Energy efficiency and reliability have long been crucial factors for ensuring
cost-effective and safe missions in autonomous systems computers. With the
rapid evolution of industries such as space robotics and advanced air mobility,
the demand for these low size, weight, and power (SWaP) computers has grown
significantly. This study focuses on introducing an estimation framework based
on spike coding theories and spiking neural networks (SNN), leveraging the
efficiency and scalability of neuromorphic computers. Therefore, we propose an
SNN-based Kalman filter (KF), a fundamental and widely adopted optimal strategy
for well-defined linear systems. Furthermore, based on the modified sliding
innovation 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 the
proposed strategies, we compare them to their algorithmic counterparts, namely
the KF and the MSIF, using Monte Carlo simulations. Additionally, we assess the
robustness of SNN-MSIF by comparing it to SNN-KF in the presence of modeling
uncertainties and neuron loss. Our results demonstrate the applicability of the
proposed methods and highlight the superior performance of SNN-MSIF in terms of
accuracy and robustness. Furthermore, the spiking pattern observed from the
networks serves as evidence of the energy efficiency achieved by the proposed
methods, as they exhibited an impressive reduction of approximately 97 percent
in emitted spikes compared to possible spikes.

本研究提出了基于脉冲编码理论和脉冲神经网络的估计框架，利用神经形态计算机的效率和可伸缩性来实现小型、轻量化和低功耗计算机在无人系统计算机中的应用并证明了其在精度和鲁棒性方面优于传统算法，减少近 97% 的能量消耗。