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Device-free localization (DFL) locates target in a wireless sensors network (WSN) without equipping with wireless devices or tags, which is an emerging technology in the fields of intrusion detection and monitoring. In order to achieve an accurate result of DFL, the conventional works adopt <inline-formula> <math display="inline"> <semantics> <msub> <mi>l</mi> <mn>1</mn> </msub> </semantics> </math> </inline-formula> norm as a regularizer to take the full potential of sparsity for locating targets. Contrasting to the previous works, we exploit the <inline-formula> <math display="inline"> <semantics> <msub> <mi>l</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics> </math> </inline-formula> norm as the regularizer and devise an efficient optimization method with a proximal operator-based scheme, which leads the proposed improved-sparse-coding algorithm with proximal operator (ISCPO). Compared with the state-of-the-art methods that adopt <inline-formula> <math display="inline"> <semantics> <msub> <mi>l</mi> <mn>1</mn> </msub> </semantics> </math> </inline-formula> norm as the regularizer, the proposed algorithm can improve the joint sparsity of sparse solution. Experimental results on our real testbeds of indoor DFL show that, in scenarios of living room and corridor, the proposed approach can achieve high localization accuracies of about 100% and 90%, respectively. In addition, the proposed ISCPO algorithm outperforms the compared state-of-the-art methods and has a more robust performance in challenged environments for target localization.