Sharif Digital Repository

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, a new method is proposed for solving bearing-only tracking when the target moves with constant velocity. The proposed method is compared to Pseudo Linear Estimator. Simulation results show that the proposed method outperforms PLE and estimation error is in vicinity of the CRLB  

Purpose - The purpose of this paper is to propose a supervisory command-to-line-of-sight guidance law with lead angle which keeps the missile flight within the tracking beam. Design/methodology/approach - A nonlinear supervisory controller is designed and coupled with the main sliding mode controller in the form of an additional control signal. The supervisory control signal is activated when the beam angle constraint goes to be violated. Initially a supervisory controller is designed using nonlinear control theory. Subsequently the main tracking controller is designed using sliding mode approach which forces the missile to fly along the desired line-of-sight. The stability of the... 

The main focus of this paper is on the estimation of target motion parameters using bearing measurements. Here, based on a linearization of measurements, a recursive least squares (RLS) solution is developed. The performance of the proposed method is compared to PLE which uses the same approach in measurement linearization. Simulation results show the excellence of the proposed method over the PLE, and RLS results are in vicinity of the CRLB  

Non Line of Sight (NLOS) propagation is a challenging issue which the performance of network based wireless localization is limited by errors primarily caused by NLOS corruption. NLOS is inherently a dominant source of errors in metropolitan area wireless networks, like cellular one, so in this paper we discuss our proposed algorithm within cellular network terminology. This paper contributes two novel algorithms. The first one is to extract the NLOS state of base station (BS) and the second one is a correction algorithm to enhance the measurements accuracy. Our proposed algorithm discuses a novel localization technique to estimate true mobile terminal (MT) location from a set of possibly... 

Various video applications such as mosaicing and object insertion require blending of image regions. The blending quality is often measured subjectively by considering the similarity of the blended image to each of the input images and the visibility of the seams among stitched regions. This paper presents a novel method to blend the moving objects in the related background mosaic based on an adaptive alpha blending method. In order to compute the best possible value for the alpha blending coefficient in dynamic mosaic updating, here we propose a fuzzy method based on relative speed and scale parameters of moving objects. Conducted experiments show that the proposed method improves the... 

We propose a new algorithm for object tracking in crowded video scenes by exploiting the properties of undecimated wavelet packet transform (UWPT) and interframe texture analysis. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region are used to construct a feature vector (FV) for every pixel in that region. Optimal search for the best match is then performed by using the generated FVs inside an adaptive search window. Adaptation of the search window is achieved by interframe texture analysis to find the direction and speed of the object motion. This temporal texture analysis... 

In this paper, we propose a new algorithm for occlusion handling for object tracking in the crowded video scenes. The algorithm exploits the properties of undecimated wavelet packet transform (UWPT) coefficients and texture analysis to track arbitrary objects. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region construct a Feature Vector (FV) for every pixel in that region. Optimal search for the best match is then performed by using the generated FVs inside an adaptive search window. Adaptation of the search window is achieved by interframe texture analysis to find the... 

In this paper, an approach is proposed for controlling the uncertain Lorenz system. Based on an identification technique, a controller is designed that guarantees the regulation of all states in the presence of system uncertainty. Since in some applications the challenging problem of output tracking is desired, we have proposed several effective set-point tracking control techniques. The control schemes that are based on the feedback linearization method, can stabilize the internal dynamics of the system. Simulation results have illustrated the effectiveness of the proposed schemes. © 2005 Elsevier Ltd. All rights reserved  

A new methodology has been proposed to enhance inverse dynamics applications in the process of trajectory planning and optimization in terrain following flights (TFFs). The new approach uses a least square scheme to solve a general two-dimensional (2-D) TFF in a vertical plane. In the mathematical process, Chebyshev polynomials are used to model the geographical data of the terrain in a given route in a manner suitable for the aircraft at hand. The aircraft then follows the modeled terrain with sufficient clearance. In this approach the terrain following (TF) problem is effectively converted to an optimal tracking problem. Results show that this method provides a flexible approach to solve... 

The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. In this paper, an adaptive algorithm for tracking maneuvering targets based on neural networks is proposed. This algorithm is implemented with two filters based on the current statistical model and a multilayer feedforward neural network. The two filters track the same maneuvering target in parallel and the neural network automatically considers all the state information of the two filters and adaptively adjusts the process variance of one of them to achieve better performance in different target maneuver tracking. Simulations results show that the proposed adaptive... 

Flooding algorithm is one of the most significant algorithms used in sensor networks. Although simple, this algorithm causes a large amount of energy and bandwidth to be wasted. The most important application of flooding is RREQ flooding in initial step of most routing algorithms. Although simple, this algorithm causes a large amount of energy and bandwidth to be wasted. Most previous efficient flooding algorithms use location information which is impossible for simple node in sensor network. Some others are not suitable for RREQ flooding due to eliminating redundant retransmissions. We present a modified flooding that simultaneously decreases energy consumption as well as network delay.... 

In this paper, a radial basis function neural network-based (RBFNN) adaptive inverse controller for real-time position tracking control of a two phase hybrid stepper motor in microstep mode is presented and compared with a conventional fixed gain stand-alone PID controller. Also it is compared to the same neuro-controller using feed forward backpropagation neural networks (FFBP) presented by the same author showing superior performance of RBFNN to FFBP neural networks. A radial basis neural network is used as a controller in the adaptive inverse control method combining with a closed loop PID controller to ensure system's initial robustness and closed loop stability and improves the... 

Most of the publications regarding the maxim covering location problem (MCLP) address the case where the decision is to be made for one period. In this paper, we deal with a rather untouched version of MCLP which is called dynamic MCLP (DMCLP). In order to solve this problem, a simulated annealing (SA) has been presented. The proposed solution algorithm is capable of solving problems with up to 2500 demand nodes and 200 potential facilities with a fair amount of exactness. Our experiments showed that the proposed approach finds solutions with errors less than one percent  

In this paper, we present a new method to solve tracking problem. The proposed method combines sparse representation and motion estimation to track an object. Recently. sparse representation has gained much attention in signal processing and computer vision. Sparse representation can be used as a classifier but has high time complexity. Here, we utilize motion information in order to reduce this computation time by not calculating sparse codes for all the frames. Experimental results demonstrates that the achieved result are accurate enough and have much less computation time than using just a sparse classifier  

In this work we present a sparse dictionary learning method, specifically tuned to solve the tracking problem. Recently, sparse representation has drawn much attention because of its genuineness and strong mathematical background. In this paper we present an online method for dictionary learning which is desirable for problems such as tracking. Online learning methods are preferable because the whole data are not available at the current time. The presented method tries to use the advantages of the generative and discriminative models to achieve better performance. The experimental results show our method can overcome many tracking challenges  

Since tracking algorithms should be robust with respect to appearance changes, online algorithms has been investigated recently instead of offline ones which has shown an acceptable performance in controlled environments. The most challenging issue in online algorithms is updating of the model causing tracking failure because of introducing small errors in each update and disturbing the appearance model (drift). in this paper, we propose an online generative tracking algorithm in order to overcome the challenges such as occlusion, object shape changes, and illumination variations. In each frame, color distribution of target candidates is obtained and the candidate having the lowest distance... 

This paper presents a new approach to sensor placement strategy in emitter localization problem which is based on Time Difference of Arrival (TDOA) measurements. The advantage of this method is its flexibility and capability of positioning sensors in constrained or non-stationary situations in which the positions of the sensors are restricted to certain portions of the space and/or needed to be changed repeatedly. The validity of the proposed algorithm is assessed by three different simulation scenarios and the results verify its proper operation  

In this paper a complete design of a high speed optical human motion tracking system has been described for biomechanical human motion analysis and animation craft applications. The main core of the image processing unit that is implemented by the differential algorithm procedure and some intelligent and conservative procedures that facilitate the search algorithm have also been proposed and implemented for the processing of human motions tracking images. In the next step an optimized modified direct linear transformation (MDLT) method has been used to reconstruct 3D locations of markers as an input data for animation unit. The low computational cost and the high precision in detecting and... 

This paper proposes a sublime-optimum soft- computing based controller to follow a desired path with a desired velocity by mobile robots with identified dynamical behavior. This method consists of a fuzzy controller where a trained Neural Network sets its membership functions values in On-line mode. Training of the Network is done by a Genetic Algorithm for various vehicle initial positions and different path convexities in Off-line. After the training of the network, during vehicle motion, it retrieves sub-optimized fuzziness values and sends them to the fuzzy control algorithm according to the vehicle position. Meanwhile, the influential of the path convexity is considered in fuzziness of...