Outdoor positioning for Unmanned Aerial Vehicles (UAVs) commonly relies on GPS signals, which might be reflected or blocked in urban areas. In such cases, additional on-board sensors such as Light Detection and Ranging (LiDAR) are desirable. To fuse GPS and LiDAR measurements, it is important, yet challenging, to accurately characterize the error covariance of the sensor measurements. In this p...

We present a framework for robust estimation of the configuration of an articulated robot using a large number of redundant proprioceptive sensors (encoders, gyros, accelerometers) distributed throughout the robot. Our method uses an Unscented Kalman Filter (UKF) to fuse the robot’s sensor measurements. The filter estimates the angle of each joint of the robot, enabling the accurate estimation ...

The continue-time unscented Kalman filter (UKF) is developed to estimate the state of a jet transport aircraft. The UKF is based on the nonlinear longitudinal aircraft equations of motion, and it is designed to provide estimates of horizontal and vertical atmospheric wind inputs. The optimal state and disturbance estimates are incorporated in feedback control laws based on the slowand fast-time...

The cubature Kalman filter (CKF), which is based on the third degree spherical–radial cubature rule, is numericallymore stable than the unscented Kalman filter (UKF) but less accurate than theGauss–Hermite quadrature filter (GHQF). To improve the performance of the CKF, a new class of CKFs with arbitrary degrees of accuracy in computing the spherical and radial integrals is proposed. The third-...

Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering ...

A typical way to update map is to compare recent satellite images with existing map data, detect new roads and add them as cartographic entities to the road layer. At present image processing and pattern recognition are not robust enough to automate the image interpretation system feasible. For this reason we have to develop an image interpretation system that rely on human guidance. More impor...

This paper explores and compares the nature of the non-linear filtering techniques on mobile robot pose estimation. Three non-linear filters are implemented including the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF). The criteria of comparison is the magnitude of the error of pose estimation, the computational time, and the robustness of each filt...

This article tackles the computational burden of propagating uncertainties in model predictive controller-based policy probabilistic model-based reinforcement learning (MBRL) system for an unmanned surface vehicles (USV). We proposed filtered control using unscented Kalman filter (FPMPC-UKF) that introduces (UKF) a more efficient uncertainty propagation MBRL. A USV based on FPMPC-UKF is develop...

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no apriori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion ( pixels) using multiple filter bank in an IR image sequ...

For various target tracking applications, it is well known that the Kalman filter optimal estimator(in minimum mean-square sense) to predict and estimate state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In case nonlinear systems, Extended filter(EKF) Unscented filter(UKF) are widely used, which can be viewed as approximations the(linear) in sense ...