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...

One of the major challenges in human-robot interaction is how to enable the use of unrestricted hand motion as a tool for communication. The direct use of hand as an input tool enables the user to connect to systems more naturally, allowing them to become an integral part of our daily lives. A vision-based approach, using cameras to capture data, supports non-contact and unrestricted movement o...

Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed....

Different approaches for the estimation of the states of linear dynamic systems are commonly used, the most common being the Kalman filter. For nonlinear systems, variants of the Kalman filter are used. Some of these variants include the LKF (linearized Kalman filter), the EKF (extended Kalman filter), and the UKF (unscented Kalman filter). With the LKF and EKF, performance varies depending on ...

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...

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 ...

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...