State estimation algorithm deals with recovering some desired state variables of a dynamic system from available noisy measurements, and estimation of the state variables is one of the fundamental and significant problems in control and signal processing areas, and many significant progresses have been made in this area. In 1940s, Wiener, the founder of the modern statistical estimation theory,...

The problem of narrowband interference suppression in impulsive noise environments is considered in this paper. To suppress the interference we introduce a new nonlinear estimator. The proposed estimator can be applied to any discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and with significantly im...

A nonlinear vision based tracking system is developed to provide estimates of the position and velocity of an Autonomous Underwater Vehicle (AUV) relative to an Autonomous Surface Craft (ASC). Nonlinear estimator design builds on the theory of linear parametrically varying (LPV) systems. The theoretical framework adopted provides a powerful tool for estimator regional stability and performance ...

in the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. in this study, hydrodynamic coefficients of an autonomous un...

We revisit the second-order nonlinear least square estimator proposed in Wang and Leblanc (Anne Inst Stat Math 60:883-900, 2008) and show that the estimator reaches the asymptotic optimality concerning the estimation variability. Using a fully semiparametric approach, we further modify and extend the method to the heteroscedastic error models and propose a semiparametric efficient estimator in ...

A nonlinear harmonic estimator (NHE) is proposed for extracting a harmonic signal and its fundamental frequency in the presence of white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a multiple sinusoidal model with staterepresentation and then efficiently specializing it for the case of harmonic estimation. The effectiveness of the NHE is verified usi...

In this paper, we present a new nonlinear receiver for the blind deconvolution of intersymbol interference (ISI) impaired data. The proposed receiver achieves fast identification of an unknown transmission channel using only one channel estimator and requiring the computation of only the second-order conditional statistics of the baud-rate sampled received signal and the knowledge of the transm...

In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...

State-dependent Riccati equation (SDRE) techniques are rapidly emerging as general design and synthesis methods of nonlinear feedback controllers and estimators for a broad class of nonlinear regulator problems. In essence, the SDRE approach involves mimicking standard linear quadratic regulator (LQR) formulation for linear systems. In particular, the technique consists of using direct paramete...

In this work, we describe a procedure to visualize nonlinear process dynamics using a self-organizing map based local model dynamical estimator. The proposed method exploits the topology preserving nature of the resulting estimator to extract visualizations (planes) of insightful dynamical features, that allow to explore nonlinear systems whose behavior changes with the operating point. Since t...