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01:08 PM, July 10, 2019
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Defense Advanced Research Projects Agency (DARPA) has awarded BAE Systems to develop “Controllable Hardware Integration for Machine-learning Enabled Real-time Adaptivity (CHIMERA),” a technology that integrates machine-learning (ML) into platforms that decipher radio frequency signals.

The solution provides a reconfigurable hardware platform for ML algorithm developers to make sense of radio frequency (RF) signals in increasingly crowded electromagnetic spectrum environments, the company said in a statement Monday.

The $4.7 million contract includes hardware delivery along with integration and demonstration support. CHIMERA's hardware platform will enable algorithm developers to decipher the ever-growing number of RF signals, providing commercial or military users with greater automated situational awareness of their operating environment. This contract is adjacent to the previously announced award for the development of data-driven ML algorithms under the same DARPA program (Radio Frequency Machine Learning Systems, or RFMLS).

RFMLS requires a robust, adaptable hardware solution with a multitude of control surfaces to enable improved discrimination of signals in the evolving dense spectrum environments of the future.

In an evolving threat environment, CHIMERA will enable ML software development to adapt the hardware’s RF configuration in real time to optimize mission performance. This capability has never before been available in a hardware solution. The system provides multiple control surfaces for the user, enabling on-the-fly performance trade-offs that can maximize its sensitivity, selectivity, and scalability depending on mission need. The system's open architecture interfaces allow for third party algorithm development, making the system future-proof and easily upgradable upon deployment.

Other RF functions, including communications, radar, and electronic warfare, also can benefit from this agile hardware platform, which has a reconfigurable array, front-end, full transceiver and digital pre-processing stage.

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