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中文摘要：
针对基于l1范数优化的稀疏信号重构算法需要的观测样本数较多,本文以lp范数最小化为目标,结合传统的罚函数（PF）优化思想,给出了基于PF的lp范数迭代重构算法,需要的观测样本数大大低于基于l1范数的优化计算需求,并通过数值实验表明该算法对稀疏信号具有较优的重构效果。
英文摘要：
Reconstruction of sparse signals is an important issue in compressed sensing.Typical algorithms for sparse signals are based on l1-norm optimization,which need more measurements.With minimizing lp-norm as the goal,combined with traditional penalty function optimization method,an iterative reconstruction algorithm for lp-norm optimization based on penalty function was proposed,which needed far less measurements than l1-norm optimization.Numerical results show that the proposed algorithm has good performance on sparse signal reconstruction.
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