Adaptive estimation of signals of opportunity
Adaptive estimation of signals of opportunity
复制标题
机会信号的自适应估计
DOI：
10.15781/t28c9rm4v
复制
发表时间：
2014
期刊：
影响因子：
3.9
通讯作者：
T. Humphreys
中科院分区：
文献类型：
--
作者：
Z. Kassas;Vaibhav Ghadiok;T. Humphreys
为了利用未知的环境射频机会信号（SOP）进行定位和导航，必须利用表征其振荡器的稳定性的一组参数来估计其状态沿着。SOP可以被建模为由过程噪声驱动的随机动态系统。这种过程噪声的统计数据对于想要利用SOP进行定位和导航的接收机来说通常是未知的。不正确的统计模型会危及估计的最优性，并可能导致滤波发散。这就需要开发自适应滤波器，它通过滤波器学习过程提供了对固定滤波器的显著改进。本文提出了两种自适应滤波器：基于新息的最大似然滤波器和交互式多模型滤波器，并比较了它们的性能和复杂度。数值和实验结果表明，这些过滤器的优越性超过固定，失配滤波器。
To exploit unknown ambient radio frequency signals of opportunity (SOPs) for positioning and navigation, one must estimate their states along with a set of parameters that characterize the stability of their oscillators. SOPs can be modeled as stochastic dynamical systems driven by process noise. The statistics of such process noise is typically unknown to the receiver wanting to exploit the SOPs for positioning and navigation. Incorrect statistical models jeopardize the estimation optimality and may cause filter divergence. This necessitates the development of adaptive filters, which provide a significant improvement over fixed filters through the filter learning process. This paper develops two such adaptive filters: an innovationbased maximum likelihood filter and an interacting multiple model filter and compares their performance and complexity. Numerical and experimental results are presented demonstrating the superiority of these filters over fixed, mismatched filters.