Transforming Tactical Targeting

Unleashing the Power of AI, ML and RPA through Project TITAN

By CW2 Jordin Katzenberger

Article published on: October 31, 2024 in Field Artillery 2024 Issue 2

Read Time: < 5 mins

The Army of 2030 and beyond will face significant challenges during Large-Scale Combat Operations (LSCO) as technology advances to new heights and continues to empower adversaries to strengthen system capabilities and increase operational reach (GAO, 2019). These challenges drive changes in tactical level targeting to enable a more efficient and accurate means to acquire and engage targets. This dynamic and constantly changing technological environment has created opportunities to improve tactical targeting procedures by utilizing artificial intelligence (AI), machine learning (ML) and robotics process automation (RPA). The utilization of sophisticated technology can significantly transform the process of target acquisition and engagement, enhancing the operational capabilities of military units in terms of timeliness, precision, productivity and overall performance. Utilizing AI, ML and RPA through Project TITAN (Tactical Intelligence Target Access Node) in the tactical-level targeting process will improve the accuracy, efficiency and effectiveness of target acquisition and engagement, ultimately empowering military units to achieve mission objectives with greater precision and reduced collateral damage.

According to Svetlana Sicular, “As AI technology evolves, the combined human and AI capabilities that augmented intelligence allows will deliver the greatest benefits to enterprises” (Bhakuni, 2023). AI has the potential to substantially impact the Army targeting process through its ability to augment data analysis and provide suggestions at a faster pace, with more capacity than a human analyst (Bhakuni, 2023). Utilizing predefined input criteria from a human source, AI can evaluate hundreds of rows of data in seconds or minutes to enable decision making. The ability to handle and evaluate large amounts of information and tactical data quickly and efficiently makes it easier to find and evaluate possible targets. Through the utilization of ML methodologies, AI can discern patterns and establish connections within past data to generate more precise forecasts and informed targeting strategies. Furthermore, recommendation systems driven by AI can aid in the target selection process by offering valuable insights and ideas derived from the study of pertinent data sources. Using AI systems can significantly improve the speed and scalability of targeting processes to enhance overall efficiency and effectiveness. This improvement enables commanders to make well informed decisions within a reasonable timeframe.

In conjunction with AI, ML will be essential in the Army targeting process, as it enables the study of patterns using historical data, the creation of target templates and the allocation of resources. Like AI, ML algorithms can examine extensive volumes of historical data, including intelligence reports, sensor data and operational records. This analytical process aims to detect patterns and trends that might potentially signify targets or threats (Bhakuni, 2023). Through the recognition of these patterns, ML assists in the identification of high-value targets and evaluates the level of their relevance to determine high-payoff targets based on inputs from the targeting officer. To reduce risk, considerations will have to be taken to develop unbiased criteria for the AI solution to evaluate against. Additionally, a final review of outputs to validate the provided high-payoff target (HPT) in order to avoid any negative ethical and legal considerations prior to engagement. ML approaches may also be employed to generate target templates that effectively encapsulate the distinctive attributes and behaviors of specific targets to facilitate their recognition and monitoring. Moreover, ML algorithms enhance resource allocation through the examination of past data pertaining to the accessibility and efficacy of military assets. This empowers commanders to distribute resources in a manner that is both efficient and effective throughout the targeting process.

Utilizing RPA in the Army targeting process can automate repetitive operations and facilitate efficient information exchange across various systems and stakeholders. RPA software robots automate labor intensive and manual activities associated with data collecting, data input and report preparation. This automation enables humans to allocate time and efforts to more crucial elements of targeting. Through the automation of these operations, RPA can improve the efficiency and precision of information processing, facilitating expedited decision-making processes. RPA can enhance the efficiency of information flow via the integrating systems and automating data exchange processes. This practice enables the effective dissemination of pertinent intelligence and operational information among all stakeholders engaged in the targeting process promoting collaboration and increasing overall situational awareness.

The main goal of the TITAN program is to improve situational awareness and the distribution of intelligence at the tactical level (PEO IEW&S, 2022). TITAN aspires to enhance the collection, processing and dissemination of vital intelligence information through the utilization of cutting-edge technology. This strategic approach strives to expedite and optimize the data flow, equipping commanders and troops with the requisite knowledge to efficiently accomplish mission objectives. TITAN enhances commanders’ comprehension of the operational environment in real time, enabling them to make decisions based on current intelligence. A significant advantage of TITAN is the ability to collect, analyze and share intelligence data across various echelons simultaneously using satellites, radars, unmanned aerial vehicles (UAVs), human and other sensor platforms. By expediting the acquisition, processing and dissemination of intelligence, TITAN facilitates quicker decision-making cycles and enables commanders to adapt to a rapidly evolving operational environment. The ability to perform deep sensing has been identified as one of the most significant gaps the Army must address as operations transition to LSCO (NAIIO, 2022). TITAN’s ability to synchronize assets across multiple domains will improve the ability to conduct deep sensing and enable a more accurate selection and prioritization process of targets within the area of operations.

The integration of cutting-edge technologies, including AI, ML and advanced sensor platforms, into the Army’s targeting process is anticipated to yield a substantial technical edge in overcoming future adversaries. These capabilities provide a complete and up-to-date representation of the operational environment, enabling the identification and surveillance of possible targets over large geographical regions. ML algorithms examine extensive quantities of data to identify patterns and recommend target prioritization. As a result, these algorithms contribute to improving target selection and engagement. The efficient examination of data using AI and ML accelerates the targeting process in dynamic operational environments.

In addition, utilizing AI and ML technology enables the implementation of adaptive targeting methods. These strategies are characterized by the ability to continually acquire knowledge from newly available data for commanders to adjust tactics in response to changing conditions. This practice guarantees the attainment of efficient responses to newly arising threats and empowers the military to maintain a strategic advantage over enemies (Peachey, 2020). Moreover, the utilization of RPA assumes a pivotal function in enhancing cooperation through automating data interchange and alleviating the cognitive burden on analysts. This enables individuals to concentrate on crucial duties while guaranteeing the distribution of operational information, fostering situational awareness and collaboration among those involved in the targeting process.

The minimization of collateral damage is significantly influenced by the precision and accuracy offered by AI and ML techniques housed within TITAN. By examining trends, historical data and contextual information, these technologies provide the capability to enhance the precision of target identification reducing the likelihood of unintentional injury to non-combatants and infrastructure. The ability to precisely and efficiently target key objectives while minimizing collateral damage to civilians greatly enhances the overall effectiveness and morality of military operations.

In conclusion, the ability to shape the deep fight at division and higher echelons during LSCO drives significant investments into programs like TITAN (PEO IEW&S, 2022). Incorporating AI, ML and RPA into programs like TITAN while utilizing advanced sensor platforms presents a technical edge that will be crucial in overcoming enemies in future conflicts. These technological breakthroughs enhance the ability to sense deep, accelerate the decision-making process, allow the implementation of adaptive targeting techniques, streamline cooperation, decrease cognitive burden and avoid unintended harm. Through the utilization of these technologies, the Army can augment operational efficacy and sustain a competitive advantage in complex and rapidly changing operational environments.

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