Sharif Digital Repository

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information. An autoregressive moving average with exogenous input (ARMAX) model of HRV is considered with a parametrically modeled respiration signal as the input. The model parameters are... 

Control of a class of uncertain nonlinear systems which estimates unavailable state variables is considered. A new approach for robust tracking control problem of satellite for large rotational maneuvers is presented in this paper. The features of this approach include -a strong algorithm to estimate attitude, based on discrete extended kalman filter combined with a continuous extended kalman filter and attitude nonlinear model -a robust controller based on slidingmode with perturbation estimation. The estimation result of interval kalman filtering is a sequence of interval estimates that encompasses all possible optimal attitude estimates, which the interval system may generate. The... 

In this paper an Extended Kalman Filter (EKF) has been proposed for the filtering of noisy ECG signals. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. An automatic parameter selection method has also been suggested, to adapt the model with a vast variety of normal and abnormal ECG signals. The results show that the EKF output is able to track the original ECG signal shape even in the most noisiest epochs of the ECG signal. The proposed method may serve as an efficient filtering procedure for applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals. © 2005 IEEE  

In this paper, a high performance procedure for estimating of hydrodynamic coefficients in Autonomous Underwater Vehicles (AUV's) is proposed. In modeling of an AUV, experimental data should be verified and validated using appropriate techniques. Due to implementation complexity in calculating methods, computation of hydrodynamic parameters is challenging. This paper presents analytical approaches for estimating an AUV's hydrodynamic coefficients. Nonlinear Kalman Filter (KF) algorithms are implemented to estimate unknown augmented states (coefficients). A comparative study is conducted which shows the superior performance of Unscented Kalman Filter (UKF) in comparison with Extended Kalman... 

In this paper via a novel method of discretized continuous-time Kalman filter, the problem of synchronization and cryptography in fractional-order systems has been investigated in presence of noisy environment for process and output signals. The fractional-order Kalman filter equation, applicable for linear systems, and its extension called the extended Kalman filter, which can be used for nonlinear systems, are derived. The result is utilized for chaos synchronization with the aim of cryptography while the transmitter system is fractional-order, and both the transmitter and transmission channel are noisy. The fractional-order stochastic chaotic Chen system is then presented to apply the... 

In this paper we present a new landmark initialization technique for Bearing-Only Simultaneous Localization and Mapping (SLAM) algorithm. The initialization scheme is a type of the delayed constrained initialization method and is different from previous approaches. In our work, it is shown that the angle between sequential observations measured by a bearing-only sensor such as pan-tilt camera and the distance between the vehicle and landmark plays an important role in landmark localization accuracy. Considering this fact, a proper constrained function that utilizes the rotation angle of the camera is applied between multiple landmark location estimates and the best estimate is selected. The... 

In this paper, we propose an efficient method for R-peak detection in noninvasive fetal electrocardiogram (ECG) signals which are acquired from multiple electrodes on mother's abdomen. The proposed method is performed in two steps: first, we employ a robust tensor decomposition-based method for fetal ECG extraction, assuming different heart rates for mother and fetal ECG; then a method based on extended Kalman filter (EKF) in which the ECG beat is modeled by 3 state equations (P, QRS and T), is used for fetal R-peak detection. The results show that the proposed method is efficiently able to estimate the location of R-peaks of fetal ECG signals. The obtained average scores of event 4 and 5 on... 

In this paper the derivation of Kalman filter for discrete time-stochastic fractional system is investigated. Based on a novel cumulative vector form model for fractional systems, a general Kalman filter is introduced. The validity of the proposed method has been compared with a previously presented method via simulation results. It is shown that this method can be better applied for discrete time stochastic fractional systems with slower dynamics  

In this paper, we proposed a method based on Kalman Filter for predicting the onset of paroxysmal atrial fibrillation (PAF) from the electrocardiogram (ECG) using clinical data available from the Computers in Cardiology (CinC) Challenge 2001. To predict PAF, we developed an algorithm based upon the number of atrial premature complexes (APCs) in the ECG. The algorithm detects classical isolated APCs by monitoring fidelity signals, which is defined here as a function of the innovation signal of Kalman filter, in vicinity of premature heartbeats and decides whether one beat is APC or not then predicts PAF, based on the number of APC. The challenge database consists of 56 pairs of 30-minute ECG... 

Purpose - This paper aims to develop an adaptive unscented Kalman filter (AUKF) formulation for orientation estimation of aircraft and UAV utilizing low-cost attitude and heading reference systems (AHRS). Design/methodology/approach - A recursive least-square algorithm with exponential age weighting in time is utilized for estimation of the unknown inputs. The proposed AUKF tunes its measurement covariance to yield optimal performance. Owing to nonlinear nature of the dynamic model as well as the measurement equations, an unscented Kalman filter (UKF) is chosen against the extended Kalman filter, due to its better performance characteristics. The unscented transformation of the UKF is shown... 

An adaptive extended Kalman filter is designed to estimate the arc length in a gas metal arc welding system. The simulation results show that the estimated variables track the true variables of the non-linear model with negligible error and are robust against parameters uncertainties. The proposed estimator also operates adequately in a highly noisy welding environment. Because of the low computational requirements and little lag produced in the process dynamic, use of the proposed estimator would be valuable in the design of a controller for the gas metal arc welding system  

This paper introduces an innovative frequency estimation approach that relies on a new and innovative structure for Kalman Filter (KF) in signal processing. Kalman Filtering, because of its state estimating nature, excels other methods in realtime processing. However, the main drawback of Kalman Filtering is that it involves time-consuming matrix operations which makes it inferior to faster methods like fast Fourier transform (FFT). The most evident privilege of the new structure is that it "synthesizes" the components rather than "extracts" them as standard Kalman Filter does. This parallel synthesis of signal components reduces the computational order of the Kalman Filter dramatically. In... 

In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model's parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions' true values. Through the extended Kalman... 

This paper reports the estimation of angular velocity in micro gyroscopes with parametric excitation. The identification procedure is done into two consecutive steps: In the first step, the physical parameters of the gyroscope (stiffness, damping, and actuator parameters) are estimated via continuous time Extended and Unscented Kalman filter. In the second step, a separate Kalman filter is dedicated to estimate the time varying rotation rate, using the output of the first step. Using numerical simulations, it is found that by introducing an artificial noise in the observer equations and tuning its variance, arbitrary temporal variation of angular velocity can be well tracked by the proposed... 

Orbit determination (OD) problem utilizing onboard sensors is a key requirement for many current and future space missions. Though there exists ample research and work on this subject, a novel algorithm is presented in this paper for the nonlinear problem of OD. In this regard, initially a new cubature-quadrature particle filter (CQPF) that uses the square-root cubature-quadrature Kalman filter (SR-CQKF) to generate the importance proposal distribution is developed. The developed CQPF scheme avoids the limitation of the standard particle filter (PF) concerning new measurements. Subsequently, CQPF is enhanced to take advantage of the relative entropy (Kullback-Leibler Distance) criterion to... 

Abstract Three-axis magnetometers (TAMs) have been widely utilized as the cornerstone of integrated navigation (IN) and attitude determination (AD) in many aerospace systems. However, accurate navigation and AD demands for precise calibration of TAM. For this purpose, a complete TAM calibration process is presented in the current research to compensate all of the key errors. In this regard, a hyper least square (HyperLS) estimator is extended for accurate and consistent ellipsoid fitting problem of TAM calibration. Subsequently, the calibrated TAM is utilized for real time attitude determination via nonlinear colored noise filters of extended Kalman filter, simplex unscented Kalman filter... 

Positioning for kinematic applications is of great importance in terms of accuracy and computational complexity. In this paper, we present an algorithm based on correlation between error components in three directions, i.e. Δx, Δy, Δz to decrease positioning error of a GPS receiver for kinematic applications. The main feature of proposed algorithm is that it modifies data at each instant and in each direction using data of two other directions at the same instant (i.e. some sort of online modification). This feature was acquired using an approach introduced to model state transition matrix using AR coefficients of two other directions. The algorithm was implemented on the output raw position... 

A continuous-discrete Distributed and Decentralized Switching Kalman Filter (DDSKF) is designed for estimation of spatial profiles in Pressure Swing Adsorption (PSA) processes. The introduced observer is an integral part of the control strategy of hybrid systems in general and PSA systems in particular. A reduced order model is developed based on the mechanistic model of the process. The sensors are optimally located and observability of the process is studied. The proposed observer is used to estimate the spatial profiles of various states of a two-bed, six-step PSA system used for production of pure H2 from a H2–CH4 gas mixture. The spatial profiles of the system have been estimated using... 

Gaussian approximation filters have increasingly been developed to enhance the accuracy of attitude estimation in space missions. The effective employment of these algorithms demands accurate knowledge of system dynamics and measurement models, as well as their noise characteristics, which are usually unavailable or unreliable. An innovation-based adaptive filtering approach has been adopted as a solution to this problem; however, it exhibits two major challenges, namely appropriate window size selection and guaranteed assurance of positive definiteness for the estimated noise covariance matrices. The current work presents two novel techniques based on relative entropy and confidence level... 

In this paper, a novel scheme of command to line-of-sight guidance law regarding the outstanding practical limitations is proposed. With the aid of formulating problem in pursuer's line of sight frame, the exact feed-forward acceleration commands are derived for a general target motion in three-dimensional engagement geometry. Then, a nonlinear control scheme constructed from feed-forward terms along with a nested saturated feedback strategy is suggested. Two momentous practical limitations are considered. The first is related to existence of pursuer's acceleration saturation which is ignored in all existing solutions. The required conditions to insure the global stability are derived...