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为了解决光电经纬仪由于机动目标运动模型不准确而引起的跟踪精度下降的问题,采用了单隐层前向神经网络(SLFNs)进行建模,提出了基于状态参数双重扩展卡尔曼滤波估计的共轴跟踪控制技术。仿真与实验结果显示,对83.33°sin0.6t的等效正弦目标的速度估计最大误差为0.070 9(°)/s,跟踪精度为2.42′;对旋转周期为4.5 s的光学动态靶标的跟踪精度达到2.96′以内。由此可见,所建立的模型与机动目标实际模型匹配,双重扩展卡尔曼滤波器(DEKF)能快速跟踪和估计状态参数。与传统控制方法相比,提出的方法具有更高的跟踪能力,能有效提高系统的跟踪精度。
In order to solve the problem that the theodolite the tracking accuracy decreases due to inaccurate maneuvering target motion model, a single hidden layer forward neural network (SLFNs) is used to model the co-axial Tracking control technology. The simulation and experimental results show that the maximum error of the velocity estimation of the equivalent sinusoid at 83.33 ° sin0.6t is 0.070 9 (°) / s, the tracking accuracy is 2.42 ’, and the tracking accuracy of the optical dynamic target with the rotation period of 4.5 s Reached 2.96 ’or less. It can be seen that the established model matches the actual model of maneuvering target, and double extended Kalman filter (DEKF) can quickly track and estimate the state parameters. Compared with the traditional control methods, the proposed method has a higher tracking ability, which can effectively improve the tracking accuracy of the system.