在测量结果状态不断变化的条件下估算小型无人飞行器方位的测量程序综述

A. A. Kostoglotov, V. Zekhtser, A. Penkov, S. Lazarenko
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引用次数: 0

摘要

本文考虑的问题是如何处理小型无人飞行器智能机载测量系统的微机械传感器测量结果状态不断变化的测量信息。当飞行器在空中飞行时,测量结果的状态会从确认到定向发生变化,例如,由于传感器缺陷、测量通道退化、微机械传感器因气团运动引起的振动和冲击而出现错误的测量输出信号。因此,小型无人飞行器失去稳定性的概率会增加,有必要提高在测量结果不断变化的情况下估计其方位的精度。卡尔曼结构的测量程序被考虑在内,其方程是根据状态的过渡矩阵和噪声矩阵的参数以及扰动矢量精确确定的,是测量过程的特征。参数值由一个基于动态数学模型的测量信息转换数学模型确定,该数学模型将所开发的测量程序与使用传统过渡矩阵的测量程序区分开来。神经网络用于查找未知参数。神经网络以多层感知器为基础,采用误差反向传播算法进行训练。根据数学建模和测量实验的结果,发现基于动态数学模型的综合测量程序的精度高于卡尔曼结构的经典过渡矩阵测量程序的精度。研究结果将有助于为在测量结果状态不断变化的条件下运行的车载测量系统开发智能测量程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthesis of a measurement procedure for estimating the orientation of a small unmanned aerial vehicle under conditions of changing status of measurement results
The problem of processing measurement information with changing status of measurement results of micromechanical sensors of an intelligent on-board measuring system of a small unmanned aerial vehicle is considered. While vehicle is in the air, the measurement results status changes from confirmed to orienting, for example, due to sensor defects, degradation of the measuring channel, the appearance of false measurement output signals of micromechanical sensors due to vibrations and shocks caused by the movement of air masses. As a result, the probability of stability loss of a small unmanned aerial vehicle increases and it is necessary to raise the accuracy of estimating its orientation in conditions of changing status of measurement results. The measuring procedure of the Kalman structure is considered, the equations of which are determined with accuracy to the parameters of the transition and noise matrices of the state, as well as the perturbation vector, characterizing the measuring process. The parameter values are determined by a mathematical model for converting measuring information based on a dynamic mathematical model, which distinguishes the developed measuring procedure from a measuring procedure with a classical transition matrix. A neural network is used to find unknown parameters. A multilayer perceptron was selected as the basis of a neural network, for which an error back propagation algorithm is used to train. Based on the results of mathematical modeling and measurement experiment, it was found that the accuracy of the synthesized measuring procedure based on a dynamic mathematical model is higher than the accuracy of the measuring procedure of the Kalman structure with a classical transition matrix. The results of the study will be useful in the development of intelligent measurement procedures for on-board measurement systems operating under conditions of changing status of measurement results
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