互补序列集(Complementary Sets of Sequences,CSS)具备良好的自相关性能,但在频谱受限的条件下,自相关性能恶化,因此有必要对频谱受限的CSS,即互补稀疏频率(Complementary Sparse Frequency,CSF)序列集,进行优化设计。本文以加权积分...互补序列集(Complementary Sets of Sequences,CSS)具备良好的自相关性能,但在频谱受限的条件下,自相关性能恶化,因此有必要对频谱受限的CSS,即互补稀疏频率(Complementary Sparse Frequency,CSF)序列集,进行优化设计。本文以加权积分旁瓣电平(Weighted Integral Sidelobe Level,WISL)表征序列集的自相关性能,以频率阻带内的能量(Energy in the Frequency Stopband,EFS)表征序列集的稀疏频谱性能;并将二者的加权和作为目标函数,以序列集为优化变量,在恒模的约束下,建立联合优化问题。针对该优化问题,采用循环迭代算法(Cyclic Iterative Algorithm,CIA)求解,引入辅助变量,将辅助变量和序列集变量迭代求解。由于在每次迭代过程中都可求得序列集的恒模闭式解,相比于其他的CSF序列集优化算法,CIA收敛速度快,所设计CSF序列集的相关特性和频谱特性好,且可抑制指定范围内的自相关旁瓣。仿真结果验证了所提算法的有效性和优异性。展开更多
Based on the recently developed data-driven time-frequency analysis(Hou and Shi, 2013), we propose a two-level method to look for the sparse time-frequency decomposition of multiscale data. In the two-level method, we...Based on the recently developed data-driven time-frequency analysis(Hou and Shi, 2013), we propose a two-level method to look for the sparse time-frequency decomposition of multiscale data. In the two-level method, we first run a local algorithm to get a good approximation of the instantaneous frequency. We then pass this instantaneous frequency to the global algorithm to get an accurate global intrinsic mode function(IMF)and instantaneous frequency. The two-level method alleviates the difficulty of the mode mixing to some extent.We also present a method to reduce the end effects.展开更多
文摘互补序列集(Complementary Sets of Sequences,CSS)具备良好的自相关性能,但在频谱受限的条件下,自相关性能恶化,因此有必要对频谱受限的CSS,即互补稀疏频率(Complementary Sparse Frequency,CSF)序列集,进行优化设计。本文以加权积分旁瓣电平(Weighted Integral Sidelobe Level,WISL)表征序列集的自相关性能,以频率阻带内的能量(Energy in the Frequency Stopband,EFS)表征序列集的稀疏频谱性能;并将二者的加权和作为目标函数,以序列集为优化变量,在恒模的约束下,建立联合优化问题。针对该优化问题,采用循环迭代算法(Cyclic Iterative Algorithm,CIA)求解,引入辅助变量,将辅助变量和序列集变量迭代求解。由于在每次迭代过程中都可求得序列集的恒模闭式解,相比于其他的CSF序列集优化算法,CIA收敛速度快,所设计CSF序列集的相关特性和频谱特性好,且可抑制指定范围内的自相关旁瓣。仿真结果验证了所提算法的有效性和优异性。
基金supported by National Science Foundation of USA (Grants Nos. DMS1318377 and DMS-1613861)National Natural Science Foundation of China (Grant Nos. 11371220, 11671005, 11371173, 11301222 and 11526096)
文摘Based on the recently developed data-driven time-frequency analysis(Hou and Shi, 2013), we propose a two-level method to look for the sparse time-frequency decomposition of multiscale data. In the two-level method, we first run a local algorithm to get a good approximation of the instantaneous frequency. We then pass this instantaneous frequency to the global algorithm to get an accurate global intrinsic mode function(IMF)and instantaneous frequency. The two-level method alleviates the difficulty of the mode mixing to some extent.We also present a method to reduce the end effects.
