Pregled klasifikacije dronova u radio-frekvencijskom domenu: tehnike, skupovi podataka i izazovi
Sažetak
Uvod/cilj: Korišćenje bespilotnih vazduhoplova – dronova, tokom poslednje decenije mnogostruko se uvećalo, kako za komercijalne (civilne ili amaterske), tako i za funkcionalne (vojne ili industrijske) potrebe. U ovom istraživačkom radu predstavljen je sveobuhvatan pregled javno dostupne i aktuelne literature o klasifikaciji (detekcija i identifikacija) dronova u radio-frekvencijskom domenu. Poseban aspekt predstavljaju algoritmi za duboko učenje i rezultati koji su dobijeni sa javno dostupnim skupom (bazom) podataka VTI_DroneSET.
Metode: Zahvaljujući značajnim unapređenjima dronovi su postali korisna sredstva za različite namene. Dodatna pogodnost jeste što su jeftiniji i pristupačniji za korišćenje, što predstavlja opasnost od njihove zloupotrebe. Stoga je pojačano angažovanje istraživača na razvoju antidron rešenja. Zbog obima javno dostupnih istraživanja, ovaj rad je obuhvatio samo klasifikaciju dronova putem pasivnih radio-frekvencijskih senzora sa opisom korišćenih tehnika (skup algoritama, metoda i procedura) i skupova podataka koji se koriste za testiranje performansi. Radi razumevanja problema klasifikacije dronova izvršena je kvantitativna i kvalitativna analiza sa metodama tehničke analize i obrade radio-signala. Kvantitativni pokazatelji sa grafičkim ilustracijama korišćeni su za sistematizaciju prikupljenih radova, dok su za utvrđivanje mogućnosti klasifikacije dronova u radio-frekvencijskom domenu korišćeni algoritmi dubokog učenja. Štaviše, predstavljeni su izazovi i ograničenja klasifikacije dronova na osnovu radio-signala.
Rezultati: Pokazano je da su algoritmi dubokog učenja trenutno najbolje rešenje za rešavanje pitanja klasifikacije dronova u radio-frekvencijskom domenu. Međutim, većina savremenih istraživanja je eksperimentalna i ima ograničenu praktičnu implementaciju. Poseban problem predstavlja nedostatak opšte specifikacije za klasifikaciju dronova u radio-frekvencijskom domenu na osnovu zahteva iz svakodnevnog iskustva.
Zaključak: Doprinos ovog istraživanja je u sistematizaciji svih dostupnih radova koji se bave klasifikacijom dronova u radio-frekvencijskom domenu, kao i u prikazu nekih mogućnosti algoritama dubokog učenja. Može se zaključiti da se predloženi algoritmi mogu iskoristiti za navedenu primenu, te da je u narednom periodu moguće testirati praktične implementacije, kao i vršiti testiranje u realnim scenarijima upotrebe antidron sistema.
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