Autonomous Robots and Vehicles need accurate positioning and localization for their guidance, navigation and control. Often, two or more different sensors are used to obtain reliable data useful for control system. This paper presents the data fusion system for mobile robot navigation. Odometry and sonar signals are fused using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS)...

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reve...

Particle filters are an alternative to approximate the Kalman filter for nonlinear problems. This paper intends to assess the potential of Particle Filter (PF) and its variants in the context of the state estimation problem of a three phase induction motor. The conventional Particle Filter (SIR-PF), and particle filters that employ importance sampling through proposal distributions such as Part...

This paper deals with spacecraft autonomous navigation in deep space missions. The considered problem is that of spacecraft localization based on angular measurements. The dynamic model of the spacecraft accounts for several perturbing effects, such as Earth and Moon gravitational field asymmetry and errors associated with the Moon ephemerides. The measurement process is based on elevation and ...

In the Magnetic Resonance Force Microscopy (MRFM) experiment that we consider, a cantilever is used to detect the presence of an electron spin. If an electron spin is present, the equation governing the motion of the cantilever is nonlinear. In the absence of a spin, the equation is linear. We propose a detector model that uses two Kalman Filter (KF) state estimators. The estimator for the spin...

This paper describes the passive emitter localization using Time Difference of Arrival (TDOA) measurements. It investigates various methods for estimating the solution of this nonlinear problem: the Maximum Likelihood Estimation (ML) as a batch algorithm, the Extended Kalman Filter (EKF) as an analytical approximation, the Unscented Kalman Filter (UKF) as a deterministic sampling approach and f...

In this study, it is aimed to design a speed sensorless DTC system for induction motors (IMs). All the states required for DTC system in addition to the load torque are estimated using an Extended Kalman Filter (EKF). Simulation results demonstrate a good performance and robustness.

This paper presents the first progress made at FGAN/FHR-RWA towards the development of a robust autonomous method for the tracking of space objects with the Tracking and Imaging Radar (TIRA). For the acquisition phase an algorithm based on a Least-Squares Estimation of the state vector and f and g series will be presented and tested. For the tracking phase the suitability of the Extended Kalman...

This paper is concerned with filtering nonlinear multivariate time series. A new approximate Bayesian algorithm is proposed which generates sample points and corresponding probability weights that match exactly the predicted values of average marginal skewness and average marginal kurtosis of the unobserved state variables, in addition to matching their mean and the covariance matrix. The perfo...

This paper considers a state estimation problem where the nominal system is linear but the sensor has a timevarying gain component, giving rise to a bilinear output equation. This is a general sensor self-calibration problem and is of particular interest in the problem of estimating wafer thickness and etch rate during semiconductor manufacturing using reflectometry. We explore the use of a lea...