The extended Kalman filter (EKF) is considered one of the most effective methods for both nonlinear state estimation and parameter estimation (e.g., learning the weights of a neural network). Recently, a number of derivative free alternatives to the EKF for state estimation have been proposed. These include the Unscented Kalman Filter (UKF) [1, 2], the Central Difference Filter (CDF) [3] and th...

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extr...

Accurate estimation of the motion and shape of a moving object is a challenging task due to great variety of noises present from sources such as electronic components and the influence of the external environment, etc. To alleviate the noise, the filtering/estimation approach can be used to reduce it in streaming video to obtain better estimation accuracy in feature points on the moving objects...

This paper describes a new approach for mobile robot navigation using an interval analysis based adaptive mechanism for an Unscented Kalman filter. The robot is equipped with inertial sensors, encoders and ultrasonic sensors. The map used for this study is two-dimensional and it is assumed to be known a-priori. Multiple sensor fusion for robot localisation and navigation has attracted a lot of ...

Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are often used in target tracking, but the required Posterior Density Function (PDF) is still approximated by a Gaussian, which may be a gross distortion of the true underlying structure and lead to filter divergence when performing EKF or UKF. Because the uncertainty of process model in bearing only tracking is small compared with ...

Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD) for single-rotor unmanned helicopters (UHs) is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on actuator healthy coefficients (AHCs), which are introduced to represent actuator faults, a combined dynamic model is established with the augmented state co...

Planetary rovers operate in environments where human intervention is expensive, slow, unreliable, or impossible. It is therefore essential to monitor the behavior of these robots so that contingencies may be addressed before they result in catastrophic failures. This monitoring needs to be efficient since there is limited computational power available on rovers. We propose an efficient particle...

The multi-step prediction problem of chaotic time series with one sampling delay is investigated in this paper. The delay is considered to be random and is modelled by a binary white noise with values of zero or one, and these values indicate that the observation arrives on time or that it is delayed by one sampling time. Based on the original extended Kalman filtering (EKF) and the Unscented K...

The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking the degradation of lithium-ion batteries. It is important to predict the capacity of a lithium-ion battery for future cycles to assess its health condition and remaining useful life (RUL). In this paper, a novel method is developed using unscented Kalman filter (UKF) with relevance vector regre...

Vehicle tracking in the field of intelligent transportation has received extensive attention recent years. Multi-sensor-based vehicle system is widely used some critical environments. However, actual scenes, observation error each sensor often different and time varying because environmental change channel difference. Therefore, this paper, we propose a multi-sensor interacted vehicle-tracking ...