An interlaced extended Kalman filter

In this paper an estimation algorithm for a class of discrete-time nonlinea r systems is proposed. The system structure we deal with is partitionable i nto m subsystems, each affine w.r.t. the corresponding part of the state ve ctor. The algorithm consists of a bank of m interlaced Kalman filters, and each of them estimates a part of the state, considering the remaining parts as known time-varying parameters whose values are evaluated by the other f ilters at the previous step. The procedure neglects the subsystem coupling terms in the covariance matrix of the state estimation error and counteract s the errors so introduced by suitably "increasing" the noise covariance ma trices. Comparisons through numerical simulations with the extended Kalman filter and its modified versions proposed in the literature illustrate the good tradeoff provided by the algorithm between the reduction of the comput ational load and the estimation accuracy.