RFspkSSM: Spiking State Space Models for Efficient Radar Target Recognition

Spiking neural networks and state-space models offer complementary approaches to efficient sequence processing, combining sparse event-driven computation with scalable long-range modelling without quadratic dependence on sequence length. Existing spiking state-space models focus largely on generic sequence modelling, with limited exploration in radar sensing and signal processing. We propose radar-oriented spiking state-space models (RFspkSSM), built around a second-order oscillatory spiking state-space model (OscSpikeSSM) that combines trainable neuronal parameterisations with stability-constrained oscillatory dynamics. For radar sensing, the core extends to structured point clouds, multi-view heatmaps, and radar-IMU representations spanning applications such as activity and gesture recognition, 3D human and hand pose estimation, and raw FMCW signal processing. Across 10 benchmark datasets, RFspkSSM efficiently outperform baselines. More specifically, RFspkSSM attains 96.8% accuracy in radar gesture recognition, reduces pose centre-distance error to 9.2 cm ($3.41\times$ lower), uses up to $125\times$ fewer parameters for mmWave recognition, and achieves up to $3.6\times$ lower theoretical compute energy in radar signal processing. Our results show that RFspkSSM generalises across heterogeneous sequence and radar representations, combining strong task performance with efficient modelling that learns spectral processing from sequence data.