Structural and crack parameters in a continuous mass model are identified using Observer Kalman filter identification (OKID) and Eigen Realization Algorithm (ERA). Markov parameters are extracted from the input and out responses from which the state space model of the structural system is determined using Hankel matrix and singular value decomposition by Eigen Realization algorithm. The structural parameters are identified from the state space model. This method is applied to a lumped mass system and a cantilever which are excited with a harmonic excitation at its free end and the acceleration responses at all nodes are measured. The stiffness and damping parameters are identified from the extracted matrices using Newton-Raphson method on the structure. Later, cracks are introduced in the cantilever and all structural parameters are assumed as known priori, the unknown crack parameters such as normalized crack depth and its location are identified using OKID/ERA. The parameters extracted by using this algorithm are compared with other structural identification methods available in the literature. The main advantage of this algorithm is good accuracy of identified structural parameters.

Electronic support (ES) isadivision of electronic warfare. and is responsible for identification of tele communication and electronic systems. The most important part of electronic systems is radars. Therefore, Radar identification is very essential importance, such that can determine the electronic warfare power. Up to now, many algorithm shave been presented for radar identification whose maximum accuracyis 93% with 5% missing pulse and 5% noise. In this paper has been suggested an algorithm based on extended Kalman filter with the accuracy of97.2% and computation complexity of 3.23 N2. Duetothe recursiveequation, it can be implemented with parallel processing systems (systolic array).

in many autonomous mobile application, robots must be capable of analyzing motion of moving objects in their environment. during movement of robot the quallity of images is affected by quakes of camera which cause high errors in image processing outputs. in this paper, we propose a novel method to effectively overcome this problem using neural networks and Kalman filtering theory. this technique uses movement parameters of camera to resolve problems caused by error in image processing outputs. the technique is successfully applied in the MRL middle size soccer robots where ball motion detection has an especial importance in their decision making. experimental results are presented and 2.2% achieved error suggests that the combined approach performs significantly better than traditional techniques.

This paper proposes a new hierarchical identification method for fractional-order systems. In this method, a SISO (single input, single output) state space model has been considered in which parameters and also state variables should be estimated. By using a linear transformation and a shift operator, the system will be transformed into a form appropriate for identification of a fractional order system. Then, the unknown parameters will be identified through a recursive least squares method and the states will be estimated using a fractional order Kalman filter. This identification method is based on the hierarchical identification principle that reduces the computational burden and is easy to implement on computer. The promising performance of the proposed method is verified using two stable fractional-order systems.

This paper presents a new hybrid methodology for learning Sugeno-type fuzzy models via subtractive clustering, Adaptive Boosting Regression (AdaBoostR) and Unscented Kalman filter (UKF). The generated fuzzy models are used for modeling nonlinear benchmark processes. In the proposed procedure, first one fuzzy rule is generated by subtractive clustering algorithm from available data of a given nonlinear process. Then this fuzzy rule is considered as a base model and AdaBoostR is employed in order to combine some of the weak learners (i.e. rules). Parameters of a rule are coded as the state vector in UKF and then UKF is used for fine tuning of these parameters. Moreover, as the second proposed method, Linear Kalman filter (LKF) is utilized for adjusting only the output membership functions parameters (first order sugeno's parameters) of base models (i.e. rules). Three case studies are considered for illustrating the applicability of our proposed boosting methods. Results apparently show the obtained fuzzy models are superior to Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of both modeling accuracy and computational requirements. Also the comparison results confirm that the obtained fuzzy models are well comparable with those of achieved by one of the powerful and recently developed fuzzy identification methods.

The inertial navigation system is a dead reckoning system, thus initial alignment for an inertial navigation system plays an important role in the accuracy of it. In this paper, a novel approach for initial alignment in an inertial navigation system with increased speed and accuracy is proposed. This method has two stages, which integrates the Kalman filter and a high order sliding mode Observer. In the inertial navigation system, leveling misalignment angles reach the steady-state faster than the azimuth misalignment angle does, which means the azimuth alignment takes a considerable time for initial alignment. Therefore, in this paper at the first stage estimations of state variables of the system are obtained using the Kalman filter and whenever all variables (except azimuth alignment) reach steady-state, the second stage begins. In the second stage, the estimation which is obtained by the Kalman filter is used as the input to design an equivalent system with unknown inputs for inertial navigation system. A high-order sliding mode Observer is then used to estimate the states of a system with an unknown input for estimating the azimuth alignment angle. This method not only increases the speed of estimation but also has comparable accuracy.

In this article, the issue of sensor fault detection and identification with sensory information is considered. This is due to the dependence of successful Fult Detection (FD) method on correct sensory measurements that suffer from various soft sensory faults such as bias, drift, scaling factor, and hard faults that can be detected independently. They are not detectable but can be combined with other sensors. To solve this issue, firstly, a state space model for pump subsystem was constructed using the electrical simulation method. Then, the sensory soft faults are modeled and amplified to electro-pump state space model. Both system states and amplified sensory soft faults are then estimated using an Extended Kalman filter (EKF) in which nonlinear model of the induction motor is linearized around the estimated states. Information of current, angular velocity (encoder) and pressure sensors are melted for this goal. The efficiency of the method is firstly evaluated through simulation and then experimental results are provided from our laboratory setup. Measured volume currents, flow, and pressure are compared with simulated signals, and results show that the proposed model is able to successfully describe the laboratory system with good precision. These results show that the model can describe the electro-pump dynamic with good precision.

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.