Sharif Digital Repository

Road roughness-induced vibrations are the main source of fatigue damage accumulation in vehicles. Hence, road surface profile modeling and simulation are of high importance when it comes to vehicle fatigue damage assessment. This research focuses on uncovering the statistical distributions that describe the characteristics of road loads that affect fatigue damage accumulation. Particle filtering is deployed to estimate the log-volatility of the road surface profile by assuming a random walk behavior for the hidden log-volatility. The skew-normal distribution with three parameters is fitted to the estimated log-volatility. The inferred parameters are used for synthesizing artificial road... 

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... 

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... 

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... 

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... 

Air pollutants are among the hazardous materials for human health. Therefore, many scientists are interested in removing particles from the carrier gas. In this study, flow of air and airborne particles through the virtual multi-fibrous filters that consist of different fiber cross-sectional shapes and arrangements is simulated where particle deposition and filtration performance are studied. Regular and irregular arrangements of fibers with the circular, elliptical, and equilateral triangular cross sections have been considered. Effects of important parameters such as solid volume fraction, internal structure, and filter thickness on particle collection efficiency and pressure drop are... 

A new heuristic filter, called firefly filter, is proposed for state estimation of nonlinear stochastic systems. The new filter formulates the state estimation problem as a stochastic dynamic optimization and utilizes the firefly optimization algorithm to find and track the best estimation. The fireflies search the state space dynamically and are attracted to one other based on the perceived brightness. The performance of the proposed filter is evaluated for a set of benchmarks and the results are compared with the well-known filters like extended Kalman filter and particle filter, showing improvements in terms of estimation accuracy. © 2016 IEEE  

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... 

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... 

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... 

Recently, Random finite set theory has attracted researchers' interest in the field of multitarget tracking time varying number of targets. Its main drawback is it is not essentially formulated for nonthresholded measurements. This paper examines the problem of multitarget tracking with time varying number of targets dealing with raw and nonthresholded measurements. Recursive equations for updating the joint multi target state posterior density are approximated by a new enhanced particle filter that includes effective strategies to tackle the challenges of effective track initialization and deletion with limited resources, as well as doing the data association step implicitly. Simulations... 

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... 

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... 

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 paper 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... 

A new heuristic filter, called Continuous Ant Colony Filter, is proposed for non-linear systems state estimation. The new filter formulates the states estimation problem as a stochastic dynamic optimization problem and utilizes a colony of ants to find and track the best estimation. The ants search the state space dynamically in a similar scheme to the optimization algorithm, known as Continuous Ant Colony System. The performance of the new filter is evaluated for a nonlinear benchmark and the results are compared with those of Extended Kalman Filter and Particle Filter, showing improvements in terms of estimation accuracy  

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...