多用户协同导航先进优化算法分析

M. A. Enright
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引用次数: 3

摘要

低信噪比(SNR)环境下的导航仍然是GNSS面临的一个极具挑战性的问题。波形的多径衰落、阴影、干扰等影响不仅极大地限制了导航精度,而且增加了接收信号电平低于最小阈值时发生中断的概率。较新的导航方法集中在使用地面信号,以保持更高的信号功率,如数字电视(DTV),蜂窝等。这些被称为机会信号(SoOP)。本研究的重点是将这些信号应用于到达时差(TDOA)协同导航网络。这类网络的一个主要问题是定位算法的收敛性。为了解决这个问题,我们的研究从不同的角度来研究这个问题。在这里,我们将把这个问题作为一个凸优化问题来处理。主要思想是,如果问题以这种形式构建,我们可以使用这个很好理解的领域来开发关于合作导航的更快收敛的算法。因此,本研究结合了分析分析、算法开发和描述特定条件下性能的仿真结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An analysis of advanced optimization algorithms for multiuser cooperative navigation
Navigation in low signal-to-noise ratio (SNR) environments continues to be an extremely challenging problem for GNSS. Effects such as multipath fading, shadowing, jamming of the waveform not only greatly limit the navigation accuracy, but also increase the outage probability that occurs when the received signal level falls below the minimum threshold. Newer navigation approaches have focused on employing terrestrial signals that maintain higher signal power such as digital television (DTV), cellular, etc. These are referred to as signals of opportunity (SoOP). The focus of this research is using these signals in a time difference of arrival (TDOA) cooperative navigation network. A major problem arises with relation to convergence of the positioning algorithm for this type of network. To address this problem, our research studies this problem from a different perspective. Here we will pose the problem as a convex optimization problem and address it as such. The main idea being that if the problem is structured in this form, we can use this well understood area to develop algorithms for faster convergence with respect to cooperative navigation. As such, this research is a combination of analytical analysis, algorithm development, and simulation results that describe performance under certain conditions.
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