Pregled bibliografske jedinice broj: 843368
Estimation of Heavy-Tailed Clutter Density using Adaptive RBF Network
Estimation of Heavy-Tailed Clutter Density using Adaptive RBF Network // Proceedings of the 22nd International Conference on Applied Electromagnetics and Communications (ICECom 2016) / Bonefačić, Davor ; Šipuš, Zvonimir (ur.).
Zagreb: Hrvatsko društvo za komunikacije, računarstvo, elektroniku, mjerenja I automatiku (KoREMA), 2016. str. s_12_3 (1)-s_12_3 (6) (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Estimation of Heavy-Tailed Clutter Density using Adaptive RBF Network
Autori
Vondra, Bojan ; Bonefačić, Davor
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 22nd International Conference on Applied Electromagnetics and Communications (ICECom 2016)
/ Bonefačić, Davor ; Šipuš, Zvonimir - Zagreb : Hrvatsko društvo za komunikacije, računarstvo, elektroniku, mjerenja I automatiku (KoREMA), 2016, S_12_3 (1)-s_12_3 (6)
ISBN
978-953-6037-72-8
Skup
22nd International Conference on Applied Electromagnetics and Communications (ICECom 2016)
Mjesto i datum
Dubrovnik, Hrvatska, 19.09.2016. - 21.09.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Radial Basis Functions; K-distribution; Viterbi algorithm;
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
