The radar tracking is one of the best LEO satellite tracking methods. While Since the tracking FILTERs which are mostly linear, and are not able to have a precise estimation of the objects with nonlinear motion dynamics such as satellite, we should use nonlinear FILTERs. In this paper, firstly, we deal with the problem of the LEO satellites motion path modelling according to the satellite motion emulation with the Cowell equations. Then the observations will be separately fed to non-linear EXTENDED KALMAN FILTER as well as Unscented KALMAN FILTER and with studying theRMS position estimation error, their performance for satellite tracking will be evaluated. Simulation results demonstrate that the UKF FILTER has a better performance in terms of accuracy in comparison with the EKF.

The error of the inertial navigation system (INS) increases with time and leads the navigation system to instability. Hence, this paper investigates INS/GPS integration. KALMAN FILTER is the most common way for integrating these two systems, but due to the nonlinear behavior of the INS/GPS integrated navigation system; nonlinear FILTERs are used for data integration. Furthermore, given that GPS is capable of measuring the velocity and position of the object, these measurements are used to estimate system states (position, velocity, and orientation). In the following, we have investigated the observability of the system’ s state space. Using practical data from a UAV, simulation results shows that the performance of the particle FILTER is better than that of the other two estimators for complex nonlinear systems with non-Gaussian noise.

Human motion tracking is a significant problem in the rehabilitation phase of people with leg injuries. To monitor and analyze them in a reliable way under low cost, Knee and thigh angles of the human leg are estimated using sensors. The human leg is modeled as a two link revolute joint robot. Initially, switched linear models of the human leg are considered. Since linear models are considered, KALMAN FILTERing algorithm is applied to obtain the values of the estimates. Results are obtained for KALMAN FILTERing algorithm and it is observed that, estimates cannot be obtained on using KALMAN FILTERing algorithm. On considering the non-linearity of the human leg, the nonlinear model is obtained. The parameters are estimated using the EXTENDED KALMAN FILTERing algorithm. The results are obtained and are reliable. Based on these values, the rate of recovery of the patient during rehabilitation phase can be assessed. Furthermore, this data can be sent to physicians over the Internet of Things.

Estimates of instrumental rules can be utilized to describe central bank's behavior and monetary policy stance. In the last decade, considerable attention has been given to time-varying parameter (TVP) specification of monetary policy rules. Constant-parameter reaction functions likely ignore the impact of model uncertainty, shifting preferences and nonlinearities of policymaker's choices. This paper examines the evolution of monetary policy reaction function in Iran via estimating a time-varying parameter (TVP) specification in the 1990: 2-2014: 4 period. We try to find out whether there is a significant time variation in coefficient of CBI (the Central Bank of Iran) reaction function. The main findings are threefold. First, monetary policy rules changed over time, hence making relevant the application of a time-varying estimation framework. Second, the monetary instrument smoothing parameter is much lower than typically reported by previous time-invariant estimates of policy rules. Third, CBI does not systematically follow instrumental rule to fight inflation. During the whole sample, there is no quarter in which the inflation gap coefficient is negative and significant; therefore, monetary policy has not counteracted inflationary pressures.

The goal of the Doppler and Bearing Tracking (DBT) as a kind of passive target tracking problem is to estimate the position and velocity of the target using its transmitted signal. The main problem of this kind of target tracking is nonlinearity of the measurement equations. In order to solve this problem، different approaches have been reported in the literature، such as EXTENDED KALMAN FILTER. However، bias and dependence on the initial conditions are the key shortcomings of such FILTERs. In this paper، first، a novel technique is proposed to provide an appropriate initial condition for the FILTER. Then، inspired by the modified covariance EXTENDED KALMAN FILTER، a new adaptive EXTENDED KALMAN FILTER is introduced. Here، the measurement and the process noise covariances are updated simultaneously for reducing the bias effects. Finally، the performance of the proposed FILTER is compared with the standard EXTENDED KALMAN FILTER، adaptive EXTENDED KALMAN FILTER and unscented KALMAN FILTER. Results show the good performance of the proposed FILTER in the problem under study

The flight condition distinguishing is essential for calculation of elapsed time in each regime. The pilots perform different flight regimes during operation which recognize them by combination of flight parameters. Thus, the flight regimes can be defined based on the qualitative descriptions by pilots. Nevertheless, the relation between flight parameters and maneuvers is so complicated and there is no precise mathematic model for flight regime recognition. In this research, a flight regime recognition algorithm is developed based on the qualitative description of maneuvers. A connection matrix is formed using maneuver description to FILTER the measured flight data and the algorithm identifies the flight regimes. The proposed flight regime recognition algorithm utilized the adaptive EXTENDED KALMAN FILTER (AEKF). Using AEKF results in no need for big flight data bank, less sensitivity to initial values and variations, and increases the accuracy during time in contrast with the exiting online regime recognition methods. The algorithm effectiveness is evaluated for the simulated flight data from a validated helicopter dynamic model.

The flight condition distinguishing is essential for calculation of elapsed time in each regime. The pilots perform different flight regimes during operation which recognize them by combination of flight parameters. Thus, the flight regimes can be defined based on the qualitative descriptions by pilots. Nevertheless, the relation between flight parameters and maneuvers is so complicated and there is no precise mathematic model for flight regime recognition. In this research, a flight regime recognition algorithm is developed based on the qualitative description of maneuvers. A connection matrix is formed using maneuver description to FILTER the measured flight data and the algorithm identifies the flight regimes. The proposed flight regime recognition algorithm utilized the adaptive EXTENDED KALMAN FILTER (AEKF). Using AEKF results in no need for big flight data bank, less sensitivity to initial values and variations, and increases the accuracy during time in contrast with the exiting online regime recognition methods. The algorithm effectiveness is evaluated for the simulated flight data from a validated helicopter dynamic model.

This paper proposes a new adaptive EXTENDED KALMAN FILTER (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed FILTER is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the number of noise parameters that need to be tuned in the EXTENDED KALMAN FILTER (EKF). Furthermore, the AEKF updates the noise covariances to enhance FILTER stability. Most importantly, in the worst case, the AEKF converges to the conventional EKF. The AEKF performance is determined based on the mean square error (MSE) performance measure and the stability is proven. The results illustrate that the proposed AEKF has a dramatic improved performance over the conventional EKF, the estimates are more stable with less noise.

In this paper a novel optimal and robust guidance law for three dimensional flying-object system is proposed based on the EXTENDED KALMAN FILTER. New guidance law consists of sliding mode and backstepping guidance laws. In this guidance law there are some coefficients that must be adjusted. These coefficients are adjusted using Genetic Algorithm (GA). Also system states are estimated by using of Continues EXTENDED KALMAN FILTER (CEKF). Finally, proposed guidance law is compared with Augmented Proportional Navigation Guidance (APNG) law. Simulation results show that proposed guidance law is better than APNG law.