【摘 要】
：
为提高压缩感知子空间追踪算法的信号重建概率及精度,提出一种递减候选集正则化子空间追踪算法.该算法基于Co Sa MP/SP算法并加以改进,将迭代过程分成若干个阶段,在每个阶段
【机 构】
：
上海大学通信与信息工程学院,上海大学特种光纤与光接入网省部共建重点实验室,
论文部分内容阅读
为提高压缩感知子空间追踪算法的信号重建概率及精度,提出一种递减候选集正则化子空间追踪算法.该算法基于Co Sa MP/SP算法并加以改进,将迭代过程分成若干个阶段,在每个阶段均采用类Co Sa MP/SP算法进行迭代计算,但各阶段的候选集原子个数依次递减,同时按正则化方法选择新的候选集原子.实验仿真对比结果表明,与同类算法相比,所提出算法能够以更高概率重建信号,在噪声环境下也具有较高的重建精度.
In order to improve the signal reconstruction probability and accuracy of compressed sensing subspace tracking algorithm, a descending candidate set regularization subspace tracking algorithm is proposed. The algorithm is based on Co Sa MP / SP algorithm and is improved. The iterative process is divided into several stages. In In each phase, Co Sa MP / SP algorithm is used to calculate iteratively, but the number of candidate atoms in each phase decreases in sequence and regularization method is used to select new candidate atoms. The experimental results show that compared with similar algorithms Compared with the proposed algorithm, the proposed algorithm can reconstruct the signal with higher probability and has higher reconstruction accuracy under noisy environment.
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