The　performance　of　the　existing　target　localization　algorithms　is　not　ideal　in　complex　acoustic　environment.　In　order　to　improve　this　problem,　a　novel　target　binaural　sound　localization　algorithm　is　presented.　First,　the　algorithm　uses　binaural　spectral　features　as　input　of　a　time-frequency　units　selector　based　on　deep　learning.　Then,　to　reduce　the　negative　impact　of　the　time-frequency　unit　belonging　to　noise　on　the　localization　accuracy,　the　selector　is　emploied　to　select　the　reliable　time-frequency　units　from　binaural　input　sound　signal.　At　the　same　time,　a　Deep　Neural　Network　(DNN)-based　localization　system　maps　the　binaural　cues　of　each　time-frequency　unit　to　the　azimuth　posterior　probability.　Finally,　the　target　localization　is　completed　according　to　the　azimuth　posterior　probability　belonging　to　the　reliable　time-frequency　units.　Experimental　results　show　that　the　performance　of　the　proposed　algorithm　is　better　than　comparison　algorithms　and　achieves　a　significant　improvement　in　target　localization　accuracy　in　low　Signal-to-Noise　Ratio(SNR)　and　various　reverberation　environments,　especially　when　there　is　noise　similar　to　the　target　sound　source.　©　2019,　Science　Press.　All　right　reserved.