is threatening to inundate the intelligence analysis systems used to support submarine search operations.
To respond, the U.S. Navy is upgrading its legacy submarine hunting operational planning platform.[v] The Navy is also rolling-out a system known is Minotaur, which fuses and analyzes sensor data.[vi] Minotaur algorithms sift through hours of sensor data and identify information patterns that match “high interest” signals from past missions. These “flagged” data packages are then forwarded to a human intelligence analyst for review and analysis.
While new systems bring new capabilities and efficiency, the pathway forward is not completely clear. For example, investment in system upgrades have focused on installing upgraded software on warships rather than on the more connected shore-based fleet headquarters. This is a shortsighted move for two reasons. First, Navy manpower is low, meaning that many warships have insufficient numbers of trained crewmembers. Second, fleet headquarters are better prepared to fuse information from many disparate sources and to provide detailed analysis. The Navy ought to focus more of their investments to upgrade its shore-based rather than ship-base systems.
The Navy is wisely turning to proven computer systems to increase the effectiveness of its limited number of warships and aircraft. Indeed, some observers have suggested that improved information fusion and big data analytics will render the ocean “transparent” and make submarines as readily detectable as surface ships.[vii],[viii] However, significant challenges remain for the American sailors.
First, government contracting moves so slowly that many systems are obsolete by the time they are fielded. Will the Navy be able to buy computer systems and software upgrades quickly enough to remain competitive? Second, the U.S. military enjoyed five decades of driving the aerospace, defense, and high technology sectors given it was the largest source of investment and revenue. However, consumers and private sector investment have become the sources of cash and demand that drive the tech sector today. Will the notoriously socially conscious tech culture in Silicon Valley be willing to build weapons systems?
The U.S. Navy is headed “back to the future” in leveraging proven operations analysis and machine learning systems. Whether the sailors are able to keep up with an evolving threat and a broken acquisition system is an open question. Until then, the secret game of cat and mouse beneath the waves will continue.
[i] Daniel H. Wagner, “Naval Tactical Decision Aids,” (Monterey: Naval Postgraduate School, September 1989), p. II-61.
[ii] Eric Schmitt, “Russian Bolsters its Submarine Fleet, and Tensions with U.S. Rise, New York Times, (April 20, 2016), https://www.nytimes.com/2016/04/21/world/europe/russia-bolsters-submarine-fleet-and-tensions-with-us-rise.html.
[iii] Michael Glynn, “Information Management in Next Generation Anti-submarine Warfare,” Center for International Maritime Security, (June 1, 2016), http://cimsec.org/information-management-next-generation-anti-submarine-warfar/25614.
[v] “U.S. Navy Fact File – AN/UYQ-100 Undersea Warfare Decision Support System (USW-DSS), U.S. Navy, (January 24, 2017), https://www.navy.mil/navydata/fact_display.asp?cid=2100&tid=324&ct=2.
[vi] William Matthews, “Navy’s Minotaur System is a Step Toward Automated Data Analysis,” Seapower Magazine, (May 18, 2016), http://seapowermagazine.org/stories/20160518-data.html.
[vii] James Holmes, “U.S. Navy’s Worst Nightmare: Submarines may no Longer be Stealthy,” The National Interest, (June 13, 2015), http://nationalinterest.org/feature/us-navys-worst-nightmare-submarines-may-no-longer-be-13103.
[viii] Bryan Clark, “The Emerging Era in Undersea Warfare,” (Washington, D.C.: Center for Strategic and Budgetary Analysis, January 22, 2015), http://csbaonline.org/publications/2015/01/undersea-warfare/.