Showing 8 results for Kalman Filter
F. Bagheri, H. Khaloozadeh, K. Abbaszadeh,
Volume 3, Issue 3 (7-2007)
Abstract
This paper presents a parametric low differential order model, suitable for
mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman
filter is proposed for recursively estimating the states and parameters of continuous–time
model with discrete measurements for fault detection ends. Typical motor faults as interturn
short circuit and increased winding resistance are taken into account. The models are
validated against winding function induction motor modeling which is well known in
machine modeling field. The validation shows very good agreement between proposed
method simulations and winding function method, for short-turn stator fault detection.
M. R. Mosavi,
Volume 5, Issue 4 (12-2009)
Abstract
This paper presents design and implementation of three new Infrared Counter-Countermeasure (IRCCM) efficient methods using Neural Network (NN), Fuzzy System (FS), and Kalman Filter (KF). The proposed algorithms estimate tracking error or correction signal when jamming occurs. An experimental test setup is designed and implemented for performance evaluation of the proposed methods. The methods validity is verified with experiments on IR seeker reticle based on a Digital Signal Processing (DSP) processor. The practical results emphasize that the proposed algorithms are highly effective and can reduce the jamming effects. The experimental results obtained strongly support the potential of the method using FS to eliminate the IRCM effect 83%.
S. Shaerbaf, S. A. Seyedin,
Volume 8, Issue 1 (3-2012)
Abstract
In recent years chaotic secure communication and chaos synchronization have received ever increasing attention. Unfortunately, despite the advantages of chaotic systems, Such as, noise-like correlation, easy hardware implementation, multitude of chaotic modes, flexible control of their dynamics, chaotic self-synchronization phenomena and potential communication confidence due to the very dynamic properties of chaotic nonlinear systems, the performance of most of such designs is not studied and so is not still suitable for wireless channels. To overcome this problem, in this paper a novel wide-band chaos-based communication scheme in multipath fading channels is presented, where the chaotic synchronization is implemented by particle filter observer. To illustrate the effectiveness of the proposed scheme, numerical simulations based on particle filter are presented in different channel conditions and the results are compared with two other EKF and UKF based communication scheme. Simulation results show the Remarkable BER performance of the proposed particle filter-based system in both AWGN and multipath fading channels condition, causes this idea act as a good candidate for asynchronous wide band communication.
M. Mousavi Moaiied, M. R. Mosavi,
Volume 12, Issue 1 (3-2016)
Abstract
In this paper, combined GPS and GLONASS positioning systems are discussed and some solutions have been proposed to improve the accuracy of navigation. Global Satellite Navigation System (GNSS) is able to provide position, velocity and time with respect to coordinated universal time. GNSS positioning is based on received satellite signals, so its performance is highly dependent on the quality of these received signals. The effect of noise and multi-path can often be large enough to produce significant errors in positioning. Satellite navigation is difficult in this situation. In such circumstances, GPS or GLONASS alone are often not able to ensure consistency and accuracy in positioning due to the absence (or low quality) of signals. The combination of these two systems is an appropriate solution to improve the situation. In positioning a receiver, one of the ways that is often used to reduce the error due to observation noise and calculation errors is Kalman Filter (KF) estimation. In this paper, some changes in the structure of the KF is applied to improve the accuracy of positioning. Process of updating KF's gain, is done in fuzzy form based on the parameters available in RINEX files, including the P code pseudo-range used as an input of the proposed fuzzy system. Simulation results show that applying a fuzzy KF based on P code pseudo-range on the available data sets, in terms of noise and blocking condition, reduces the positioning error respectively from 24 to 14 meters and 90 to 25 meters.
S. Khosroazad, N. Neda, H. Farrokhi,
Volume 12, Issue 3 (9-2016)
Abstract
Physical-layer network coding (PLNC) has the ability to drastically improve the throughput of multi-source wireless communication systems. In this paper, we focus on the problem of channel tracking in a Decode-and-Forward (DF) OFDM PLNC system. We proposed a Kalman Filter-based algorithm for tracking the frequency/time fading channel in this system. Tracking of the channel is performed in the time domain while data detection is implemented in the frequency domain. As an important advantage, this approach does not need for training of some subcarriers in every OFDM symbols and this, results in higher throughput, compared to other methods. High accuracy, no phase ambiguity, and stability in fast fading conditions are some other advantages of this approach.
M. H. Lazreg, A. Bentaallah,
Volume 15, Issue 1 (3-2019)
Abstract
This article presents a sensorless five level DTC control based on neural networks using Extended Kalman Filter (EKF) applied to Double Star Induction Machine (DSIM). The application of the DTC control brings a very interesting solution to the problems of robustness and dynamics. However, this control has some drawbacks such as the uncontrolled of the switching frequency and the strong ripple torque. To improve the performance of the system to be controlled, robust techniques have been applied, namely artificial neural networks. In order to reduce the number of sensors used, and thus the cost of installation, Extended Kalman filter is used to estimate the rotor speed. By viewing the simulation results using the MATLAB language for the control. The results of simulations obtained showed a very satisfactory behaviour of the machine.
Z. Kazemi, A. A. Safavi,
Volume 16, Issue 3 (9-2020)
Abstract
Kalman filtering has been widely considered for dynamic state estimation in smart grids. Despite its unique merits, the Kalman Filter (KF)-based dynamic state estimation can be undesirably influenced by cyber adversarial attacks that can potentially be launched against the communication links in the Cyber-Physical System (CPS). To enhance the security of KF-based state estimation, in this paper, the basic KF-based method is enhanced by incorporating the dynamics of the attack vector into the system state-space model using an observer-based preprocessing stage. The proposed technique not only immunizes the state estimation against cyber-attacks but also effectively handles the issues relevant to the modeling uncertainties and measurement noises/errors. The effectiveness of the proposed approach is demonstrated by detailed mathematical analysis and testing it on two well-known IEEE cyber-physical test systems.
R. Havangi,
Volume 16, Issue 4 (12-2020)
Abstract
The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome these problems. The proposed method uses an adaptive unscented Kalman filter (AUKF) filter to generate the proposal distribution, in which the covariance of the measurement and process of the state are online adjusted by predicted residual as an adaptive factor based on a covariance matching technique. In addition, it uses the genetic operators based strategy to further improve the particle diversity. The results show the effectiveness of the proposed approach.