As part of the close collaboration with the SAF, DSTA has been supporting Exercise Forging Sabre (XFS) – an integrated strike live-firing exercise held in the US – since 2009. For XFS 2019, a DSTA team leveraged artificial intelligence (AI) and analytics to boost the SAF’s ability to orchestrate integrated strike operations swiftly and effectively with an enhanced command and control (C2) system.

The Command Post C2 system is used to coordinate between exercise entities, including sense and strike assets from the RSAF and the Singapore Army. Its core function is to help the SAF make sense of the battlefield and orchestrate missions faster.

The DSTA team built up smart tools for the entire sense-strike process: from detecting the target, tracking it, predicting where it will be, to the point where it is neutralised. This enables commanders to make faster, better informed and more effective decisions, and shortens the sensor-shooter cycle.

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One of the new tools introduced for XFS 2019 was the Automatic Target Detection (ATD) module, which aids operators in detecting targets of interest automatically from the Heron-1 Unmanned Aerial Vehicle live video streams.

During missions, operators have to constantly monitor these live feeds to identify threats in a complex and dynamic environment, and this demands high levels of concentration. With the ATD module, the AI picks out targets automatically and informs the operator of potential threats through visual cues displayed on the video feed.

The team also integrated the Target Look-Ahead (TLA) module which leverages AI to recommend strike locations and calculate how long it would take a target to arrive. This would also enable targets to be taken out at the right place and time to minimise collateral damage.

The TLA module analyses the target’s potential routes from map data, together with other information such as its current location and speed, in order to predict the target’s Estimated Time of Arrival (ETA). To account for the possibility of deviations in path or changes in speed, the capability runs in real time to give operators up-to-date information and ETA predictions.

With the TLA module, operators are relieved of the task of manually estimating the adversarial target’s ETA, and can focus on managing the strike mission. This reduces the cognitive workload on SAF operators and increases the reaction time available for decision-making.

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