▁assumption ▁of ▁wave ▁propag ation . ▁W ave ▁propag ation ▁means ▁there ▁is ▁a ▁system ic ▁relationship ▁between ▁the ▁signal ▁received ▁on ▁spatial ly ▁separated ▁sens ors . ▁By ▁creating ▁a ▁physical ▁model ▁of ▁the ▁wave ▁propag ation , ▁or ▁in ▁machine ▁learning ▁applications ▁a ▁training ▁data ▁set , ▁the ▁relationships ▁between ▁the ▁signals ▁received ▁on ▁spatial ly ▁separated ▁sens ors ▁can ▁be ▁lever aged ▁for ▁many ▁applications . ▁ ▁Some ▁common ▁problem ▁that ▁are ▁solved ▁with ▁array ▁processing ▁techniques ▁are : ▁ ▁determine ▁number ▁and ▁locations ▁of ▁energy - rad i ating ▁sources ▁▁ ▁enh ance ▁the ▁signal ▁to ▁noise ▁ratio ▁S NR ▁" signal - to - inter ference - plus - no ise ▁ratio ▁( S IN R )" ▁ ▁track ▁moving ▁sources ▁ ▁Array ▁processing ▁metrics ▁are ▁often ▁ass essed ▁no isy ▁environments . ▁The ▁model ▁for ▁noise ▁may ▁be ▁either ▁one ▁of ▁spatial ly ▁in co her ent ▁noise , ▁or ▁one ▁with ▁inter fer ing ▁signals ▁following ▁the ▁same ▁propag ation ▁physics . ▁Est imation ▁theory ▁is ▁an ▁important ▁and ▁basic ▁part ▁of ▁signal ▁processing ▁field , ▁which ▁used ▁to ▁deal ▁with ▁estimation ▁problem ▁in ▁which ▁the ▁values ▁of ▁several ▁parameters ▁of ▁the ▁system ▁should ▁be ▁estimated ▁based ▁on ▁measured / emp ir ical ▁data ▁that ▁has ▁a ▁random ▁component . ▁As ▁the ▁number ▁of ▁applications ▁increases , ▁estim ating ▁temporal ▁and ▁spatial ▁parameters ▁become ▁more ▁important . ▁Array ▁processing ▁emer ged ▁in ▁the ▁last ▁few ▁dec ades ▁as ▁an ▁active ▁area ▁and ▁was ▁centered ▁on ▁the