This paper introduces the advances of ultra-wideband （UWB） and super-wideband （SWB） planar antennas based on the printed monopole, microstrip slot and other planar antenna designs in the last decade. A brief histo...This paper introduces the advances of ultra-wideband （UWB） and super-wideband （SWB） planar antennas based on the printed monopole, microstrip slot and other planar antenna designs in the last decade. A brief history of the ultrawideband antennas is first provided. Several types of planar antennas for UWB systems with band-notched designs are reviewed. Special SWB planar antenna designs with the bandwidth ratio greater than 10：1 including metal-plate and printed monopole antennas and tapered slot antennas are presented and compared.展开更多
由于无网格(grid-less)稀疏重构方法的波达方向(direction of arrival,DOA)估计数学模型为单快拍形式,因此该方法只有在噪声电平趋近于零时才具有优越的性能.为了提高grid-less方法在信噪比(signal-to-noise ratio,SNR)较低时宽带相干...由于无网格(grid-less)稀疏重构方法的波达方向(direction of arrival,DOA)估计数学模型为单快拍形式,因此该方法只有在噪声电平趋近于零时才具有优越的性能.为了提高grid-less方法在信噪比(signal-to-noise ratio,SNR)较低时宽带相干信源的估计性能,提出了一种多快拍grid-less DOA估计方法.首先,对多快拍阵列观测矢量实施奇异值分解(singular value decomposition,SVD)获得观测矩阵的时域信号子空间,通过观测矩阵到时域信号子空间的投影实现观测矩阵的降噪;然后,为了不增加多快拍计算复杂度,将降噪后观测矩阵的列向量加权累加处理得到单快拍形式;最后,从理论上证明了本文提出的GL-SVD方法求解的模型是凸的,能够实现宽带信号DOA的精确重构.仿真结果表明,该方法在低SNR以及宽带相干信源情况下的估计精度都高于L 1范数最小化奇异值分解(L 1-norm minimum singular value decomposition,L 1-SVD)和离格稀疏贝叶斯推断奇异值分解(off-grid sparse Bayesian inference singular value decomposition,OGSBI-SVD),且在较小角度间隔的情况下具有更高的估计概率和分辨率.展开更多
The traditional super-resolution direction finding methods based on sparse recovery need to divide the estimation space into several discrete angle grids, which will bring the final result some error. To this problem,...The traditional super-resolution direction finding methods based on sparse recovery need to divide the estimation space into several discrete angle grids, which will bring the final result some error. To this problem, a novel method for wideband signals by sparse recovery in the frequency domain is proposed. The optimization functions are found and solved by the received data at every frequency, on this basis, the sparse support set is obtained, then the direction of arrival (DOA) is acquired by integrating the information of all frequency bins, and the initial signal can also be recovered. This method avoids the error caused by sparse recovery methods based on grid division, and the degree of freedom is also expanded by array transformation, especially it has a preferable performance under the circumstances of a small number of snapshots and a low signal to noise ratio (SNR).展开更多
基金Project supported by the National Natural Science Foundation of China （Grant No.60571053）, and the Shanghai Leading Academic Discipline Project （Grant No.T0102）.
文摘This paper introduces the advances of ultra-wideband （UWB） and super-wideband （SWB） planar antennas based on the printed monopole, microstrip slot and other planar antenna designs in the last decade. A brief history of the ultrawideband antennas is first provided. Several types of planar antennas for UWB systems with band-notched designs are reviewed. Special SWB planar antenna designs with the bandwidth ratio greater than 10：1 including metal-plate and printed monopole antennas and tapered slot antennas are presented and compared.
文摘由于无网格(grid-less)稀疏重构方法的波达方向(direction of arrival,DOA)估计数学模型为单快拍形式,因此该方法只有在噪声电平趋近于零时才具有优越的性能.为了提高grid-less方法在信噪比(signal-to-noise ratio,SNR)较低时宽带相干信源的估计性能,提出了一种多快拍grid-less DOA估计方法.首先,对多快拍阵列观测矢量实施奇异值分解(singular value decomposition,SVD)获得观测矩阵的时域信号子空间,通过观测矩阵到时域信号子空间的投影实现观测矩阵的降噪;然后,为了不增加多快拍计算复杂度,将降噪后观测矩阵的列向量加权累加处理得到单快拍形式;最后,从理论上证明了本文提出的GL-SVD方法求解的模型是凸的,能够实现宽带信号DOA的精确重构.仿真结果表明,该方法在低SNR以及宽带相干信源情况下的估计精度都高于L 1范数最小化奇异值分解(L 1-norm minimum singular value decomposition,L 1-SVD)和离格稀疏贝叶斯推断奇异值分解(off-grid sparse Bayesian inference singular value decomposition,OGSBI-SVD),且在较小角度间隔的情况下具有更高的估计概率和分辨率.
基金supported by the National Natural Science Foundation of China(61501176)University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT-2016017)
文摘The traditional super-resolution direction finding methods based on sparse recovery need to divide the estimation space into several discrete angle grids, which will bring the final result some error. To this problem, a novel method for wideband signals by sparse recovery in the frequency domain is proposed. The optimization functions are found and solved by the received data at every frequency, on this basis, the sparse support set is obtained, then the direction of arrival (DOA) is acquired by integrating the information of all frequency bins, and the initial signal can also be recovered. This method avoids the error caused by sparse recovery methods based on grid division, and the degree of freedom is also expanded by array transformation, especially it has a preferable performance under the circumstances of a small number of snapshots and a low signal to noise ratio (SNR).
