This letter extends the overfitting lattice filter for ARMA parameter estimation with additive noise proposed by Sun and Yahagi. A new way of calculating the lattice parameters is proposed, making their computation truly recursive. This simplifies the method in Ref.(1), and makes it suitable to the parameter estimation of high-order systems.

An adaptive restoration algorithm is developed for binary images degraded nonadditively with flip noises. The true image is assumed to be a realization of a Markov Random Field (MRF) and the nonadditive flip noises are assumed to be statistically independent and asymmetric. Using the Expectation and Maximization (EM) method and approximating the Baum's auxiliary function, the degraded image is restored iteratively. The algorithm is implemented as follows. First, the unknown parameters and the true image are guessed or estimated roughly. Second, using the true image estimate, the Baum's auxiliary function is approximated and then the noise and MRF parameters are reestimated. To reestimate the MRF parameters the Maximum Pseudo-likelihood (MPL) method is used. Third, using the Iterated Conditional Modes (ICM) method, the true image is reestimated. The second and third steps are carried out iteratively until by some ad hoc criterion a critical point of EM algorithm is approximated. A number of simulation examples are presented which show the effectiveness of the algorithm and the parameter estimation procedures.

This paper describes the higher-order moment analysis of superposed Markov jumping processes. A superposed Markov jumping process is defined as a linear superposition of a finite number of piecewise constant real valued stochastic process whose value changes are associated with state transitions in an underlying descrete state continuous time Markov process. Some phenomena are modeled well by the process such as membrane current fluctuations observed at bio-membranes or load fluctuations in electrical power systems. Theoretical formula of the moment function of any order k is derived and the parameter estimation problem utilizing higher-order moment functions is discussed. A new method of estimating the kinetic parameters of membrane current fluctuations is proposed as a possible application.

This paper describes a new trial of dynamical parameter estimation for the actual room acoustic system, in a practical case when the input excitation is polluted by a background noise in contrast with the usual case when the output observation is polluted. The room acoustic system is first formulated as a discrete time model, by taking into consideration the original standpoint defining the system parameter and the existence of the background noise polluting the input excitation. Then, the recurrence estimation algorithm on a reverberation time of room is dynamically derived from Bayesian viewpoint (based on the statistical information of background noise and instantaneously observed data), which is applicable to the actual situation with the non-Gaussian type sound fluctuation, the non-linear observation, and the input background noise. Finally, the theoretical result is experimentally confirmed by applying it to the actual estimation problem of a reverberation time.