The　traditional　subspace　class　algorithm　has　low　or　even　no　estimation　accuracy　for　DOA　estimation　under　the　conditions　of　less　number　of　snapshots,　low　SNR　and　source　coherence.　Therefore,　the　application　of　compressed　sensing　theory　in　DOA　estimation　is　studied　in　this　paper.　To　address　the　problems　of　poor　estimation　accuracy　of　sparsity　adaptive　matching　Pursuit(SAMP)　algorithm　in　noisy　environment　and　the　need　to　gradually　approximate　the　true　sparsity　from　zero,　a　sparsity　pre-estimation　adaptive　matching　Pursuit(SPAMP)　algorithm　is　proposed　in　this　paper　.　The　algorithm　in　this　paper　optimizes　the　iterative　termination　conditions　of　the　algorithm　by　using　the　changing　rules　of　iterative　residuals,　and　at　the　same　time　approximates　the　source　sparsity　quickly　and　accurately　by　pre-estimating　the　initial　sparsity..　The　simulation　results　show　that　the　algorithm　in　this　paper　has　the　advantages　of　high　estimation　accuracy,　fast　operation　speed　and　better　noise　immunity,　which　promotes　further　integration　of　compressed　sensing　and　DOA　estimation　in　practical　situations.　©　2022　ACM.