SOURCE: AFI
The Defence Electronics Research Laboratory (DLRL) is spearheading a cutting-edge initiative to enhance electronic warfare (EW) capabilities by developing an advanced AI-based EW Data Analysis System (AI-EWDA). This system leverages machine learning and deep learning technologies to predict the identity of unknown emitters and provide countermeasure suggestions, marking a significant leap in India’s defense technology landscape.
The AI-EWDA system is designed to process and analyze data from multiple sensors to deliver comprehensive and precise information about potential electronic threats. Electronic warfare relies on detecting and analyzing various types of electromagnetic signals, such as radar emissions and communication signals, to identify enemy emitters and devise counter-strategies. However, this process has traditionally required significant manual intervention and expert analysis. DLRL’s new AI-EWDA system aims to automate and accelerate this process, transforming how EW operations are conducted.
The system comprises two core components:
Control Center System: The hub for receiving, processing, and storing emitter data.
Platform Unit System: A more compact, operational version deployed on mobile platforms such as ships, aircraft, or ground vehicles.
Both systems are equipped with specialized software that utilizes machine learning models to analyze incoming data and identify unknown emitters, such as enemy radars or communication systems. The Control Center System performs the heavy lifting, training the machine learning models and storing large-scale data from various sensors, while the Platform Unit System uses a subset of the control center’s features, enabling it to operate in real-time on the field.
One of the standout features of the AI-EWDA system is its ability to fuse emitter data from multiple sensors—such as ESM (Electronic Support Measures), COMINT (Communications Intelligence), and ELINT (Electronic Intelligence)—to create a more complete picture of the electromagnetic environment. This process, known as Multi-Sensor Data Fusion (MSDF), integrates data from diverse sources to provide a more accurate identification of emitters and their associated platforms.
The machine learning model, trained at the Control Center, plays a pivotal role in this process. By analyzing historical emitter data stored in the EW database—including radar signals, communication signals, and other electromagnetic emissions—the AI-EWDA system can learn to predict the identity of unknown emitters with increasing accuracy. This predictive capability is crucial for modern warfare, where speed and accuracy in identifying threats can mean the difference between success and failure in mission execution.
Another powerful feature of the AI-EWDA system is its ability to perform post-mission analysis. After a mission, the system gathers all intercepted data, fuses it with historical records, and uses its machine learning model to refine the identification of unknown emitters. This data is stored in mission libraries, enabling the system to continuously learn and improve its predictions for future operations.
In real-time scenarios, the AI-EWDA system assists in constructing a Graphical and Tabular Electronic Order of Battle (EOB), providing a comprehensive view of the electronic landscape. Using open-source map tools, this information can be visualized to facilitate rapid decision-making and deployment of countermeasures.