Electromagnetic vector sensor(EMVS)embedded multiple-input multiple-output(MIMO)radar is an emerging technology that enables two-dimensional(2D)direction of arrival(DOA)estimation.In this paper,we proposed a low-compl...Electromagnetic vector sensor(EMVS)embedded multiple-input multiple-output(MIMO)radar is an emerging technology that enables two-dimensional(2D)direction of arrival(DOA)estimation.In this paper,we proposed a low-complexity estimation of signal parameters via rotational invariance techniques(ESPRIT)algorithm for uniform linear array(ULA)EMVSMIMO radar at a monostatic,enabling rapid estimation of 2D target angles.Initially,by employing a selection matrix,complexity reduction is applied to the array data,thereby eliminating redundancy in the array data.Subsequently,leveraging the rotation invariance propagator method(PM)algorithm,obtain the estimation of the elevation angle,but due to array sparsity,this estimation exhibits ambiguity.Then,the vector cross-product(VCP)technique is employed to achieve unambiguous 2D-DOA estimation.Finally,the aforementioned estimates are synthesized to obtain highresolution,unambiguous elevation angle estimation.The proposed algorithm is applicable to largescale and spare EMVS-MIMO radar systems and provides higher estimation accuracy compared to existing ESPRIT algorithms.The effectiveness of the algorithm is verified through matrix laboratory(MATLAB)simulations.展开更多
将传统电磁矢量均匀阵列推广为电磁矢量互质阵列,突破了阵元间距不大于半波长的限制。提出了电磁矢量互质阵列中基于降维Capon的波达方向(Direction of arrival,DOA)和极化联合估计算法。该算法无需假设已知极化信息,且只需一维搜索,避...将传统电磁矢量均匀阵列推广为电磁矢量互质阵列,突破了阵元间距不大于半波长的限制。提出了电磁矢量互质阵列中基于降维Capon的波达方向(Direction of arrival,DOA)和极化联合估计算法。该算法无需假设已知极化信息,且只需一维搜索,避免了多维搜索,可实现DOA和极化参数自动配对;与相同阵元数的均匀阵列相比,明显提高了角度估计性能,并拓展了天线孔径,具有相对较高的自由度,且降低了运算复杂度。相同阵列及参数条件下,本文算法的角度估计性能优于ESPRIT算法和三线性分解算法。展开更多
针对复杂多变的海杂波常伴随待测信号混入雷达系统而严重干扰目标信号的波达角度估计的问题,提出一种新的基于电磁矢量阵列的四元数模型,通过分数低阶统计量特性抑制海杂波噪声的二维波达角度估计方法。首先推导出电磁矢量阵列的四元数...针对复杂多变的海杂波常伴随待测信号混入雷达系统而严重干扰目标信号的波达角度估计的问题,提出一种新的基于电磁矢量阵列的四元数模型,通过分数低阶统计量特性抑制海杂波噪声的二维波达角度估计方法。首先推导出电磁矢量阵列的四元数导向矢量,然后利用分数低阶概念得到四元数分数低阶协方差矩阵,最后根据奇异值分解MUSIC(Multiple Signal Classification)算法得到谱估计公式。仿真实验结果表明,与标量阵列和非四元数电磁矢量阵列MUSIC算法相比较,该方法可较好地抑制海杂波噪声,且能提高估计精度。展开更多
由于四元数MUSIC(Multiple Signal Classification)算法计算量较大,本文结合声矢量传感器的四元数导向矢量模型,提出了一种声矢量阵列波达方向估计的四元数最小范数法。首先,将声矢量阵列输出协方差矩阵奇异值分解所得到的(M-N)×M...由于四元数MUSIC(Multiple Signal Classification)算法计算量较大,本文结合声矢量传感器的四元数导向矢量模型,提出了一种声矢量阵列波达方向估计的四元数最小范数法。首先,将声矢量阵列输出协方差矩阵奇异值分解所得到的(M-N)×M维(M为阵元数、N为信源数)噪声子空间依最小范数(Minimum-Norm,MN)准则构建为一个新的四元数域1×M维噪声矢量。接着,提出了简化的谱峰搜索公式,理论分析了四元数最小范数法在搜索计算量上的优势。对提出的算法与Q-MUSIC算法进行了对比。结果显示:该算法至少能节省50%的谱峰搜索量。同时,提出的算法构建的低维噪声矢量与导向矢量间的正交性优于高维噪声子空间与导向矢量间的正交性,在0dB时,其范德蒙范数和谱峰分别为Q-MUSIC算法的1/3和3倍。另外,该算法在减小谱峰搜索量的同时,可以较好地分辨信源波达方向,且其统计特性与四元数MUSIC算法相当。提出的算法不局限于L线阵,也适用于双平行线阵及面阵。展开更多
文摘Electromagnetic vector sensor(EMVS)embedded multiple-input multiple-output(MIMO)radar is an emerging technology that enables two-dimensional(2D)direction of arrival(DOA)estimation.In this paper,we proposed a low-complexity estimation of signal parameters via rotational invariance techniques(ESPRIT)algorithm for uniform linear array(ULA)EMVSMIMO radar at a monostatic,enabling rapid estimation of 2D target angles.Initially,by employing a selection matrix,complexity reduction is applied to the array data,thereby eliminating redundancy in the array data.Subsequently,leveraging the rotation invariance propagator method(PM)algorithm,obtain the estimation of the elevation angle,but due to array sparsity,this estimation exhibits ambiguity.Then,the vector cross-product(VCP)technique is employed to achieve unambiguous 2D-DOA estimation.Finally,the aforementioned estimates are synthesized to obtain highresolution,unambiguous elevation angle estimation.The proposed algorithm is applicable to largescale and spare EMVS-MIMO radar systems and provides higher estimation accuracy compared to existing ESPRIT algorithms.The effectiveness of the algorithm is verified through matrix laboratory(MATLAB)simulations.
文摘将传统电磁矢量均匀阵列推广为电磁矢量互质阵列,突破了阵元间距不大于半波长的限制。提出了电磁矢量互质阵列中基于降维Capon的波达方向(Direction of arrival,DOA)和极化联合估计算法。该算法无需假设已知极化信息,且只需一维搜索,避免了多维搜索,可实现DOA和极化参数自动配对;与相同阵元数的均匀阵列相比,明显提高了角度估计性能,并拓展了天线孔径,具有相对较高的自由度,且降低了运算复杂度。相同阵列及参数条件下,本文算法的角度估计性能优于ESPRIT算法和三线性分解算法。
文摘针对复杂多变的海杂波常伴随待测信号混入雷达系统而严重干扰目标信号的波达角度估计的问题,提出一种新的基于电磁矢量阵列的四元数模型,通过分数低阶统计量特性抑制海杂波噪声的二维波达角度估计方法。首先推导出电磁矢量阵列的四元数导向矢量,然后利用分数低阶概念得到四元数分数低阶协方差矩阵,最后根据奇异值分解MUSIC(Multiple Signal Classification)算法得到谱估计公式。仿真实验结果表明,与标量阵列和非四元数电磁矢量阵列MUSIC算法相比较,该方法可较好地抑制海杂波噪声,且能提高估计精度。
文摘由于四元数MUSIC(Multiple Signal Classification)算法计算量较大,本文结合声矢量传感器的四元数导向矢量模型,提出了一种声矢量阵列波达方向估计的四元数最小范数法。首先,将声矢量阵列输出协方差矩阵奇异值分解所得到的(M-N)×M维(M为阵元数、N为信源数)噪声子空间依最小范数(Minimum-Norm,MN)准则构建为一个新的四元数域1×M维噪声矢量。接着,提出了简化的谱峰搜索公式,理论分析了四元数最小范数法在搜索计算量上的优势。对提出的算法与Q-MUSIC算法进行了对比。结果显示:该算法至少能节省50%的谱峰搜索量。同时,提出的算法构建的低维噪声矢量与导向矢量间的正交性优于高维噪声子空间与导向矢量间的正交性,在0dB时,其范德蒙范数和谱峰分别为Q-MUSIC算法的1/3和3倍。另外,该算法在减小谱峰搜索量的同时,可以较好地分辨信源波达方向,且其统计特性与四元数MUSIC算法相当。提出的算法不局限于L线阵,也适用于双平行线阵及面阵。
