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针对雷达数据随机误差超差的问题,分析了非平稳时间序列自回归求和滑动平均(ARIMA)模型,并以雷达某次实测国际空间站数据的前4 000点数据建立ARIMA模型,设计了基于此模型的Kalman滤波器,利用所设计滤波器对雷达前4 000点数据和剩余数据分别进行了滤波处理,补偿后误差为原数据的13.7%和20.1%。结果表明:该方法能有效降低雷达测量数据随机误差,提高数据质量。
Aiming at the problem of random error tolerance of radar data, ARIMA model of non-stationary time series is analyzed. ARIMA model is established based on the data of the former 4,000 points of a certain measured space station. Based on this, Kalman filter of the model is used to filter the 4 000-point data and the remaining data of the radar respectively by using the designed filter, and the compensated error is 13.7% and 20.1% of the original data. The results show that this method can effectively reduce the random error of radar measurement data and improve the data quality.