一种压缩采样中的稀疏度自适应子空间追踪算法

 针对压缩采样中未知稀疏度的信号,本文提出一种自适应子空间追踪算法.首先,采用了一种基于匹配测试的估计方法获取稀疏度的估计值,再通过子空间追踪重构信号.若子空间追踪不能成功重构,则通过渐近增加信号稀疏度的方法实施估计,而上述过程可描述为在弱匹配原则下新原子的选取过程.仿真结果表明,本文的算法可以准确有效重构信号,同时运算量也较低.

A Sparsity Adaptive Subspace Pursuit Algorithm for Compressive Sampling

A novel sparsity adaptive subspace pursuit algorithm is proposed for sparse signals with unkown sparsity.First,a new sparsity estimation method based on matching test is used to get an initial estimated value.If subspace pursuit cannot reconstruct sparse signal successfully,the estimated value is increased each iteration.Weak matching is used in the iteration to select new atoms.Compared to other algorithms,it is competitive both in running speed and recovering accuracy.