Advancing precision in multi-agent systems: a neuromorphic approach with spiking neural network-modified sliding innovation filter

Abstract

In the realm of multi-spacecraft missions, crew transport and satellite tasks require precision in rendezvous maneuvers. A robust navigation system becomes essential for addressing uncertainties in space robotic modeling. This study presents a novel approach by leveraging neuromorphic computing, introducing the Spiking Neural Network-Modified Sliding Innovation Filter (SNN-MSIF) for satellite rendezvous in circular orbit. The SNN-MSIF combines the efficiency of neuromorphic computing with MSIF's robustness, enhancing accuracy and stability. Utilizing Clohessy-Wiltshire equations, the model captures relative motion between spacecraft. Monte Carlo simulations are used to compare the SNN-MSIF with SNN-Kalman filters and their non-spiking counterparts, showcasing the superior accuracy and stability of our approach. The evaluation of their robustness under uncertaintie1s and neuron silencing demonstrates their reliability. The findings establish SNN-MSIF as an effective, efficient, and promising filtering framework for space robotics, refining navigation, and addressing multi-spacecraft challenges.