The underwater glider has the advantages of low power, long endurance and high accuracy. Micro-Electro-MechanicalSystem (MEMS) grade inertial sensors are more suitable for an underwater glider because of their low cost and small size. Models of MEMS sensor noises which include not only the white noises and random walk terms but also the bias instabilities of the sensor noises are analyzed. The ...

The standard formulations of the Kalman filter (KF) and extended Kalman filter (EKF) require the storage and multiplication of matrices of size n × n, where n is the size of the state space, and the inversion of matrices of size m × m, where m is the size of the observation space. Thus when both m and n are large, implementation issues arise. In this paper, we advocate the use of the limited me...

The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is propose...

Dual estimation refers to the problem of simultaneously estimating the state of a dynamic system and the model which gives rise to the dynamics. Algorithms include expectation-maximization (EM), dual Kalman filtering, and joint Kalman methods. These methods have recently been explored in the context of nonlinear modeling, where a neural network is used as the functional form of the unknown mode...

The accurate state of charge (SOC) estimation of the LiFePO4 battery packs used in robot applications is required for better battery life cycle, performance, reliability, and economic issues. In this paper, a new SOC estimation method, “Modified ECE + EKF”, is proposed. The method is the combination of the modified Equivalent Coulombic Efficiency (ECE) method and the Extended Kalman Filter (EKF...

We introduce the antiparticle filter, AF, a new type of recursive Bayesian estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. We show that for a classic problem of robot localization the AF can substantially outperform these other filters in some situations. The AF estimates the posterior distribution as an auxiliary variable...

Abstracr-The problem of delay estimation in the presence of multipath is considered. It is shown that the extended Kalman filter (EKF) can be used to obtain joint estimates of time-of-arrival and multipath coefficients for deterministic signals when the channel can be modeled by a tapped-delay line. Simulation results are presented for the EKF joint estimator used for synchronization in a direc...

Robot localization using odometry and feature measurements is a nonlinear estimation problem. An efficient solution is found using the extended Kalman filter, EKF. The EKF however suffers from divergence and inconsistency when the nonlinearities are significant. We recently developed a new type of filter based on an auxiliary variable Gaussian distribution which we call the antiparticle filter ...

In this paper, a dynamic model of car motion is proposed in which the turn of the steering wheel and the distance between the front and rear wheel are taken into account. Extended Kalman Filter (EKF) is widely used in visual tracking systems. However, because there is no direct link between the behaviour of the driver who controls the motion of the car and the assumed dynamic model, the traditi...

A new and fast methodology is discussed as a solution to pinpointing the location of a robot called RobVigil, in a robust way, without environment preparation, even in dynamic scenarios. This solution does not require a high computational power. Firstly, the EKF-SLAM is used to find the location of the vehicle, allowing to map the surrounding area in the 3D space. Afterwards, the constructed ma...