Yin Jiexin, Wang Ding, Wu Ying
National Digital Switching System Engineering and Technology Research Center, Zhengzhou 450002, China.
Zhengzhou Information Science and Technology Institute, Zhengzhou 450002, China.
Sensors (Basel). 2018 Jan 23;18(2):324. doi: 10.3390/s18020324.
This paper focuses on the localization methods for multiple sources received by widely separated arrays. The conventional two-step methods extract measurement parameters and then estimate the positions from them. In the contrast to the conventional two-step methods, direct position determination (DPD) localizes transmitters directly from original sensor outputs without estimating intermediate parameters, resulting in higher location accuracy and avoiding the data association. Existing subspace data fusion (SDF)-based DPD developed in the frequency domain is computationally attractive in the presence of multiple transmitters, whereas it does not use special properties of signals. This paper proposes an improved SDF-based DPD algorithm for strictly noncircular sources. We first derive the property of strictly noncircular signals in the frequency domain. On this basis, the observed frequency-domain vectors at all arrays are concatenated and extended by exploiting the noncircular property, producing extended noise subspaces. Fusing the extended noise subspaces of all frequency components and then performing a unitary transformation, we obtain a cost function for each source location, which is formulated as the smallest eigenvalue of a real-valued matrix. To avoid the exhaustive grid search and solve this nonlinear function efficiently, we devise a Newton-type iterative method using matrix Eigen-perturbation theory. Simulation results demonstrate that the proposed DPD using Newton-type iteration substantially reduces the running time, and its performance is superior to other localization methods for both near-field and far-field noncircular sources.
本文聚焦于由相距甚远的阵列接收的多个信号源的定位方法。传统的两步法先提取测量参数，然后据此估计位置。与传统两步法不同，直接位置确定（DPD）直接从原始传感器输出定位发射机，无需估计中间参数，从而提高了定位精度并避免了数据关联。现有的基于子空间数据融合（SDF）的DPD是在频域中开发的，在存在多个发射机的情况下具有计算优势，但其未利用信号的特殊特性。本文针对严格非循环信号源提出了一种改进的基于SDF的DPD算法。我们首先推导了严格非循环信号在频域中的特性。在此基础上，通过利用非循环特性拼接并扩展所有阵列处观测到的频域向量，生成扩展噪声子空间。融合所有频率分量的扩展噪声子空间，然后进行酉变换，我们得到每个信号源位置的代价函数，该函数被表述为实值矩阵的最小特征值。为避免穷举网格搜索并有效求解此非线性函数，我们利用矩阵特征值扰动理论设计了一种牛顿型迭代方法。仿真结果表明，所提出的使用牛顿型迭代的DPD显著减少了运行时间，并且其性能在近场和远场非循环信号源方面均优于其他定位方法。
