基于最小二乘的迭代多径超分辨技术
Least-squares based iterative multipath super-resolution technique
AI总结：
针对直接序列扩频信号的多径信道估计问题，提出一种基于最小二乘的迭代多径超分辨（LIMS）算法，在数据有限或路径系数相关时仍能有效分辨短间隔到达的多径分量，并适用于递归多径跟踪。
摘要：
本文研究了直接序列扩频信号的多径信道估计问题。为了分辨在短间隔内到达的多径分量，我们提出了一种新算法，称为基于最小二乘的迭代多径超分辨（LIMS）。与传统的超分辨技术（如多信号分类（MUSIC）和基于旋转不变技术的信号参数估计（ESPRIT））相比，我们的算法具有几个吸引人的特点。特别是，即使在传统超分辨技术因数据有限或路径系数相关性而效果不佳的关键情况下，LIMS算法也能产生成功的结果。此外，由于其迭代性质，LIMS算法适用于递归多径跟踪，而传统超分辨技术可能不适用。通过数值仿真，我们表明LIMS算法能够以比传统早-迟鉴别器技术低得多的均方误差分辨紧密到达的独立衰落多径中的第一到达路径。
英文摘要：
In this paper, we study the problem of multipath channel estimation for direct sequence spread spectrum signals. To resolve multipath components arriving within a short interval, we propose a new algorithm called the least-squares based iterative multipath super-resolution (LIMS). Compared to conventional super-resolution techniques, such as the multiple signal classification (MUSIC) and the estimation of signal parameters via rotation invariance techniques (ESPRIT), our algorithm has several appealing features. In particular, even in critical situations where the conventional super-resolution techniques are not very powerful due to limited data or the correlation between path coefficients, the LIMS algorithm can produce successful results. In addition, due to its iterative nature, the LIMS algorithm is suitable for recursive multipath tracking, whereas the conventional super-resolution techniques may not be. Through numerical simulations, we show that the LIMS algorithm can resolve the first arrival path among closely arriving independently faded multipaths with a much lower mean square error than can conventional early-late discriminator based techniques.
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