In this study, a novel methodology is proposed for more accurate target tracking. The proposed method is basically built on the merging of interacting multiple model (Imm) structures using Hidden markov model (Hmm). T...
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ISBN:
(数字)9781728172064
ISBN:
(纸本)9781728172071
In this study, a novel methodology is proposed for more accurate target tracking. The proposed method is basically built on the merging of interacting multiple model (Imm) structures using Hidden markov model (Hmm). Thus, more models are used than the ordinary Imm algorithm, but more accurate state vectors are estimated by selecting the most likely ones. The proposed algorithm is compared with the variable structure Imm (VSImm) algorithm, which is the most similar methodology in the literature, in mATLAB environment and the results are presented.
In this study, the problem of the association of the measurement given by another 2B radar to a 2B track created by measuring the yaw angle and the range is discussed. In order to be able to associate, an equality has...
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In this study, the problem of the association of the measurement given by another 2B radar to a 2B track created by measuring the yaw angle and the range is discussed. In order to be able to associate, an equality has to be established in the deterministic environment and an association decision has been done in the case that the measurement provides this equality. Then, an algorithm was developed to test if equality is provided in the case of random vectors containing noises, whether it is measurement or track, and the performance is tested on the samples. The algorithm developed is a new algorithm.
In this study, a novel methodology based on Hidden markov model (Hmm) is proposed to classify air vehicles according to the their types. The proposed methodology uses target radar cross section (RCS) information. The ...
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In this study, a novel methodology based on Hidden markov model (Hmm) is proposed to classify air vehicles according to the their types. The proposed methodology uses target radar cross section (RCS) information. The method is differentiates from the studies in the literature by the ways of using Hmm and RCS information. RCS information is a sequence that accumulates during the flight. Likelihood values are obtained for the possible target types by considering the compatibility of the RCS sequence with the Hidden markov models generated for each class. Probabilities belonging to the target types are calculated using these likelihood values. The proposed methodology is tested with realistically prepared synthetic data and the obtained results are presented in this study.
In this study, a new methodology for combining probability masses from different sources is proposed for Dempster-Shafer theory. Unlike the existing works in the literature, this methodology treats the combination pro...
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In this study, a new methodology for combining probability masses from different sources is proposed for Dempster-Shafer theory. Unlike the existing works in the literature, this methodology treats the combination problem as an optimization problem and proposes an objective function that uses conflict and entropy measures to solve this problem. The proposed objective function aims to minimize the conflict between the combined masses and the masses comes from the sources, and at the same time maximize the entropy of the combined probability masses. Thus, the difference between the combined probability masses and the masses coming from the sources is minimized while being cautious and avoiding a final certain decision. This new methodology is tested in the mATLAB environment and compared with the existing methods.
The quality and precision of tracking manuevering targets under large clutter is highly dependent on both the data association and state estimation algorithms. In this study, measurement-to track association problem w...
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The quality and precision of tracking manuevering targets under large clutter is highly dependent on both the data association and state estimation algorithms. In this study, measurement-to track association problem was discussed and the optimal association problem was shown to be a markov Decision Process. The problemmodel considers the batch measurements in a time interval. The optimization problem has an heavy computational load, therefore the rollout algorithm is used to solve this problem. The approximate solution to the association problem is a new approach and it does not exist in the literature. The algorithm was applied to a tracking scenario and its efficiency is demonstrated in the simulations part.
This study proposes a multi-dimensional Hough transform algorithm that is improved from by detecting weak targets in the high clutter. In the proposed algorithm execution time is reduced by eliminating the measurement...
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This study proposes a multi-dimensional Hough transform algorithm that is improved from by detecting weak targets in the high clutter. In the proposed algorithm execution time is reduced by eliminating the measurements considering speed and SNR values before the Hough transform. The skor-based track confirmation algorithm proposed is improved and tracks that belong to same target are eliminated. The proposed algorithm is tested with real data and results are presented.
The quality and precision of tracking maneuvering targets under heavy clutter is highly dependent on both the data association and the state estimation algorithms. In this study, measurement-to-track association probl...
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The quality and precision of tracking maneuvering targets under heavy clutter is highly dependent on both the data association and the state estimation algorithms. In this study, measurement-to-track association problem for a single target when PD = 1 is discussed. The problem considers the batch set of measurements in a time interval. An approximate stochastic optimization algorithm for data association is presented. To reduce the computational load, the rollout algorithm is utilized. The algorithm is applied to a tracking scenario and GNN, JPDA and mHT algorithms are compared with their rollout versions. In the comparison several different track quality measures are used to demonstrate the efficiency of the algorithm.
In this work, the concept of sensor management is introduced and stochastic dynamic programming based resource allocation approach is proposed to track multi target. The core of this approach is to use Lagrange relaxa...
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In this work, the concept of sensor management is introduced and stochastic dynamic programming based resource allocation approach is proposed to track multi target. The core of this approach is to use Lagrange relaxation for decreasing the state space dimension. By this approximation, the overall problem is separated into components instead of using joint markov model to optimize large scale stochastic control problem. The aim of this study is to adaptively allocate radar resources in an optimal way in order to maintain track qualities for multi-target case. Time scale is divided into two levels that are called as micro management and macro management. During this work, we deal with macro management part that aims to construct a policy which is optimal for a given objective function under the resource constraints. In this work, some rule based techniques are added into simulation. The performance of algorithm is analyzed on the average number of update decision and average number of target drops in time horizon.
In this paper, classification of air vehicles according to their types is studied. Dempster-Shafer theory is utilized for this purpose. The target tracker data and radar cross section data are used for obtaining the i...
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In this paper, classification of air vehicles according to their types is studied. Dempster-Shafer theory is utilized for this purpose. The target tracker data and radar cross section data are used for obtaining the instantaneous probability masses by comparing them with the prior information. Final decision is made by combining the instantaneous probability masses in time. This new methodology is tested on real data and results are presented.
In this study, dynamic models for thrusting and ballistic flight modes of multi mode projectile are obtained and marginalization method is applied by separation of the linear and nonlinear parts of state space model. ...
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In this study, dynamic models for thrusting and ballistic flight modes of multi mode projectile are obtained and marginalization method is applied by separation of the linear and nonlinear parts of state space model. In marginalized Particle Filter (mPF), dimension of the nonlinear system is reduced so that the model can be utilized to obtain better estimates of the state using the same number of particles as that of standard particle filter. The Extended Kalman Filter (EKF), the Particle Filter (PF) and the marginalized Particle Filter (mPF) are compared by their RmS errors in position and velocity estimations obtained by monte Carlo simulations. In general, EKF has the best performance on position estimation and mPF has the best performance on velocity estimation.
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