首页> 外文期刊>Radioelectronics and Communications Systems >THE NEURAL-NETWORK AND STATISTICAL ALGORITHMS FOR ESTIMATING COORDINATES OF A SOURCE OF RADIO RADIATION IN MULTI-POSITION RADIO SYSTEMS IN THE PRESENCE OF ABNORMAL ERRORS OF PRIMARY PARAMETER MEASUREMENT
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THE NEURAL-NETWORK AND STATISTICAL ALGORITHMS FOR ESTIMATING COORDINATES OF A SOURCE OF RADIO RADIATION IN MULTI-POSITION RADIO SYSTEMS IN THE PRESENCE OF ABNORMAL ERRORS OF PRIMARY PARAMETER MEASUREMENT

机译:存在主要参数测量误差的神经网络和统计算法,用于估计多位置无线电系统中的辐射源的坐标

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摘要

The paper considers algorithms for estimating the coordinates of a radio radiation source by a single measurement in the angle-measuring and difference-type range-measuring multi-position radio systems in the presence of abnormal errors of measurement of primary parameters. The algorithms are developed based on neural networks with direct coupling. Analysis of accuracy characteristics of the neural-network estimation of coordinates is performed — as compared with the statistical Bayes-type estimation obtained with the use of Huber's minimax approach.
机译:本文考虑了在存在主要参数测量异常的情况下,通过在角度测量和差异类型测距多位置无线电系统中进行单次测量来估计无线电辐射源坐标的算法。该算法是基于具有直接耦合的神经网络开发的。与使用Huber的minimax方法获得的统计贝叶斯型估计相比,进行了坐标神经网络估计的精度特征分析。

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