Improved Kalman Filter Initialisation using Neurofuzzy Estimation

It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs7, 9, 10 but unfortunately their performance is extremely sensitive to state initialisation accuracy (Figure 1). Good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter divergence. Divergence is generally due to excessive observation noise and leads to errormagnitudes that quickly become unbounded1, 5. When a filter diverges, it must be re-initialised but because the observations are extremely poor, re-initialised states will have poor estimates.