Technical Perspective: NeuroRadar: Can Radar Systems Be Reimagined Using Computational Principles?

Interest in miniature radar systems has grown dramatically in recent years as they enable rich interaction and health monitoring in everyday settings. By 2025, industrial radar applications are anticipated to encompass 10 million devices, whereas the consumer market will reach a substantial $250 million. The applications are diverse—for example, Google’s Pixel phones incorporated radar for gesture control, while small radar sensors are being deployed in homes to monitor elderly residents’ movements and detect falls, offering more privacy than camera-based solutions.

However, conventional radar architectures rely on complex RF front ends with power amplifiers, low-noise amplifiers, and phase-locked loops, collectively consuming hundreds of milliwatts of power. The subsequent digital signal-processing chain, often implementing sophisticated neural networks for perception tasks, further compounds the power demands. This makes radar sensing impractical for battery-powered or self-powered Internet of Things (IoT) devices and wearables.

The accompanying paper, “NeuroRadar: A Neuromorphic Radar Sensor for Low-Power IoT Systems,” explores an interesting question: Can radar systems be reimagined using computational principles similar to how biological brains process sensory information? The result, NeuroRadar, introduces a brain-inspired approach that reduces computing power by one to two orders of magnitude compared to traditional digital signal processing while maintaining high accuracy in tasks such as gesture recognition and moving-target localization.

Biological brains achieve remarkable efficiency through parallel computation and event-driven processing—neurons only consume energy when processing incoming spikes (electrical pulses). Drawing on these principles, NeuroRadar implements a comprehensive neuromorphic architecture that spans from sensing to processing. The system captures changes in the radar environment and encodes them as asynchronous, event-driven spikes containing motion information, which are then processed by an efficient spiking neural network. This represents a dramatic departure from traditional radar systems that continuously sample and process data regardless of whether meaningful changes are occurring.

At the heart of NeuroRadar is an elegant sensing mechanism: self-injection locked (SIL) oscillators act as both transmitter and receiver, eliminating the need for power-intensive components like low-noise amplifiers, power amplifiers, or phase-locked loops. The physics of injection locking does the heavy lifting: As objects move in the environment, they alter the phase relationship between the oscillator’s signal and its reflection. The oscillator automatically adjusts its frequency to maintain phase lock, creating a natural motion detector that consumes mere microwatts of power. The captured motion is then rate-based-encoded using a biologically inspired neuron model—the leaky integrate-and-fire (LIF) model—which generates spikes at a rate linearly correlated with the amplitude of the input signal, preserving the sensing information within the spike sequence. By eliminating most active RF components, this approach reduces front-end power consumption by approximately 500x.

The authors overcome the inherent limitations of SIL sensing—namely ambiguous range information and lack of directionality—through a clever, frequency-diverse array design. A single SIL element provides ambiguous range information and no directional capability. The authors solve this through a frequency-diverse array design, where multiple SIL elements operate at different frequencies with carefully chosen spacing. Like a compound eye in nature, each element sees the target from a slightly different perspective, and the combination resolves spatial ambiguity. The random frequency permutation across array elements further reduces ambiguity by creating a range-angle decoupled beam pattern.

The system’s processing pipeline combines neuromorphic principles with deep-learning approaches to achieve robust performance. This is done by employing an ANN-to-SNN conversion method for training, while using spiking convolutional layers for inference. The system strategically buffers spikes into time windows—an approach analogous to spatial flattening in image processing—enabling the convolutional layers to effectively extract spatiotemporal patterns from the spike sequences. While this trades some temporal processing capabilities of native SNNs, it provides a practical path to robust gesture recognition and localization tasks.

The system achieves remarkable efficiency in real-world tasks. Evaluations across hand-gesture recognition and moving-target localization demonstrated performance comparable to traditional radar systems while consuming orders of magnitude less power. Even compared to other neuromorphic approaches, NeuroRadar achieved significant power reductions while maintaining robust sensing capabilities.

NeuroRadar demonstrates an exciting path forward in neuromorphic sensing, elegantly combining bio-inspired analog design with practical engineering compromises to achieve ultra-low-power operation. The implications extend beyond power efficiency; this work opens new possibilities for always-on monitoring with radars previously considered impractical due to energy constraints. As neuromorphic hardware platforms mature and training methods for spiking neural networks advance, future systems may more fully exploit the temporal processing capabilities inherent in neuromorphic architectures.

Significant challenges remain, particularly in demonstrating robustness across diverse environmental conditions and usage scenarios. The system needs further validation for end-to-end applications in health monitoring, navigation, and security. Nevertheless, this work charts a promising direction toward truly ubiquitous, intelligent sensing systems that could fundamentally transform how we interact with and monitor our environment, while operating within the strict power constraints of battery-powered devices.