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State estimation for systems with unknown state transfer matrix and multiplicative noise is very classical and important in signal processing and target tracking. Aiming at the problem, an adaptive estimation algorithm which can deal with unknown state transfer matrix and multiplicative noise has been presented by Dongsheng Chu for linear systems under without considering multiplicative noise in [1]...
Based on an exact kinematics model, this paper considers two strategies aimed at diagnosing the health of a 3-axis rate gyro. In the first strategy, noisy attitude measurements are used to estimate angular velocity; comparing these estimates to the actual rate-gyro measurements provides the means for assessing the health of the rate gyro. In the second strategy, noisy attitude and angular velocity...
This study applies the unscented Kalman filter (UKF) and the ensemble Kalman filter (EnKF) to estimate a contraction ratio of the McKibben pneumatic artificial muscle (PAM) and to present that the UKF is more effective than the EnKF in estimating the PAM length. Both filters were applied for a commercial PAM, FESTO DMSP-20-200N, to validate them. The estimation was conducted offline under three types...
This paper presents the implementation of an adaptive fading multiplicative extended Kalman filter (AFMEKF), applied to the problem of attitude estimation in the context of quadrotors. The extended Kalman filter is adapted for use with quaternions and made adaptive to account for inaccurate measurement information. Simulations have been conducted in order to validate the filter performance.
In order to increase the tracking performance of ballistic targets, various estimation algorithms have been implemented in the literature. Extended Kalman Filter is one of the most widely used estimation algorithm which uses the nonlinear system and measurement models and linearization methods to estimate the state and state covariances. In the first part of this study, a ballistic coefficient state...
Despite the ever-widening use across different sectors, the lithium-ion batteries (LiBs) still face serious concerns over their thermal vulnerability. Motivated by this problem, this paper, for the first time, seeks to reconstruct the three-dimensional temperature field of a LiB pack in real time. The major challenge lies in how to acquire a high-fidelity reconstruction with constrained computation...
We study the problem of distributed state estimation where a set of nodes are required to estimate the state of a system with stochastic state transition matrix based on their observations which have themselves measurement matrices evolving stochastically. We extend previous work on distributed diffusion Kalman filtering to the stochastic case, and propose a diffusion algorithm for linear least mean...
State estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in...
Marine researchers need consistent historical and georeferenced data from the marine environment in order to constantly monitor the biological condition of the habitat or to document delicate archeological sites. To overcome the difficulties related to the acquisition of high quantity of worthy data and to the accurate estimation of the position, the development of easy-to-use IT tools could certainly...
For the large-scale wireless mobile networks, the topology or global state information directly affects congestion control, traffic control, quality of service and so on. Due to the dynamic change of the opportunistic network structure, the node can not perceive the current state of the network, therefore, it is important to improve the routing quality by designing a topology aware algorithm that...
Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance,...
Accurate state-of-charge (SOC) estimation is essential to battery management system. The widely adopted estimation methods based on Kalman Filter (KF) fail to take the variable environmental conditions into consideration, which may result in a poor accuracy. This paper proposes a novel estimation model based on KF method to estimate SOC of Lithium-ion battery. In the proposed model, the noise variances...
Extended Kalman Filter (EKF) can not only be used for tracking L1 signal under high dynamic, but also combine with semi-codeless techniques to track high dynamic L2 signal. And EKF can hold stability lock with semi-codeless in the ionospheric scintillation. The method of maximum a posteriori (MAP) estimation is used to estimate the unknown W-bit to overcome anti-spoofing (AS). EKF based semi-codeless...
In this work, well known Sigma Point Kalman Filters (SPKFs); namely Unscented Kalman filter (UKF), the Cubature Kalman filter (CKF), and the Central Differences Kalman filter (CDKF) will be combined to the Smooth Variable Structure Filter (SVSF), in order to create stable and robust algorithms that can be applied to highly non-linear systems. The proposed algorithms will be applied into 4-DOF robotic...
Recently unmanned aerial vehicles have become one of the most interesting research topics among scientists. Although researchers are very concerned in this area, they generally use the same dynamical plant equations. Simulations using that ready-to-use plant equations are common methods in order to apply control or optimization theories. But, beyond simulations, in real time control of these systems...
This paper proposes a system for the motion characteristics estimation of multiple objects with uncertain quantity. The system employs background subtraction method based on Gaussian Mixture Model to detect objects and tracks them through an improved algorithm combining Camshift with Kalman filtering. By analyzing the directions and trails of targets, the system can figure out their pixel distance...
Worldwide the use of nonlineardevices have increased sharply in recent times. As a result, harmonic pollution has become a vital problem. Harmonic in the power system network interfere with the system equipments, disturbing their normal operation which can deteriorate the quality of the delivered power. In this context, this paper presents an efficient and fast method for estimation of harmonics and...
It's interesting for a collaborative group of mobile robots to be able to identify and localize each other without referring to external beacons. In this context, we are able to develop a localizer model by combining an extended Kalman filter with an ultrasonic sensing model and synchronize them by a communication protocol among mobile agents. The approach we present in this paper consists of embedding...
For saving energy, lead acid battery plays an important role in photovoltaic system. Battery state of charge estimation is a key function of battery management system due to the requirement of ensuring optimum operation and safety. Therefore, for achieving a fiable operation, it is necessary to develop an accurate model for the estimation of the state of charge (SOC) of battery. In this paper, a RC...
This paper is concerned with optimal estimation of the state of a Boolean dynamical systems observed through correlated noisy Boolean measurements. The optimal Minimum Mean-Square Error (MMSE) state estimator for general Partially-Observed Boolean Dynamical Systems (POBDS) can be computed via the Boolean Kalman Filter (BKF). However, thus far in the literature only the case of white observation noise...
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