▁obtained ▁by ▁model ing ▁the ▁signal ▁wave forms ▁as ▁a ▁Gaussian ▁random ▁process ▁under ▁the ▁assumption ▁that ▁the ▁process ▁x ( t ) ▁is ▁a ▁station ary , ▁zero - mean , ▁Gaussian ▁process ▁that ▁is ▁completely ▁described ▁by ▁its ▁second - order ▁cov ar iance ▁matrix . ▁This ▁model ▁is ▁a ▁reasonable ▁one ▁if ▁the ▁measurements ▁are ▁obtained ▁by ▁filtering ▁wide - band ▁signals ▁using ▁a ▁narrow ▁band - pass ▁filter . ▁ ▁Appro ach ▁over view ▁ ▁Det erm in istic ▁ML ▁approach ▁ ▁While ▁the ▁background ▁and ▁receiver ▁noise ▁in ▁the ▁assumed ▁data ▁model ▁can ▁be ▁thought ▁of ▁as ▁em an ating ▁from ▁a ▁large ▁number ▁of ▁independent ▁noise ▁sources , ▁the ▁same ▁is ▁usually ▁not ▁the ▁case ▁for ▁the ▁em itter ▁signals . ▁It ▁therefore ▁appears ▁natural ▁to ▁model ▁the ▁noise ▁as ▁a ▁station ary ▁Gaussian ▁white ▁random ▁process ▁whereas ▁the ▁signal ▁wave forms ▁are ▁determin istic ▁( ar bitr ary ) ▁and ▁unknown . ▁According ▁to ▁the ▁Det erm in istic ▁ML ▁the ▁signals ▁are ▁considered ▁as ▁unknown , ▁determin istic ▁quantities ▁that ▁need ▁to ▁be ▁estimated ▁in ▁conj unction ▁with ▁the ▁direction ▁of ▁arrival . ▁This ▁is ▁a ▁natural ▁model ▁for ▁digital ▁communication ▁applications ▁where ▁the ▁signals ▁are ▁far ▁from ▁being ▁normal ▁random ▁variables , ▁and ▁where ▁estimation ▁of ▁the ▁signal ▁is ▁of ▁equal ▁interest . ▁ ▁Cor relation ▁spect rom eter ▁ ▁The ▁problem ▁of ▁computing ▁pair wise ▁correlation ▁as ▁a ▁function ▁of ▁frequency ▁can ▁be ▁solved ▁by ▁two ▁mathemat ically ▁equivalent ▁but ▁distinct ▁ways .