针对利用雷达测量数据进行炮位和落点预测时“先跟踪,后预测”缺乏快速性的问题,提出一种以雷达采样点数为分段参量将抛物线近似、最小二乘拟合、非线性滤波等算法进行综合的炮位预测分段算法和落点预测加权算法。以某120mm迫弹为例进行仿真,结果表明,炮位预测分段算法能够在雷达获取两组测量数据时开始预测;落点预测加权算法在雷达获取首个测量数据时即进行预测,且在雷达采样数据较少时具有比非线性滤波算法更高的落点预测精度。

Aming at the problem that "First tracking, then prediction" lacked rapidity when using radar measured data to predict artillery location and point of fall, the artillery location prediction segmentation algorithm and point of fall prediction weighted algorithm was proposed, which used radar sampling points as piecewise parameters, and synthesized parabola approximation, lease square fitting, nonlinear filtering algorithms. A 120 mm shell was as an example for simulation, the result indicated that the artillery location prediction segmentation algorithm could start predicting after radar got two groups of measured data. The point of fall prediction weighted algorithm started predicting when the first measured data was got, meanwhile, the approach could acquire more accurate results when the sampled data was quite few compared with the nonlinear filtering algorithm.