▁given ▁observed ▁process . ▁A ▁class ▁of ▁spatial ▁spectral ▁estimation ▁techniques ▁is ▁based ▁on ▁the ▁eigen - value ▁decomposition ▁of ▁the ▁spatial ▁cov ar iance ▁matrix . ▁The ▁r ationale ▁behind ▁this ▁approach ▁is ▁that ▁one ▁wants ▁to ▁emphas ize ▁the ▁choices ▁for ▁the ▁ste ering ▁vector ▁a ( θ ) ▁which ▁correspond ▁to ▁signal ▁directions . ▁The ▁method ▁explo its ▁the ▁property ▁that ▁the ▁directions ▁of ▁arrival ▁determine ▁the ▁eigen ▁structure ▁of ▁the ▁matrix . ▁The ▁trem end ous ▁interest ▁in ▁the ▁sub space ▁based ▁methods ▁is ▁mainly ▁due ▁to ▁the ▁introduction ▁of ▁the ▁M US IC ▁( Multi ple ▁Sign al ▁Classification ) ▁algorithm . ▁M US IC ▁was ▁originally ▁presented ▁as ▁a ▁DO A ▁estim ator , ▁then ▁it ▁has ▁been ▁successfully ▁brought ▁back ▁to ▁the ▁spectral ▁analysis / system ▁identification ▁problem ▁with ▁its ▁later ▁development . ▁▁ ▁Appro ach ▁over view ▁▁ ▁M US IC ▁spectrum ▁approaches ▁use ▁a ▁single ▁real ization ▁of ▁the ▁st ochastic ▁process ▁that ▁is ▁represent ▁by ▁the ▁snap sh ots ▁x ▁( t ), ▁t = 1 , ▁ 2 ▁... M . ▁M US IC ▁estimates ▁are ▁consistent ▁and ▁they ▁conver ge ▁to ▁true ▁source ▁bear ings ▁as ▁the ▁number ▁of ▁snap sh ots ▁grows ▁to ▁infinity . ▁A ▁basic ▁draw back ▁of ▁M US IC ▁approach ▁is ▁its ▁sens itivity ▁to ▁model ▁errors . ▁A ▁cost ly ▁procedure ▁of ▁cal ibration ▁is ▁required ▁in ▁M US IC ▁and ▁it ▁is ▁very ▁sensitive ▁to ▁errors ▁in ▁the ▁cal ibration ▁procedure . ▁The ▁cost