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Fires in the Fourth Industrial Revolution

Army’s Kill Chain and Targeting Automation for the Defence of Australia

A United States Army M142 High Mobility Artillery Rocket System (HIMARS) fires in Puslatpur, Indonesia during Exercise Super Garuda Shield 2023.

The fourth industrial revolution has heralded an era of artificial intelligence (AI), automation, and robotics that represents vast potential benefits for the Australian Army.[1] One key area that these innovations could aid is the Army’s growing indirect fires and targeting responsibilities. This is evident in four key areas. Firstly, new AI and robotic technologies can bridge the Army’s indirect firepower gap above the formation level. Second, robotics can enhance the endurance and reach of our artillery systems. Third, the Army’s ability to integrate its fires platforms into the joint force can be aided by autonomous kill chain programs. Finally, augmented intelligence programs can considerably accelerate the Army’s targeting cycle. The effective integration of these innovations is however contingent on control measures that maintain human authority over the targeting process. 

Fires Platform Augmentation

AI can help to bridge the Army’s firepower deficiencies at the divisional level. This year’s reorganisation of the 1st (Australian) Division will see a growing need for divisional indirect fires platforms, especially if we attempt divisional manoeuvre. The role of divisional artillery cannot however be fulfilled by the Army’s howitzers, which are instruments of formation-level close combat. Additionally, the Army’s new High Mobility Artillery Rocket System (HIMARS) is likely to be preoccupied with strategic deterrence-by-denial tasks, as outlined in the 2023 Defence Strategic Review.[2] Furthermore, in a major war scenario, we must also be prepared for attack aviation and fixed-wing support to be limited by adversary air denial.

The resultant divisional firepower gap could instead be bridged in part by loitering autonomous weapons. Loitering munitions such as the Harpy and HERO drones boast an endurance of tens of hours and ranges of hundreds of kilometres.[3] These capabilities are heavily suited to delivering deep-shaping fires that enable divisional manoeuvre.[4] Evidence of the operational effectiveness of these weapons is abundant, particularly from the Nagarno-Karabakh and Russo-Ukraine conflicts.[5] Autonomous weapon augments would permit the field artillery to concentrate on achieving fire supremacy in the close fight. It will also permit Army’s preeminent deep strike asset, HIMARS, to conduct strategic strikes in defence of Australia.

Remotely Operated Artillery

Army’s future artillery force also stands to gain from robotic technologies because of its suitability to Australia’s littoral operating environment. Autonomous artillery platforms reduce crew, cost, and weight, which improves deployability, and sustainability.[6] These attributes are advantageous within the austere remoteness of Australia’s Indo-Pacific surroundings. More importantly, these advantages improve the Army’s ability to project all-weather high-endurance anti-ship deterrents that can hold aggressors at risk with reduced human exposure. This is the concept that drives the United States’ (US) Remotely Operated Guided Unit for Expeditionary Fires (ROGUE-F) capability, and Germany’s Artillerie-Geschütz-Modul. The ROGUE-Fires system is central to the US’ land-based ship interdiction system, a capability with obvious utility for Australia. The Army has already embarked on its experimental journey with remotely crewed armoured vehicles, such as BAE Systems’ optionally crewed M113 APC.[7] This domestic innovation could be leveraged to develop remotely operated artillery for the Army as the technology becomes more tactically desirable.  

Automated Kill Chains

The use of AI can also greatly improve the integration of Army’s HIMARS into the joint force kill chain. The term ‘kill chain’ refers to the process of finding, fixing, and engaging targets across the battlespace. Programs now exist that digitally integrate a greater variety of firing systems across a joint force to increase the number of potential kill chain pathways. For instance, a program called Automated Tactical Targeting and Counter-fire Kill-chain System (ATTACKS) can autonomously re-format K-series targeting data used by Western air forces, into J-series formats used by field army artillery.[8] This permits rapid target hand-off across joint service platforms, greatly improving interoperability. Another US program named the Joint Targeting Command and Control System integrates all of a joint force’s air and surface fire assets onto a common digital fire control thread, thereby greatly aiding the execution of multi-domain strikes.[9]  

Capabilities like these are particularly advantageous to Australia for two reasons. One, they are optimised for executing deep strikes inside denied battlespaces, the need for which will be great if war erupts in Australia’s neighbourhood. Secondly, they improve the Army’s ability to synchronise its long-range firepower with the joint force. This will become increasingly critical as the ADF’s next-generation missile fleet, which includes Tomahawk and Naval Strike Missile, is fielded.[10] The cooperative synchronisation of the ADF’s disparate long-range effectors will be essential to achieving kill chain dominance. These innovations will aid the Army’s ability to play a critical role in the ADF’s future precision strike complex.

AI-enabled Targeting

The Army’s targeting cycle can be accelerated by adopting augmented intelligence software. A targeting cycle is a process of prioritising, locating, and engaging targets in order of their value to tactical objectives.[11] Augmented intelligence programs can accelerate aspects of the targeting cycle that are typically slow and staff-intensive. One such program, called Tactical Intelligence Targeting Access Node (TITAN), autonomously aggregates vast quantities of raw data from secure and open-source media to identify targets across a wide battlespace for potential engagement.[12] Once found, other programs such as Watchbox then Processes, Exploits and Disseminates (PED) targets to engagement decision-makers.[13] Following a strike, the challenging process of Battle Damage Assessment (BDA) can also be expedited using machine learning. Convolutional neural networks can process inputs from cyber, visual, and electromagnetic sensors, such as satellites, to provide a summary of the effects delivered across wide areas.[14] Following this, other programs could catalogue the effectiveness of different weapon combinations to shape subsequent targeting events. Although these programs are still in their infancy, they provide a glimpse into the near future of how AI can help accelerate key bottlenecks to achieve kill chain dominance.

Control Measures and Limitations

For all the proposed benefits of AI, we must also ensure that its implementation is governed by robust control measures. This is to ensure that the Army’s targeting procedures, which amount to the lawful taking of human life, are not divested to an algorithm. As Tim MacFarland posits, we must ensure that ‘trust’ does not become the substitute for ‘control’ in the application of autonomous weapons.[15] Control measures are also necessary to ensure that, in our enthusiasm to innovate, we do not invite overdependence and negligence to coagulate the kill chain. The innovations proposed in this article do not invalidate the Army’s need to revert to more rudimentary methods of targeting, especially when systems become denied by the enemy. Finally, control measures are necessary to ensure that AI-augmented kill chains are adaptive to the fluid nature of warfare. Only humans can define the character of a conflict’s political objectives and the moral dimension enshrining its ethical conduct. War, in all its chaos, is a human phenomenon that transcends algorithms. For these reasons, AI augmentation should be focused on accelerating slow-moving phases of targeting cycles and kill chains, rather than substituting them.


The Army’s indirect fires and targeting responsibilities stand to greatly benefit from the innovations of AI and robotics. Loitering munitions and robotic technologies can greatly reduce the Army’s divisional firepower gap while also improving the endurance of its platforms in the defence of Australia. The emergence of automated kill chain software can expedite target hand-off, enabling the Army to better integrate its LRPF into joint force strikes. Augmented intelligence programs can significantly accelerate time-intensive aspects of the targeting cycle, such as detection, dissemination, and BDA. All these innovations must, however, be tempered with the responsibility to safeguard human authority over targeting engagements as befits the strategic objectives and moral dimension of a given war.

This article is a submission to the Spring Series 2023 Short Writing Competition, 'Army’s approach to accelerated preparedness'.

[1] Jean-Marc Rickli, ‘The Strategic Implications Of Artificial Intelligence For International Security’, in Handbook Of AI And Robotic Process Automation, ed. Al Naqvi, Mark Munoz, (London: Cambridge University Press, 2022), 46

[2] National Defence Strategic Review, Canberra: Commonwealth of Australia, 2023, 19

[3] Lisa Parks, Caren Kaplan, Life in the Age of Drone Warfare, (North Carolina: Duke University Press, 2017), London, 270

[4] Brennan Deveraux, ‘Loitering Munitions are the Future of Division Shaping Operations’, Real Clear Defence, Mar 2023; Hero Loitering Munitions, Rheinmetall

[5] Zhirayr Amirkhanyan, “A Failure to Innovate: The Second Nagorno-Karabakh War”, in Parameters: US Army War College, Vol.52, 2022, 122; Justin Bronk with Nick Reynolds and Jack Watling, The Russian Air War and Ukrainian Requirements for Air Defence, (London: RUSI, 2022), 4 - 5

[7]Kym Berhmann, “BAE Systems Delivers 20 M113 AS4 Optionally Crewed Combat Vehicles”, Asia Pacific Defence Report, Nov 2021

[8] Pablo Kruger, Michael Molinari, Benjamin Baumann, “The Future of Air-Ground Integration: Linking Sensor to Shooter in the Deep Fight”, Air Land Sea Space Application (ALSSA) Center, 2021

[10] Nigel Pittaway, “Strike Further and Harder – Astralia’s Precision Strike Capability”, Australian Defence Magazine, Vol. 31, No. 4, May 2023, 26-30

[11] Jimmy Gomez, ‘The Targeting Process: D3A and F3EAD’, Small Wars Journal, 2011, 11, 1- 16

[13] Ridger, R. “Winning the Counterland Battle By Enabling Sensor-to-Shooter Automation”, Air Land Sea Application Center, 1 Nov 2021

[14] Michael O’Gara, ‘AI and Integrated Fires’, in AI at War: How Big Data, Artificial Intelligence, and Machine Learning Are Changing Naval Warfare, Ed. Tangredi, S. J., & Galdorisi, G. (Naval Institute Press, 2021), 225; Tyler Knight, ‘Machine Learning to Detect Battle Damage Using Satellites’, Defense Systems Information Analysis Center, Oct 2022

[15] Tim McFarland, ‘Reconciling trust and control in the military use of artificial intelligence’, International Journal of Law and Information Technology, Vol. 30, (2023) 472-483, 473

The views expressed in this article and subsequent comments are those of the author(s) and do not necessarily reflect the official policy or position of the Australian Army, the Department of Defence or the Australian Government.

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