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.

翻译：能效与可靠性长期以来一直是确保自主系统计算机经济、安全运行的关键因素。随着空间机器人、先进空中交通等行业的快速发展，对低尺寸、重量与功耗（SWaP）计算机的需求显著增长。本研究聚焦于基于脉冲编码理论与脉冲神经网络（SNN）的估计框架，利用神经形态计算机的高效性与可扩展性。因此，我们提出了一种基于SNN的卡尔曼滤波器（KF），这是针对明确线性系统的经典且广泛采用的最优策略。此外，基于改进滑动创新滤波器（MSIF），我们提出了一种名为SNN-MSIF的鲁棒策略。值得注意的是，网络权重矩阵根据系统模型设计，无需学习过程。为评估所提策略的有效性，我们通过蒙特卡洛模拟将其与算法对应物（即KF和MSIF）进行对比。同时，在存在建模不确定性与神经元丢失的情况下，通过将SNN-MSIF与SNN-KF进行鲁棒性评估。结果表明了所提方法的适用性，并凸显了SNN-MSIF在精度与鲁棒性方面的优越性能。此外，网络中观察到的脉冲模式证明了所提方法实现的能效，其发射脉冲数相较于潜在脉冲数减少了约97%，效果显著。