▁of ▁cal ibration ▁increases ▁as ▁the ▁number ▁of ▁parameters ▁that ▁define ▁the ▁array ▁manifold ▁increases . ▁ ▁Param et ric – based ▁solutions ▁ ▁While ▁the ▁spectral - based ▁methods ▁presented ▁in ▁the ▁previous ▁section ▁are ▁computation ally ▁attract ive , ▁they ▁do ▁not ▁always ▁yield ▁sufficient ▁accuracy . ▁In ▁particular , ▁for ▁the ▁cases ▁when ▁we ▁have ▁highly ▁correl ated ▁signals , ▁the ▁performance ▁of ▁spectral - based ▁methods ▁may ▁be ▁ins u fficient . ▁An ▁alternative ▁is ▁to ▁more ▁fully ▁explo it ▁the ▁underlying ▁data ▁model , ▁leading ▁to ▁so - called ▁paramet ric ▁array ▁processing ▁methods . ▁The ▁cost ▁of ▁using ▁such ▁methods ▁to ▁increase ▁the ▁efficiency ▁is ▁that ▁the ▁algorithms ▁typically ▁require ▁a ▁mult id im ensional ▁search ▁to ▁find ▁the ▁estimates . ▁The ▁most ▁common ▁used ▁model ▁based ▁approach ▁in ▁signal ▁processing ▁is ▁the ▁maximum ▁lik elihood ▁( ML ) ▁technique . ▁This ▁method ▁requires ▁a ▁statistical ▁framework ▁for ▁the ▁data ▁generation ▁process . ▁When ▁applying ▁the ▁ML ▁technique ▁to ▁the ▁array ▁processing ▁problem , ▁two ▁main ▁methods ▁have ▁been ▁considered ▁depending ▁on ▁the ▁signal ▁data ▁model ▁assumption . ▁According ▁to ▁the ▁Sto ch astic ▁ML , ▁the ▁signals ▁are ▁mode led ▁as ▁Gaussian ▁random ▁processes . ▁On ▁the ▁other ▁hand , ▁in ▁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 . ▁ ▁Sto ch astic ▁ML ▁approach ▁ ▁The ▁st ochastic ▁maximum ▁lik elihood ▁method ▁is