Search for: particle-filter
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    An algorithm to estimate parameters and states of a nonlinear maneuvering target

    , Article Cogent Engineering ; Volume 7, Issue 1 , 2020 Hosseini, S. N ; Haeri, M ; Khaloozadeh, H ; Sharif University of Technology
    Cogent OA  2020
    This paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented so far that could be applied to general cases. The available methods are ineffective when the range of variation of the unknown input parameter is large. Also, the issue of determining the system class could improve the performance of the tracking algorithms in many applications. Using the Bayesian theory, the posterior distribution functions of state and parameter could be obtained concurrently. In the proposed algorithm, Liu and West and... 

    Adaptive passive sensor selection for maneuvering target localization and tracking using a multisensor surveillance system

    , Article Cogent Engineering ; Volume 7, Issue 1 , 2020 Hosseini, S. N ; Haeri, M ; Khaloozadeh, H ; Sharif University of Technology
    Cogent OA  2020
    This paper investigates maneuvering-target tracking problem based on a multisensor system and interacting multiple model (IMM). The estimation is performed by a novel particle filter (PF) with a capability to deal with the state-dependent noises and interference of the sensors’ coverage environment. An adaptive sensor selection algorithm, where some sensors are selected in each stage based on the signal-to-interference pulse noise ratio (SINR) and participate in the state estimation, is proposed. To deal with the effect of interference, we focus on designing and implementing the sensor selection algorithm, where a multisensor system with nonuniform arrays is derived by solving a convex... 

    Multitarget Tracking with Improved Particle Filter Eliminating Data Association Step

    , Ph.D. Dissertation Sharif University of Technology Raees Danaee, Meysam (Author) ; Behnia, Fereidoon (Supervisor)
    In general, multi-target tracking consists of estimation of the posterior density function of present targets at each scan in the observation area. These targets may have unknown and time varying number of targets. It is a tough job due to misdetections, false alarms, data association ambiguity, and nonlinear equations-non Gaussian noises. These all make it difficult to apply Kalman filter and its extensions such as extended Kalman filter and unscented Kalman filter. Monte Carlo methods, particularly particle filters, have recently aroused the interest of designers and enjoyed a lot of success to deal with multi-target tracking difficulties. In addition, they can handle nonthresholded data... 

    Online adaptive motion model-based target tracking using local search algorithm

    , Article Engineering Applications of Artificial Intelligence ; Volume 37 , January , 2015 , Pages 307-318 ; 09521976 (ISSN) Karami, A. H ; Hasanzadeh, M ; Kasaei, S ; Sharif University of Technology
    Elsevier Ltd  2015
    An adaptive tracker to address the problem of tracking objects which undergo abrupt and significant motion changes is introduced. Abrupt motion of objects is an issue which makes tracking a challenging task. To address this problem, a new adaptive motion model is proposed. The model is integrated into the sequential importance resampling particle filter (SIR PF), which is the most popular probabilistic tracking framework. In this model, in each time step, if necessary, the particles' configurations are updated by using feedback information from the observation likelihood. In order to overcome the local-trap problem, local search algorithm with best improvement strategy is used to update... 

    Integrated Orbit and Attitude Parameter Determination of a Satellite Using Hybrid Nonlinear Filters

    , Ph.D. Dissertation Sharif University of Technology Kiani, Maryam (Author) ; Pourtakdoust, Hossein (Supervisor)
    Rapid growth of space traffic and small satellite systems for current and future space missions have generated new enhanced performance requirements for navigation subsystem. As such, the navigation subsystem is considered as a vital part of all active satellite systems that effectively influences their successful missions. In this regard, the present work is dedicated on the subject of autonomous satellite navigation utilizing nonlinear filters, for which some laboratory experimentations have also been implemented. Generally, advanced orbit and attitude estimation algorithms can effectively compensate for the effect of low cost hardware and sensor packs utilized in microsatellites. In... 

    Patchwise joint sparse tracking with occlusion detection

    , Article IEEE Transactions on Image Processing ; Vol. 23, Issue. 10 , 2014 , Pages. 4496-4510 ; ISSN: 10577149 Zarezade, A ; Rabiee, H. R ; Soltani-Farani, A ; Khajenezhad, A ; Sharif University of Technology
    This paper presents a robust tracking approach to handle challenges such as occlusion and appearance change. Here, the target is partitioned into a number of patches. Then, the appearance of each patch is modeled using a dictionary composed of corresponding target patches in previous frames. In each frame, the target is found among a set of candidates generated by a particle filter, via a likelihood measure that is shown to be proportional to the sum of patch-reconstruction errors of each candidate. Since the target's appearance often changes slowly in a video sequence, it is assumed that the target in the current frame and the best candidates of a small number of previous frames, belong to... 

    Comparison of nonlinear filtering techniques for inertial sensors error identification in INS/GPS integration

    , Article Scientia Iranica ; Volume 25, Issue 3B , 2018 , Pages 1281-1295 ; 10263098 (ISSN) Kaviani, S ; Salarieh, H ; Alasty, A ; Abediny, M ; Sharif University of Technology
    Sharif University of Technology  2018
    Nonlinear filtering techniques are used to fuse the Global Positioning System (GPS) with Inertial Navigation System (INS) to provide a robust and reliable navigation system with a performance superior to that of either INS or GPS alone. Prominent nonlinear estimators in this field are Kalman Filters (KF) and Particle Filters (PF). The main objective of this research is the comparative study of the well-established filtering methods of EKF, UKF, and PF based on EKF and UKF in an INS-GPS integrated navigation system. Different features of INS-GPS integrated navigation methods in the state estimation, bias estimation, and bias/scale factor estimation are investigated using these four filtering... 

    , M.Sc. Thesis Sharif University of Technology Sharifzadeh, Abdorrahman (Author) ; Behnia, Fereidoon (Supervisor)
    In this thesis, Particle Filter Methods are investigated in the context of moving targets tracking and the associated performance analysis. The application scope of this algorithm which is a particular case of Sequential Monte Carlo Method is far broader than tracking of moving targets. This algorithm can be used for mathematical calculations such as estimation of mathematical expectations, integrals, surface area of curves and many other mathematical calculations. In addition, it has applications in other branches of science like genetics. This algorithm is based on random sampling of a probability density function and resampling from the extracted samples. We change this algorithm in... 

    Distributed Tracking in Smart Camera Networks

    , M.Sc. Thesis Sharif University of Technology Rezaei Hosseinabadi, Fatemeh (Author) ; Hossein Khalaj, Babak (Supervisor)
    Human tracking is an essential step in many computer vision-based applications. As single view tracking may not be sufficiently robust and accurate, tracking based on multiple cameras has been widely considered in recent years. This thesis presents a distributed human tracking method in a smart camera network and introduces a particle filter design based on Histogram of Oriented Gradients (HOG) and color histogram. The proposed adaptive motion model also estimates the target speed from the history of its latest displacement and improves the robustness of the tracker by decreasing the probability of missing targets. In addition, a distributed data fusion method is proposed which fuses the... 

    Human Tracking by Probabilistic and Learning Methods

    , M.Sc. Thesis Sharif University of Technology Raziperchikolaei, Ramin (Author) ; Jamzad, Mansour (Supervisor)
    To overcome challenges such as object appearance changes and environment illumination variations in tracking methods, online algorithms are suggested to be used instead of offline ones. Online algorithms update the model by the information acquired in the last processed frame. The main challenge of using online algorithms is the accumulation of small errors after several steps of updating of the model (drift) which disturbs the model and causes tracking failure. Using the object information in the first frame in each update can be considered as a solution. The proposed online semi-supervised boosting algorithms can overcome the drift problem at the expense of decreasing their capabilities in... 

    Particle Filter and its Application in Tracking

    , M.Sc. Thesis Sharif University of Technology Amidzadeh, Mohsen (Author) ; Babaiezadeh, Massoud (Supervisor)
    The aim of tracking is localization and positioning of position-variant object through consecutive times. The essence of this object determines the application of tracking. For example this object can be the satellite, mobile, certain object in sequential movie or etc. The particle filter as an estimation filter is a method that provides us the solution of tracking Problem. Therefore this thesis is devoted to particle filter and its application in tracking. But tracking problem needs some prior information; one of them is access to measurements relating to object position. In situations that the measurement equation which is related to object position has ambiguity we need another mechanism... 

    Object Tracking Via Sparse Representation Model

    , M.Sc. Thesis Sharif University of Technology Zarezade, Ali (Author) ; Rabiee, Hamid Reza (Supervisor)
    Visual tracking is a classic problem, but is continuously an active area of research, in computer vision. Given a bounding box defining the object of interest (target) in the first frame of a video sequence, the goal of a general tracker is to determine the ob-ject’s bounding box in subsequent frames. Utilizing sparse representation, we propose a robust tracking algorithm to handle challenges such as illumination variation, pose change, and occlusion. Object appearance is modeled using a dictionary composed of target patch images contained in previous frames. In each frame, the target is found from a set of candidates via a likelihood measure that is proportional to the sum of the... 

    Stochastic Modeling of Online Advertising System

    , M.Sc. Thesis Sharif University of Technology Divsalar, Mohammad Reza (Author) ; Nobakhti, Amin (Supervisor) ; Babazadeh, Maryam (Supervisor)
    Television, radio, newspaper, magazines, and billboards are among the major channels that traditionally place ads, however, the advancement of the Internet enables users to seek information online. In display and mobile advertising, the most significant technical development in recent years is the growth of Real-Time Bidding (RTB), which facilitates a real-time auction for a display opportunity. Real-time means the auction is per impression and the process usually occurs less than 300 milliseconds before the ad is placed. RTB has fundamentally changed the landscape of the digital media market by scaling the buying process across a large number of available inventories among publishers in an... 

    Extension of particle filters for time-varying target presence through split and raw measurements

    , Article IET Radar, Sonar and Navigation ; Volume 7, Issue 5 , 2013 , Pages 517-526 ; 17518784 (ISSN) Danaee, M. R ; Behnia, F ; Sharif University of Technology
    Target tracking through particle filter (PF) for time-varying presence of a target is compared for thresholded and nonthresholded measurements, where in both cases a track produces more than one measurement. To that end, thresholded split measurements PF along with non-thresholded measurements, sequential importance resampling PF (SIR PF) and auxiliary variable PF (AV PF) are extended to cope with time-varying target presence. Simulations show superiorities in working through non-thresholded measurements. Furthermore, they surprisingly demonstrate that non-thresholded measurements SIR PF leads to less root-mean-square position estimation error than non-thresholded measurements AV PF in case... 

    Visual tracking using D2-clustering and particle filter

    , Article 2012 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2012 ; 2012 , Pages 230-235 ; 9781467356060 (ISBN) Raziperchikolaei, R ; Jamzad, M ; Sharif University of Technology
    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... 

    Development of Integrated Navigation Algorithm Based on the Integration of INS, GPS, Altimeter Using Particle Filters

    , M.Sc. Thesis Sharif University of Technology Kaviani, Samira (Author) ; Salarieh, Hassan (Supervisor) ; Alasti, Aria (Supervisor)
    Over the past years, several approaches have been used for navigation. Along with the advancement of technology, complicated navigation systems such as inertial navigation system and global positioning system have been employed. These navigation systems have their own strengths and weaknesses. To improve overall system performance they are integrated together as one system.
    Presented research aims at developing the integrated navigation system through particle filters. In this study, after a brief overview of the data integration techniques, several techniques which are more common and have better performance than others are examined. This contribution concentrates on filtering methods... 

    Auxiliary unscented particle cardinalized probability hypothesis density

    , Article 2013 21st Iranian Conference on Electrical Engineering, ICEE 2013, Mashhad ; 2013 ; 9781467356343 (ISBN) Danaee, M. R ; Behnia, F ; Sharif University of Technology
    The probability hypothesis density (PHD) filter has been recently introduced by Mahler as a relief for the intractable computation of the optimal Bayesian multi-target filtering. It propagates the posterior intensity of the random finite set (RFS) of targets in time. Despite serving as a powerful decluttering algorithm, PHD filter still has the problem of large variance of the estimated expected number of targets. The cardinalized PHD (CPHD) filter overcomes this problem through jointly propagating the posterior intensity and the posterior cardinality distribution. Unfortunately, the particle filter implementation of the CPHD filter suffers from lack of an efficient method for boosting its... 

    Particle filter-based object tracking using adaptive histogram

    , Article 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011 - Proceedings ; 2011 ; 9781457715358 (ISBN) Fotouhi, M ; Gholami, A. R ; Kasaei, S ; Sharif University of Technology
    Object tracking is a difficult and primary task in many video processing applications. Because of the diversity of various video processing tasks, there exists no optimum method that can perform properly for all applications. Histogram-based particle filtering is one of the most successfu1 object tracking methods. However, for dealing with visual tracking in real world conditions (such as changes in illumination and pose) is still a challenging task. In this paper, we have proposed a color-based adaptive histogram particle filtering method that can update the target model. We have used the Bhattacharyya coefficients to measure the likelihood between two color histograms. Our experimental... 

    Particle filtering-based low-elevation target tracking with multipath interference over the ocean surface

    , Article IEEE Transactions on Aerospace and Electronic Systems ; Volume 56, Issue 4 , 2020 , Pages 3044-3054 Shi, X ; Taheri, A ; Cecen, T ; Celik, N ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    As radar signals propagate above the ocean surface to determine the trajectory of a target, the signals that are reflected directly from the target arrive at the receiver along with indirect signals reflected from the ocean surface. These unwanted signals must be properly filtered; otherwise, their interference may mislead the signal receiver and significantly degrade the tracking performance of the radar. To this end, we propose a low-elevation target tracking mechanism considering the specular and diffuse reflection effects of multipath propagation over the ocean surface simultaneously. The proposed mechanism consists of a state-space model and a particle filtering algorithm and promises... 

    Tracking dynamical transition of epileptic EEG using particle filter

    , Article 8th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2008, Sarajevo, 16 December 2008 through 19 December 2008 ; February , 2008 , Pages 270-274 ; 9781424435555 (ISBN) Mamaghanian, H ; Shamsollahi, M. B ; Hajipour, S ; IEEE Signal Processing Society and IEEE Computer Society ; Sharif University of Technology
    In this work we used the Liley EEG model as a dynamical model of EEG. Two parameters of the model which are candidates for change during an epileptic seizure are defined as new states in state space representation of this dynamical model. Then SIS particle filter is applied for estimating the defined states over time using the recorded epileptic EEG as the observation of the system. A method for fast numerical solution of the nonlinear coupled equation of the model is proposed. This model is used for tracking the dynamical properties of brain during epileptic seizure. Tracking the changes of these new defined states of the model have good information about the state transition of the model...