One of the most important problems when designing controller is how to deal with all kinds of uncertainties, which, along with the high nonlinearities of most real systems, makes it difficult to guarantee the desired closed loop performance. Recently, nonlinear Kalman-class filter has been extensively researched and several well-known algorithms, including Extended Kalman Filter (EKF), Unscente...

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient i...

The localization of objects equipped with passive UHF RFID labels using the Received Signal Strength Indicator (RSSI) values is investigated. An Unscented Kalman Filter (UKF) is used for the localization of the object. Adding (N-1) additional tags with known relative position to the object (relative to its main tag), this information can be incorporated into the UKF resulting in an Unscented Ka...

In this short paper, we describe the tractography method based on the intrinsic unscented Kalman filer (IUKF) [1]. This method is a generalization of unscented Kalman filter (UKF) and involves the use of intrinsic geometry of the space of symmetric positive definite matrices denoted henceforth by Pn. In this filter, operations that are intrinsic to Pn are employed and thus no explicit constrain...

We introduce the Unscented von Mises–Fisher Filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the n-dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, for example when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, ...

This paper presents the results of a quaternion-based unscented Kalman filtering for attitude estimation using low cost MEMS sensors. The unscented Kalman filter uses the pitch and roll angles computed from gravity force decomposition as the measurement for the filter. The immeasurable gravity accelerations are deduced from the outputs of the three axes accelerometers, the relative acceleration...

This paper focuses on applying a neural network model to predict pseudorange corrections (PRC) for differential Global Positioning System (DGPS). The class of nonlinear autoregressive recurrent neural networks is chosen as the basic architecture. The neural networks are trained by an unscented Kalman filter due to its powerful capabilities for online parameters estimation. The paper first brief...

This paper aims to investigate several new nonlinear/non-Gaussian filters in the context of the sequential data assimilation. The unscentedKalman filter (UKF), the ensemble Kalman filter (EnKF), the sampling importance resampling particle filter (SIR-PF) and the unscented particle filter (UPF) are described in the state-space model framework in the Bayesian filtering background. We first evalua...

The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the...