Most　of　the　existing　Beamforming　methods　are　based　on　the　assumptions　that　the　sources　are　all　point　sources　and　the　angular　separation　between　the　direction　of　arrival　(DOA)　of　the　source　and　the　interference　is　large　enough　to　assure　good　performance.　In　this　paper,　we　consider　a　tough　scenario　where　the　target　source　and　the　interference　are　simultaneously　spatially　distributed　and　overlapped.　To　improve　the　performance　of　Beamforming　in　this　scenario,　we　propose　two　approaches:　the　first　approach　exploits　the　non-Gaussianity　as　well　as　the　spectrogram　sparsity　of　the　output　of　the　microphone　array;　the　second　approach　exploits　the　generalized　sparsity　with　overlapped　groups　of　the　Beampattern.　The　proposed　criteria　are　solved　by　methods　based　on　linearized　preconditioned　alternating　direction　method　of　multipliers　(LPADMM)　with　high　accuracy　and　high　computational　efficiency.　Numerical　simulations　and　real　data　experiments　show　the　advantages　of　the　proposed　approaches　compared　to　previously　proposed　Beamforming　methods　for　signal　enhancement.