Successful climate prediction at seasonal-to-interannual time scales may depend on the optimal initialization of the land surface states, in particular soil moisture (Koster and Suarez 2001). Such optimal initialization can be achieved by assimilating soil moisture observations into the land model prior to the forecast. We assess the performance of the Extended Kalman filter (EKF) and the Ensem...

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimati...

This paper describes the design of a system for estimating the pose of a camera mounted on a vehicle which moves over a flat floor. The system has been designed to work in conjunction with an obstacle avoidance scheme. Measurements are derived from a sequence of images captured by a single camera rigidly mounted on the vehicle and looking at the floor, and combined with measurements of the vehi...

The aim of this paper is to present a comparison of some popular methods for online harmonic estimation. The wellknown methods Descrete Fourier Ttransform (DFT), Enhanced Phase Locked Loop (EPLL), Adaptive Notch Filter (ANF) and method based on Extended Kalman Filter (EKF) are simulated and compared. The methods are compared in critical phases, such as the fast change of harmonic amplitudes and...

For the mobile robot Simultaneous Localization and Mapping (SLAM),a new algorithm is proposed, and named Adaptive Iterated Square-Root Cubature Kalman Filter based SLAM algorithm (AISRCKF-SLAM). The main contribution of the algorithm is that the numerical integration method based on cubature rule is directly used to calculate the SLAM posterior probability density. To improve innovation covaria...

Extended Kalman filter (EKF) is widely used for tracking moving objects like missiles, aircrafts, robots etc. In this paper we examine the case of a single sensor or observer bearing only tracking (BOT) problem for two different models. In model 1, the target is assumed to have a constant velocity and constant course. In model 2, the target is assumed to follow a coordinated turn model with con...

Evolving Artificial Neural Networks (ANN) is a new method that, except of the training, was applied to the structure optimization problem. This method combines ideas from both the evolution and adaptive signal processing techniques. An ANN is considered as a layered array of non-linear systems (the neuron models), each producing on its output a local error. Each of these errors is minimized usi...

In this paper a new Adaptive Unscented Kalman Filter (AUKF) is proposed and applied for the state estimation of a LEO (Low earth Orbit) satellite planar model. The Unscented Kalman Filter (UKF) is preferred here because of its derivative free calculation process and superior performance in highly non linear systems. Further the choice of adaptive filter gives the opportunity to estimate the sta...

The ensemble Kalman filter (EnKF) is a technique for dynamic state estimation. EnKF approximates the standard extended Kalman filter (EKF) by creating an ensemble of model states whose mean and empirical covariance are then used within the EKF formulas. The technique has a number of advantages for large-scale, nonlinear problems. First, large-scale covariance matrices required within EKF are re...

st , θ ⊤ 0 , θ ⊤ 1 , . . . , θ ⊤ K )⊤ is the state vector. (Note that the superscript t refers to the set of variables at time t.) In general, the complexity of computing such a density grows exponentially with time; to make the computation tractable, the true state is being assumed to be an unobserved Markov process implying that • the true state is conditionally independent of all earlier sta...