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首页> 外文期刊>IEE Proceedings. Part D >Real-time identification of missile aerodynamics using a linearised Kalman filter aided by an artificial neural network
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Real-time identification of missile aerodynamics using a linearised Kalman filter aided by an artificial neural network

机译：使用线性化卡尔曼滤波器和人工神经网络实时识别导弹的空气动力学特性

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The paper investigates the problem of real-time identification of aerodynamic derivatives in a guided missile application. This application provides a severe test for any parameter estimator, since it has to identify the linearised parameters of a multivariable, nonlinear, time variant, noisy plant, which is initially unstable and then becomes lightly damped. Initially, two radically different approaches are taken by designing both a linearised Kalman filter (LKF) estimator and an artificial neural network (ANN) based estimator. A hybrid estimator is then formed by an LKF, which is aided by the ANN. This produces a new estimator which has superior performance to those from which it is derived. The performance of these estimators is assessed with a nonlinear single plane model against eight types of engagements.
机译：本文研究了在制导导弹应用中空气动力学导数的实时识别问题。该应用程序必须对任何参数估计器进行严格的测试，因为它必须识别多变量，非线性，时变，有噪声的设备的线性化参数，该设备最初是不稳定的，然后变得微弱衰减。最初，通过设计线性化卡尔曼滤波器（LKF）估计器和基于人工神经网络（ANN）的估计器，采用了两种截然不同的方法。然后由LKF形成混合估计器，并由ANN辅助。这样就产生了一种新的估算器，其性能要优于其得出的估算器。这些估计量的性能是通过针对八种类型的啮合的非线性单平面模型进行评估的。

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