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Energy efficiency and reliability have long been crucial factors for ensuringcost-effective and safe missions in autonomous systems computers. With therapid evolution of industries such as space robotics and advanced air mobility,the demand for these low size, weight, and power (SWaP) computers has grownsignificantly. This study focuses on introducing an estimation framework basedon spike coding theories and spiking neural networks (SNN), leveraging theefficiency and scalability of neuromorphic computers. Therefore, we propose anSNN-based Kalman filter (KF), a fundamental and widely adopted optimal strategyfor well-defined linear systems. Furthermore, based on the modified slidinginnovation filter (MSIF) we present a robust strategy called SNN-MSIF. Notably,the weight matrices of the networks are designed according to the system model,eliminating the need for learning. To evaluate the effectiveness of theproposed strategies, we compare them to their algorithmic counterparts, namelythe KF and the MSIF, using Monte Carlo simulations. Additionally, we assess therobustness of SNN-MSIF by comparing it to SNN-KF in the presence of modelinguncertainties and neuron loss. Our results demonstrate the applicability of theproposed methods and highlight the superior performance of SNN-MSIF in terms ofaccuracy and robustness. Furthermore, the spiking pattern observed from thenetworks serves as evidence of the energy efficiency achieved by the proposedmethods, as they exhibited an impressive reduction of approximately 97 percentin emitted spikes compared to possible spikes.

Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach