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 considerable radar data random error, the autoregressive integrated moving average (ARIMA) model is analyzed by time-series analysis method. The ARIMA model and Kalman filter are designed based on the foregoing 4 000 data points to smooth the radar data, which is obtained by measuring the International Space Station. The foregoing 4 000 data points and other points are processed by the filter respectively, and random error reduces to 13.7% and 20.1% of the original data separately. The results show that the random error of radar measurements can be reduced effectively by the new method, improving the data quality.

TP971.1