People tracking is an essential part for modern service robots. In this paper we compare three different Bayesian estimators to perform such task: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) Particle Filter. We give a brief explanation of each technique and describe the system implemented to perform people tracking with a mobile robot usi...

An accurate and low-cost level measurement method based on acoustic resonance is presented. The method is useful in the cases that measurements are noisy which might be due to environment noise or due to low-cost instruments used in the measurement process. An iterative nonlinear filtering algorithm, called the “Unscented Kalman filter” (UKF) has been employed to obtain a good estimate of the n...

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...

Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended ...

An adaptive High-Gain observer (AHG) as well as an Extended (EKF) and Unscented Kalman filter (UKF) are implemented for joint state and parameter estimation of a novel multi-axial electromagnetically actuated punch. These observers are compared in terms of convergence and response time to erroneous parameter and state initialization, as well as parameter modifications during operation. The AHG ...

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...

NI-13. RESOLVING THE CHALLENGES OF PERITUMORAL EDEMA IN TRACING ARCUATE FASCICULUS FOR SURGICAL PLANNING USING TWO-TENSOR UNSCENTED KALMAN FILTER TRACTOGRAPHY Zhenrui Chen1,4, Yanmei Tie1, Lauren O’Donnell1,2, Laura Rigolo1, Alireza Mehrtash1,2, Olutayo Olubiyi1, Isaiah Norton1, Ofer Pasternak3, Yogesh Rathi3, and Alexandra Golby1,2; Department of Neurosurgery, Brigham and Women’s Hospital, Har...

State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH method considering influence temperature. It combines Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total (TLS) Unscented Kalma...

Accurate state of charge (SOC) estimation is great significance to promote the development new energy vehicles. And a battery model’s accuracy for SOC accuracy. To this end, method based on back propagation neural network-Ant Lion Optimizer (BPNN-ALO) and unscented Kalman filter (UKF) proposed. First, 2-RC model established BPNN used fit OCV-SOC corresponding relationship. Second, ALO are combi...

Abstract Aiming at the problem of rapid transfer alignment inertial navigation system in shipborne environment with large azimuth misalignment angle, a dimension reduction fast scheme aircraft based on CKF algorithm is designed. Firstly, nonlinear attitude error equation, velocity equation and position Global Navigation Satellite System (GNSS) assisted strapdown case are analyzed. Secondly, dim...