波达方向(direction of arrival,DOA)是阵列信号处理模型中的非线性参数,当信噪比较低时,其估计值会偏离真实值。为了降低无网格DOA估计方法中该问题的阈值,介绍了一种基于无网格的基于协方差的稀疏迭代估计(sparse iterative covarianc...波达方向(direction of arrival,DOA)是阵列信号处理模型中的非线性参数,当信噪比较低时,其估计值会偏离真实值。为了降低无网格DOA估计方法中该问题的阈值,介绍了一种基于无网格的基于协方差的稀疏迭代估计(sparse iterative covariance-based estimation,SPICE)方法。引入了最大似然求根多重信号分类(maximum likelihood root multiple signal classification,ML-Root-MUSIC)来计算DOA,使用最大似然准则来选择根,可以降低阈值并获得更好的分辨率特性。在原始无网格SPICE的优化问题中加入了负熵项,使得无网格SPICE的均方根误差曲线更接近于Cramer-Rao下界。最后,蒙特卡罗仿真实验验证了所提方法在低信噪比非冗余阵列情况下的优越性。展开更多
Many algorithms have been proposed to achieve sparse representation over redundant dictionaries or transforms. A comprehensive understanding of these algorithms is needed when choosing and designing algorithms for par...Many algorithms have been proposed to achieve sparse representation over redundant dictionaries or transforms. A comprehensive understanding of these algorithms is needed when choosing and designing algorithms for particular applications. This research studies a representative algorithm for each category, matching pursuit （MP）, basis pursuit （BP）, and noise shaping （NS）, in terms of their sparsifying capability and computational complexity. Experiments show that NS has the best performance in terms of sparsifying ca- pability with the least computational complexity. BP has good sparsifying capability, but is computationally expensive. MP has relatively poor sparsifying capability and the computations are heavily dependent on the problem scale and signal complexity. Their performance differences are also evaluated for three typical ap- plications of time-frequency analyses, signal denoising, and image coding. NS has good performance for time-frequency analyses and image coding with far fewer computations. However, NS does not perform well for signal denoising. This study provides guidelines for choosing an algorithm for a given problem and for designing or improving algorithms for sparse representation.展开更多
基金Supported by the Joint Research Fund for Overseas Chinese Young Scholars of the National Natural Science Foundation of China (No.60528004)the Key Project of the National Natural Science Foundation of China (No. 60528004)
文摘Many algorithms have been proposed to achieve sparse representation over redundant dictionaries or transforms. A comprehensive understanding of these algorithms is needed when choosing and designing algorithms for particular applications. This research studies a representative algorithm for each category, matching pursuit （MP）, basis pursuit （BP）, and noise shaping （NS）, in terms of their sparsifying capability and computational complexity. Experiments show that NS has the best performance in terms of sparsifying ca- pability with the least computational complexity. BP has good sparsifying capability, but is computationally expensive. MP has relatively poor sparsifying capability and the computations are heavily dependent on the problem scale and signal complexity. Their performance differences are also evaluated for three typical ap- plications of time-frequency analyses, signal denoising, and image coding. NS has good performance for time-frequency analyses and image coding with far fewer computations. However, NS does not perform well for signal denoising. This study provides guidelines for choosing an algorithm for a given problem and for designing or improving algorithms for sparse representation.
