针对极化敏感面阵的极化域-空域联合谱估计,现有的多重信号分类(Multiple Signal Classification,MUSIC)算法需要进行四维谱峰搜索,计算量较大。建立了一种极化参数与空域参数分离的长矢量模型,在此基础上提出了一种基于不等式约束的降...针对极化敏感面阵的极化域-空域联合谱估计,现有的多重信号分类(Multiple Signal Classification,MUSIC)算法需要进行四维谱峰搜索,计算量较大。建立了一种极化参数与空域参数分离的长矢量模型,在此基础上提出了一种基于不等式约束的降维MUSIC算法。利用极化矢量的模值有界性,将联合谱估计问题转化为不等式约束优化问题,在空间域进行谱峰搜索先行估计出信号的波达方向(Direction of Arrival,DOA),进而估计极化相位差和极化幅角。与4D-MUSIC算法相比,所提算法将四维搜索降低至二维,运算量显著降低。计算机仿真实验证明了算法的有效性和高精度性。展开更多
针对目前极化敏感面阵空域-极化域联合谱估计运算量大、耗时长的问题,提出一种降维求根MUSIC(Multiple Signal Classification)优化算法。通过对接收信号进行降维处理,提出新的求解模型将传统四维MUSIC转化为两个一维求根MUSIC求解空域...针对目前极化敏感面阵空域-极化域联合谱估计运算量大、耗时长的问题,提出一种降维求根MUSIC(Multiple Signal Classification)优化算法。通过对接收信号进行降维处理,提出新的求解模型将传统四维MUSIC转化为两个一维求根MUSIC求解空域波达方向和引用已求解出的空域信息结合拉格朗日乘子法解决来波信号极化信息估计问题。相比传统的4D-MUSIC和秩亏MUSIC,所提算法在不损失估计精度的前提下提高了运算速度,降低了运算复杂度,无需谱峰搜索过程,消除了因搜索步长而导致的量化误差。对日后大规模阵列计算及MIMO(Multiple Input Multiple Output)雷达引入提供快速求解方法。仿真实验表明,所提算法在低信噪比0 dB下空域误差约为0.85°,速度相比秩亏MUSIC提升了约64.7%,验证了该算法的有效性和高精度性。展开更多
The problem of joint direction of arrival(DOA)and polarization estimation for polarization sensitive coprime planar arrays(PS-CPAs)is investigated,and a fast-convergence quadrilinear decomposition approach is proposed...The problem of joint direction of arrival(DOA)and polarization estimation for polarization sensitive coprime planar arrays(PS-CPAs)is investigated,and a fast-convergence quadrilinear decomposition approach is proposed.Specifically,we first decompose the PS-CPA into two sparse polarization sensitive uniform planar subarrays and employ propagator method(PM)to construct the initial steering matrices separately.Then we arrange the received signals into two quadrilinear models so that the potential DOA and polarization estimates can be attained via quadrilinear alternating least square(QALS).Subsequently,we distinguish the true DOA estimates from the approximate intersecting estimations of the two subarrays in view of the coprime feature.Finally,the polarization estimates paired with DOA can be obtained.In contrast to the conventional QALS algorithm,the proposed approach can remarkably reduce the computational complexity without degrading the estimation performance.Simulations demonstrate the superiority of the proposed fast-convergence approach for PS-CPAs.展开更多
文摘针对极化敏感面阵的极化域-空域联合谱估计,现有的多重信号分类(Multiple Signal Classification,MUSIC)算法需要进行四维谱峰搜索,计算量较大。建立了一种极化参数与空域参数分离的长矢量模型,在此基础上提出了一种基于不等式约束的降维MUSIC算法。利用极化矢量的模值有界性,将联合谱估计问题转化为不等式约束优化问题,在空间域进行谱峰搜索先行估计出信号的波达方向(Direction of Arrival,DOA),进而估计极化相位差和极化幅角。与4D-MUSIC算法相比,所提算法将四维搜索降低至二维,运算量显著降低。计算机仿真实验证明了算法的有效性和高精度性。
基金supported by the Open Research Fund of the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(No.CEMEE2019Z0104B)。
文摘The problem of joint direction of arrival(DOA)and polarization estimation for polarization sensitive coprime planar arrays(PS-CPAs)is investigated,and a fast-convergence quadrilinear decomposition approach is proposed.Specifically,we first decompose the PS-CPA into two sparse polarization sensitive uniform planar subarrays and employ propagator method(PM)to construct the initial steering matrices separately.Then we arrange the received signals into two quadrilinear models so that the potential DOA and polarization estimates can be attained via quadrilinear alternating least square(QALS).Subsequently,we distinguish the true DOA estimates from the approximate intersecting estimations of the two subarrays in view of the coprime feature.Finally,the polarization estimates paired with DOA can be obtained.In contrast to the conventional QALS algorithm,the proposed approach can remarkably reduce the computational complexity without degrading the estimation performance.Simulations demonstrate the superiority of the proposed fast-convergence approach for PS-CPAs.
