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...

This paper is the second of a two-part series that discusses the implementation issues and test results of a robust Unscented Kalman Filter (UKF) for power system dynamic state estimation with non-Gaussian synchrophasor measurement noise. The tuning of the parameters of our Generalized MaximumLikelihood-type robust UKF (GM-UKF) is presented and discussed in a systematic way. Using simulations c...

The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performanc...

This paper presents a computer vision system for tracking and predicting flying balls in 3-D from a stereo-camera. It pursues a “textbook-style” approach with a robust circle detector and probabilistic models for ball motion and circle detection handled by state-of-theart estimation algorithms. In particular we use a Multiple-Hypotheses Tracker (MHT) with an Unscented Kalman Filter (UKF) for ea...

In this work, we formulate a state estimation scheme for a nonlinear hybrid system that is subjected to stochastic state disturbances and measurement noise using an interacting Multiple-Model Algorithm (IMM). In particular, we propose the use of an IMM Extended Kalman Filter (IMM-EKF) and an IMM Unscented Kalman filter (IMM-UKF), which belongs to the class of derivative free estimators to carry...

Accurate state estimation of large-scale lithiumion battery packs is necessary for the advanced control of batteries, which could potentially increase their lifetime through e.g. reconfiguration. To tackle this problem, an enhanced reduced-order electrochemical model is used here. This model allows considering a wider operating range and thermal coupling between cells, the latter turning out to...

Unscented Kalman Filter (UKF) (Julier & Uhlmann, 1997) was developed as an improvement of Extended Kalman Filter (EKF) (Grewal & Andrews, 2001) for discrete-time filtering of the nonlinear dynamic systems. Comparison between different statistical approaches on the state and parameter estimation of the dynamic systems revealed that the performance of UKF is superior to EKF in many Kalman Filter ...

Outdoor applications for small-scale Unmanned Aerial Vehicles (UAVs) commonly rely on Global Positioning System (GPS) receivers for continuous and accurate position estimates. However, in urban areas GPS satellite signals might be reflected or blocked by buildings, resulting in multipath or non-line-of-sight (NLOS) errors. In such cases, additional onboard sensors such as Light Detection and Ra...

In light of the intuition that a better symmetrical structure can further increase the numerical accuracy, the paper by Fan and Zeng 2009 developed a new sigma point construction strategy for the unscented Kalman filter UKF , namely, geometric simplex sigma points GSSP . This comment presents a different perspective from the standpoint of the numerical integration. In this respect, the GSSP con...

Sigma-Point Kalman Filters (SPKFs) are popular estimation techniques for high nonlinear system applications. The benefits of using SPKFs include (but not limited to) the following: the easiness of linearizing the nonlinear matrices statistically without the need to use the Jacobian matrices, the ability to handle more uncertainties than the Extended Kalman Filter (EKF), the ability to handle di...