izvorni znanstveni rad
In this paper, a method for estimating clutter density using radial basis function (RBF) network is described. Clutter density is important parameter for data association techniques in single and multitarget scenarios. K-distribution is widely accepted model of heavy-tailed sea lutter, however, estimating its parameters using traditional method of moments MM) or maximum ikelihood (ML) approach require computationally ntense task. Instead of this, a non-parametric pproach is used (density is directly estimated, ased on samples in validation volume of tracked target). During tracking process, returns from target and clutter are clustered using Linde, Buzo and Gray (LBG) algorithm, with fixed number of clusters and minimum distance criterion. Based on representative kernel of each cluster, density is constructed and integrated in Viterbi data association filter that also provides a track quality output. Since densities based under target-present and clutter-present hypothesis are available, corresponding likelihood ratios can be used to further discriminate target from clutter and thus enhance tracking process. Although the method for estimating clutter density is described using single target scenario, it is applicable to multitarget case e.g. using multihypothesis Viterbi filter.
Radial Basis Functions; K-distribution; Viterbi algorithm;
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…
