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Research on Modeling of Air Target Motion Characteristics and Track Identification Method

机译:空中目标运动特性建模与航迹识别方法研究

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The track is an important basis for the classification and identification of radar air targets. In this paper, the equations of the aircraft motion model, including the horizontal straight, the horizontal arc and the vertical arc, are firstly derived. Then, by taking the Earth-fixed coordinate system as a bridge, the coordinate transformations are used to obtain the radar target parameters, i.e., the range, azimuth and pitch angle. The influence of the Earth curvature in long-distance conditions is compensated. The track recognition method based on radial basis function (RBF) neural network is further designed. Finally, this paper established four flight situations of the target track on the basis of three models above, aiming to obtain radar measured data. The necessity of coordinate transformation is verified by quantitative analysis of long-distance effects. A large number of track recognition tests are performed to analyze the influence of track fluctuations on the recognition rate. Experiments show that, when the track initial position and initial velocity fluctuations are in a certain area, this paper can achieve a high target recognition rate, which verifies the correctness and effectiveness of the proposed method.
机译:航迹是雷达空中目标分类和识别的重要基础。本文首先推导了飞机运动模型的方程,包括水平直线度,水平弧度和垂直弧度。然后,通过将固定在地球上的坐标系作为桥梁,使用坐标变换获得雷达目标参数,即距离,方位角和俯仰角。补偿了长距离条件下地球曲率的影响。进一步设计了基于径向基函数神经网络的轨迹识别方法。最后,在以上三种模型的基础上,建立了目标航迹的四种飞行情况,旨在获得雷达实测数据。通过对长距离效应的定量分析,验证了坐标变换的必要性。进行了大量的轨道识别测试,以分析轨道波动对识别率的影响。实验表明,当轨迹的初始位置和初始速度波动在一定范围内时,可以达到较高的目标识别率,验证了该方法的正确性和有效性。

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