Advanced Radar Signal Processing with Neuromorphic Architectures: A State-Space Approach

Session details:

Traditional radar systems face significant challenges in accurately and efficiently processing complex signals in real-world environments, particularly for fine-grained detection and classification. The high computational load and power consumption of conventional digital signal processors limit their deployment in size, weight, and power (SWaP)-constrained platforms. This presentation introduces a transformative approach to radar signal processing by leveraging BrainChip's Akida™ neuromorphic processor and advanced state-space based models.

The proposed methodology utilizes a novel combination of spiking neural networks (SNNs) and temporal event-based neural networks (TENNs) to process radar data with unprecedented efficiency. By adopting a state-space model architecture, we move beyond conventional, frame-based processing to an event-based paradigm. This allows the system to focus computation only on meaningful changes in the radar signal (e.g., micro-Doppler signatures), effectively filtering out noise and clutter while drastically reducing power consumption.

This approach significantly enhances the state of the art in radar applications, including micro-Doppler signature analysis for activity discrimination, object classification in cluttered environments, and real-time threat detection. By embedding this intelligence directly on the Akida chip, we can enable more autonomous and responsive systems for a wide range of applications in aerospace, defense, and industrial IoT. The presentation will detail how this technology improves detection accuracy, reduces latency, and provides a path to next-generation intelligent sensors with dramatically lower power footprints.

 
You can learn more about BrainChip's work in radar technology in a podcast episode with Dr. Jonathan Tapson and Dr. Alan Wilson-Langman, which you can listen to here: A Chat with Dr. Tapson and Dr. Alan Wilson-Langman.