This paper investigates the possibility estimating the direction of arrival (DOA) in a system identification perspective. The system is modeled as an autoregressive (AR) process and extended Kalman filter (EKF) is used to estimate the DOA, which forms a state of the augmented state vector of the EKF. The states generate the signals at a linearly phased array. Simulation results demonstrate the ...

This paper investigates the possibility estimating the direction of arrival (DOA) in a system identification perspective. The system is modeled as an autoregressive (AR) process and extended Kalman filter (EKF) is used to estimate the DOA, which forms a state of the augmented state vector of the EKF. The states generate the signals at a linearly phased array. Simulation results demonstrate the ...

Accurate position and velocity estimation of a re-entry vehicle is essential for realizing its deviation from the desired descent trajectory and providing necessary guidance command in real-time. Generally the Extended Kalman Filter (EKF) is utilized for position and velocity estimation of a space vehicle. However, in the EKF the error covariance is predicted by linearizing the non-linear dynam...

We present a simplified drift-flux model (DFM) describing a multiphase (gas-liquid) flow during drilling. The DFM uses a specific slip law, without flow-regime predictions, which allows for transition between single and two phase flows. With this model, we design an Unscented Kalman Filter (UKF) for estimation of unmeasured states, production parameters and slip parameters using real time measu...

The lithium-ion battery is a viable power source for hybrid electric vehicles (HEVs) and, more recently, electric vehicles (EVs). Its performance, especially in terms of state of charge (SOC), plays a significant role in the energy management of these vehicles. The extended Kalman filter (EKF) is widely used to estimate online SOC as an efficient estimation algorithm. However, conventional EKF ...

The traditional Extended Kalman filter (EKF) is a useful tool for structural parameter identification with limited observations. It is, however, not applicable when the excitations on the structure are unknown or the excitation locations are not monitored. A novel Extended Kalman filter approach referred to as the General Extended Kalman filter with unknown inputs (GEKF-UI) is proposed to estim...

This paper presents a technique for multiUAV localization using ranging measurements from Two-Way Time-Of-Flight Ultra-Wideband (UWB) transceivers. In continuation with our previous work, the use of the Extended Kalman Filter (EKF) estimate is extended by fusing with other sensors to achieve a usable altitude estimate. Besides EKF, another method based on Non-linear Regression (NLR) is also dev...

This paper is an attempt to generalize the results obtained earlier and presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of parti...

We benchmark in this article three different parametrizations for punctual landmarks in monocular 6DOF EKF-SLAM. These parametrizations are homogeneous points (HM), inverse-distance points (IDP, better known as inversedepth), and the new anchored homogeneous points (AHP). The discourse used for describing them is chosen to highlight their differences and similarities, showing that they are just...

In this paper we show that all processes associated to the move-sense-update cycle of EKF SLAM can be carried out in time linear in the number of map features. We describe Divide and Conquer SLAM, an EKF SLAM algorithm where the computational complexity per step is reduced from O(n) to O(n) (the total cost of SLAM is reduced from O(n) to O(n)). In addition, the resulting vehicle and map estimat...