In this paper, a real-time predictive filter is derived for nonlinear systems. This provides a method of determining optimal state estimates in the presence of significant error in the assumed (nominal) model. The new real-time nonlinear filter determines (i.e., “predicts”) the optimal model error trajectory so that the measurement-minus-estimate covariance statistically matches the known measu...

Digital images are often corrupted by different noises. These noises can be removed by designing an efficient filter. Based upon the specified operation the pixels are classified. Absolute Difference Based Progressive Switching Median Filter (ADBPSMF) outperforms all other filter for the removal of impulse noise from corrupted images. This filter performs two basic operations for efficient nois...

In this paper, a new square-root domain, first order, all-pass filter based on the MOSFET square law is presented. The proposed filter is designed by using nonlinear mapping on the state variables of a state space description of the transfer function. To the best knowledge of the authors, the filter is the first square-root domain first order all-pass structure designed by using state space syn...

In this paper, the finite impulse response (FIR) filter based on an exponential quadratic cost function is proposed for a stochastic discrete-time state space model. The joint probability density function of the current state and the external noises on the recent finite horizon is introduced and the corresponding expected value of the exponential quadratic cost function is minimized with respec...

The problem of estimating the number and state of multiple targets using a sensor with limited sensing ability is raised in a variety of applications, including monitoring of endangered species, civilian security, and military surveillance. The particle filter is widely used to solve this problem since Kalman filter’s disadvantage on estimating non-Gaussian distribution. However, the problem be...

The relation between the discrete time Lainiotis filter on the one side and the golden section and the Fibonacci sequence on the other is established. As far as the random walk system is concerned, the relation between the Lainiotis filter and the golden section is derived through the Riccati equation since the steady state estimation error covariance is related to the golden section. The relat...

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This...

We consider robust recursive filtering in the case of a linear, finite-dimensional and timediscrete state-space model with Euclidean state space. Insisting on recursivity for computational reasons, we come up with a new procedure, the rLS-filter, using a Huberized correction-step in the Kalman-filter recursions. Simulation results for ideal and contaminated data indicate that this procedure ach...

We combine conditional state density construction with an extension of the Scenario Approach for stochastic Model Predictive Control to nonlinear systems to yield a novel particle-based formulation of stochastic nonlinear output-feedback Model Predictive Control. Conditional densities given noisy measurement data are propagated via the Particle Filter as an approximate implementation of the Bay...

This work solves the signal reconstruction problem involving nonuniform filter bank systems with rational decimation factors and noises. Three main nonuniform filter bank systems, i.e., filter-block decimator (FBD) structure, upsamplerfilter-downsampler (UFD) structure, and tree structure, are included in this study. According to different operating conditions, two different signal reconstructi...