论文标题
一种非线性子空间方法，用于从短数据记录的参数估计以及应用于瑞利褪色的短数据记录
A Nonlinear Subspace Approach for Parametric Estimation of PDFs from Short Data Records with Application to Rayleigh Fading
论文作者
论文摘要
本文解决了有限数据集的广泛概率密度函数的实时参数估计问题。这种类型的估计解决了需要关节感应和驱动的最新应用。建议的估计量在非线性子空间中运行，即分布的参数空间在测量样品空间中创建。这使估计器能够嵌入有关计算中分布的先验可用信息，以产生由仅考虑正确的密度函数的信号组件诱导的参数估计值。它还使它能够无效估算过程中不属于此类的组件的效果。估算器可以很高的精度可以快速计算出从短数据记录中的广泛概率密度函数的参数。开发了该方法，并为瑞利分布提供了基本的正确性证明，该证明用于表征无线通信渠道，在严重混乱的环境中经历快速褪色的无线通信渠道。仿真结果证明了建议的程序的功能及其与常规基于规范的估计技术相比具有明显的优势。结果还显示了建议的方法估计其他密度函数的能力，包括用于表征无线通信中阴影的两参数对数正态分布。
This paper tackles the issue of real-time parametric estimation of a wide class of probability density functions from limited datasets. This type of estimation addresses recent applications that require joint sensing and actuation. The suggested estimator operates in the nonlinear subspace that the parameter space of the distribution creates in the measurement sample space. This enables the estimator to embed a priori available information about the distribution in the computations to produce parameter estimates that are induced by signal components belonging only to the correct class of density functions being considered. It also enables it to nullify the effect of those components that do not belong to this class on the estimation process. The estimator can, with high accuracy, compute quickly the parameters of a wide class of probability density functions from short data records. The approach is developed and basic proofs of correctness are carried-out for the Rayleigh distribution, which is used to characterize wireless communication channels experiencing fast fading in heavily cluttered environments. Simulation results demonstrate the capabilities of the suggested procedure and the clear advantages it has over conventional norm-based estimation techniques. The results also show the ability of the suggested approach to estimate other density functions including the two-parameter lognormal distribution used to characterize shadowing in wireless communication.
