摘要
针对观测和传感矩阵都存在噪声扰动的欠定线性系统的稀疏恢复问题,该文基于FOCUSS(FOCal Underdetermined System Solver)算法提出了一种改进算法——SD(Synchronous Descending)-FOCUSS。文中由MAP(最大后验)估计方法推导出系统模型的的目标函数,应用松弛迭代算法对其进行优化从而找到近似最优的稀疏解。SD-FOCUSS算法可应用于MMV(多观测向量)模型。可证明SD-FOCUSS是收敛算法;最后用仿真实验展示了与其他算法相比时,新算法在准确性、稳定性等方面的优越性。
For sparse recovery of underdetermined linear systems where noise perturbations exist in both the measurements and sensing matrix, based on FOCal Underdetermined System Solver （FOCUSS） algorithm, an improved algorithm, named Synchronous Descending （SD） -FOCUSS, is proposed. The objective function of system model is deduced through a Maximum A Posteriori （MAP） estimation; then approximate optimum sparse-solution can be found while optimizing objective function using iterative relaxation algorithm. Another breakthrough of SD-FOCUSS is that the new algorithm can be applied to Multiple Measurement Vector （MMV） models. The convergence of SD-FOCUSS algorithm can be established with mathematical proof. The simulation results illustrate advantages of the new algorithm on accuracy and stability compared with other algorithms.
出处
《电子与信息学报》
EI
CSCD
北大核心
2012年第8期1813-1818,共6页
Journal of Electronics & Information Technology
关键词
信号处理
稀疏恢复
模型噪声
SD—FOCUSS
收敛性
Signal processing
Sparse recovery
Model noise
Synchronous Descending-FOCal UnderdeterminedSystem Solver （SD-FOCUSS）
Convergence
