利用机器学习从飞机内部传感器进行实时风力估算

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ali Motamedi, Mehdi Sabzehparvar, Mahdi Mortazavi
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引用次数: 0

摘要

本文探讨了一种利用机器学习技术进行实时风速矢量和参数估计以及风模型识别的方法。所提出的方法仅使用飞机的状态测量值,不需要控制指令、航空数据系统或卫星数据。小型无人驾驶飞行器(UAV)可以从这种方法中获益,因为它只依赖普通传感器的测量结果作为姿态和航向参考系统。外部信息源的独立性使估算不受故意误差的影响。该算法在涉及分类和回归的两步深度学习过程中使用了长短期记忆神经网络(LSTM NN)。使用四个不同的标注风模型训练分类神经网络,同时训练单个回归神经网络来估计每个风模型的速度矢量和参数。线性加速度、角速度和欧拉角测量值被用作训练网络的输入。该算法建议在第一步识别准确的风模型,在第二步使用训练有素的网络正确分配的估计值来估计风速矢量和参数。学习过程中的数据集是固定翼无人机在不同风力模型下进行平直和水平转弯机动的非线性六自由度模拟。蒙特卡洛模拟证明了所提算法在识别风模型和估计三维风速矢量及参数方面的准确性和快速性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Real-time wind estimation from the internal sensors of an aircraft using machine learning

Real-time wind estimation from the internal sensors of an aircraft using machine learning

A real-time wind velocity vector and parameters estimation and wind model identification approach using a machine learning technique is addressed in this paper. The proposed method uses only the state measurements of an aircraft and does not require control commands, air data systems, or satellite-based data. Small unmanned aerial vehicles (UAVs) can benefit from this method, since it relies solely on measurement results from the common sensors as an attitude and heading reference system. The independence of external sources of information made estimations resistant to intentional errors. This algorithm uses long short-term memory neural networks (LSTM NNs) in a two-step deep learning process involving classification and regression. A classification NN was trained with four different labeled wind models, while individual regression NNs were trained to estimate the velocity vector and parameters of each wind model. The linear acceleration, angular velocity, and Euler angle measurements were used as the inputs of trained networks. The algorithm suggests in its first step identifying the exact wind model, and in its second step estimating the wind velocity vector and parameters using a properly assigned estimation from a trained network. A nonlinear six-degree-of-freedom simulation of straightforward and level turn maneuvers of a fixed-wing UAV in the presence of different wind models served as the dataset in the learning process. Monte Carlo simulations proved the accuracy and rapidity of the proposed algorithm in identifying the wind model and estimating three-dimensional wind velocity vector and parameters.

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来源期刊
Soft Computing
Soft Computing 工程技术-计算机:跨学科应用
CiteScore
8.10
自引率
9.80%
发文量
927
审稿时长
7.3 months
期刊介绍: Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.
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