Data assimilation in a two-dimensional hydrodynamic model for bays, estuaries and coastal areas is considered. Two different methods based on the Kalman filter scheme are presented. These include (1) an extended Kalman filter in which the error covariance matrix is approximated by a matrix of reduced rank using a square root factorisation (RRSQRT KF), and (2) an ensemble Kalman filter (EnKF) ba...

This paper analyses the velocity estimation of a target, from the Doppler filter using 1) Kalman filter 2) Adaptive Kalman filter 3) Kalman filter with state vector fusion 4) Adaptive Kalman filter with state vector fusion 5) State vector fused adaptive Kalman filter. Simulation through MATLAB gave good response for 4 and 5 algorithms under low signal to noise ratio. 2 and 3 algorithms gave bet...

State estimation is the common problem in every area of engineering. There are different filters used to overcome the problem of state estimation like Kalman filter, Particle filters etc. Kalman Filter is popular when the system is linear but when the system is highly non-linear then the different derivatives of Kalman Filter are used like Extended Kalman Filter (EKF), Unscented Kalman filter. ...

Kalman filtering is a well-established methodology used in various multi-sensor data fusion applications. In our experiment, we first obtain measurements from the accelerometer and gyroscope and fuse them using Kalman filter in an inertial measurement unit (IMU). We estimate Kalman filter output and estimation error. The affect of process noise and measurement noise on estimation error is teste...

This paper proposes a new radar tracking filter named Noise-estimate Particle Filter (NPF). Kalman filter and particle filter are popular filtering techniques for target tracking. The tracking performance of the Kalman filter severely depends on the setting of several parameters such as system noise and observation noise. However, it is an open problem how to choose proper parameters for variou...

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that pr...

A consistent systematic comparison of filter algorithms based on the Kalman filter and intended for data assimilation with high-dimensional nonlinear numerical models is presented. Considered are the Ensemble Kalman Filter (EnKF), the Singular Evolutive Extended Kalman (SEEK) filter, and the Singular Evolutive Interpolated (SEIK) filter. Within the two parts of this thesis, the filter algorithm...

This paper develops a framework for state-space estimation when the parameters of the underlying linear model are subject to uncertainties. Compared with existing robust filters, the proposed filters perform regularization rather than de-regularization. It is shown that, under certain stabilizability and detectability conditions, the steady-state filters are stable and that, for quadratically-s...