  State estimation of nonlinear dynamical systems has long aimed to balance
accuracy, computational efficiency, robustness, and reliability. The rapid
evolution of various industries has amplified the demand for estimation
frameworks that satisfy all these factors. This study introduces a neuromorphic
approach for robust filtering of nonlinear dynamical systems: SNN-EMSIF
(spiking neural network-extended modified sliding innovation filter). SNN-EMSIF
combines the computational efficiency and scalability of SNNs with the
robustness of EMSIF, an estimation framework designed for nonlinear systems
with zero-mean Gaussian noise. Notably, the weight matrices are designed
according to the system model, eliminating the need for a learning process. The
framework's efficacy is evaluated through comprehensive Monte Carlo
simulations, comparing SNN-EMSIF with EKF and EMSIF. Additionally, it is
compared with SNN-EKF in the presence of modeling uncertainties and neuron
loss, using RMSEs as a metric. The results demonstrate the superior accuracy
and robustness of SNN-EMSIF. Further analysis of runtimes and spiking patterns
reveals an impressive reduction of 85% in emitted spikes compared to possible
spikes, highlighting the computational efficiency of SNN-EMSIF. This framework
offers a promising solution for robust estimation in nonlinear dynamical
systems, opening new avenues for efficient and reliable estimation in various
industries that can benefit from neuromorphic computing.
