基于时间序列分析的弹道目标进动周期提取

Precession period extraction of ballistic targets based on time series analysis

利用微波暗室静态测量数据,反演了某型弹道目标在典型战情下的动态雷达散射截面积(RCS)时间序列。分析表明:该序列具有非平稳性、相关性和拟周期性的特点,直接利用谱分析方法提取进动周期的效果较差。采用B样条函数、广义自回归条件异方差(GARCH)模型和自回归—滑动平均模型(ARMA模型)构造了RCS序列的迭合滤波分解模型。其中B样条函数用来表征趋势项,GARCH模型表征波动聚集项,ARMA模型表征受噪声污染的平稳周期项。证明了周期项等价于一个参数缓变的ARMA序列,进而可以利用常规功率谱估计方法对进动周期进行精密估计。仿真表明:所提方法能够有效提取弹道目标的进动周期。

Based on static measurement data of microwave anechoic chamber, this paper constructs the dynamic radar cross section（RCS） time series for a ballistic target in typical scenarios. The simulation indicates that the time series have the characteristics of nonstationarity, correlativity and quasi-periodicity. The effect of direct power spectrum estimation of precession period is not significant. This paper uses B-spline function, generalized autoregressive conditional heteroskedasticity （GARCH）model and autoregressive-moving average（ARMA）model to constitute a cascaded filtering model, in which the B-spline function represents the tendency term,GARCH model represents the volatility clustering term, and ARMA model represents the noise corrupted sinusoidal term. We demonstrate theoretically that the sinusoidal term is equivalent to an ARMA sequence with slowly varying parameters. The results show that the method can effectively extract the precession period of the ballistic target.