Bu çalışmada genişletilmiş hedef izleme (GHİ) problemi ele alınmıştır. GHİ problemi klasik hedef izleme probleminden farklı olarak, bir hedefin tek bir anda birden fazla ölçüme sebep olması durumunu inceler. Bu varsayım altında toplanan ölçümlerden, hedefin hem kinematik bilgileri hem de şekli kestirilir. Literatürde bu problemi çözmeye yönelik yaklaşık çözümlü algoritmalar vardır. Ancak bu çalışmaların pek çoğu teorik alt yapısı zayıf olan buluşsal çözüm önerileri içerir. Bu çalışmada yüzeyi birden çok elips ile gösterilebilen bir hedefi takip eden ve hedefin şeklini öğrenebilen beklenti maksimizasyonu (BM) temelli yeni bir yöntem geliştirilmiştir. GHİ problemi stokastik durum uzay modellerinde parametre kestirimi problemi haline getirilmiş ve parçacık filtresi kullanarak kestirim yapılmıştır. Simülasyonlarda çoklu elipsten oluşan ve bilinmeyen şekle sahip bir genişletilmiş hedef isabetle takip edilmiş ve hedefin şekli başarıyla kestirilmiştir.
This study has addressed the problem of extended target monitoring (GI). Unlike the classic target tracking problem, the GHI problem examines the case that a target causes multiple measurements at a time. From the measurements collected under this assumption, both the kinematical information and the shape of the target are cut. In literature there are approximately solved algorithms to solve this problem. However, many of these studies include theoretical sub-structure weak inventive solutions suggests. In this study, a new method is developed based on expectancy maximization (BM) that follows a target that can be displayed with multiple elips and that can learn the form of the target. GHI problem stocastic state space models have become a problem of parameter cutting and cutting using particle filters. In simulations, an extended target with an unknown shape, consisting of multiple ellipstones, was traced with a hit and the shape of the target was successfully cut.
In this study, extended target tracking (ETT) problem is considered. The ETT problem, unlike the classical target tracking problem, makes the assumption that a target generates more than one measurement at a time. Both the kinematic state and the shape of targets are estimated from the measurements collected under this assumption. In the literature, there are approximate solution algorithms to solve this problem. However, many of these studies include heuristic approximations. In this paper, we develop a new expectation maximization (EM) based method, which can track and learn the shape of an extended target whose extent can be represented by multiple ellipses. For this purpose, we cast the ETT problem as a parameter estimation problem in stochastic state space models, and perform estimation using particle filters. In the simulations, an extended target with unknown shape consisting of multiple ellipses is tracked accurately, and the shape of the target is estimated successfully.
Alan : Mühendislik
Dergi Türü : Ulusal
Benzer Makaleler | Yazar | # |
---|
Makale | Yazar | # |
---|