Positioning and tracking enhancement through a spiking Legendre memory unit

By coupling State Space Models (SSMs) with Spiking Neural Networks (SNNs), the potential of neuromorphic solutions for positioning and tracking can be successfully explored. A spiking SSM based on the Legendre Memory Unit (LMU) is shown to effectively predict the state of an Extended Kalman Filter (EKF) using Inertial Measurement Unit (IMU) and Global Positioning System (GPS) inputs, with memory reduction up to 96% compared to Deep Learning (DL) solutions. Additionally, such SNN-based architecture enables GPS error correction through data fusion of IMUs, wheel encoders, and gyroscopes, highlighting the efficiency of spike-based solutions over traditional models.