A real-time correlation algorithm for GEO targets based on radar ranging and velocity measurement
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摘要:
针对航迹密集情况下地球同步轨道（GEO）目标容易关联错误的问题，提出了一种基于雷达测距和测速二维判决的GEO目标实时关联算法。利用空间目标两行轨道根数（TLE）建立待关联初始库属目标集；根据空间目标轨道预报误差扩散规律设置粗关联门限，得到二次关联库属目标集；利用雷达测距和测速精度高的特点构建二次关联代价函数，根据归一化加权均方根误差最小原则得到关联结果。仿真结果表明：该算法在目标航迹密集的情况下取得了较好的关联效果，具有较高的关联正确率。
Abstract:In order to address the issue of error correction of the geosynchronous orbit (GEO) targets under the dense track, a real-time correlation algorithm for the GEO targets based on the two-dimensional of radar range and velocity measurement was developed. Firstly, the initial target set is established by using two lines elements (TLE). Then, the rough-correlation threshold is set according to the diffusion law of orbit prediction error of the space targets, and the secondary correlation target set is obtained. Finally, the correlation result is obtained using the idea of the minimal normalized weighted root mean square error, and the secondary correlation cost function is built based on the characteristics of high-accuracy radar range and velocity measurement. The simulation results show that the algorithm achieves a better correlation effect under the condition of dense target tracks, and has a higher correlation accuracy.
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Key words:
- radar /
- two-dimensional judgment /
- dense track /
- geosynchronous orbit /
- real-time correlation
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表 1 跟踪雷达参数信息
Table 1. Tracking-radar parameter information
工作
频段信号带宽/
MHz天线波束
宽度/(°)脉冲重复
间隔/ms脉冲宽度/
msS 5 0.15 5~20 1~4 
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表 2 测量信息中添加的高斯噪声（标准差）
Table 2. Gaussian noise (standard deviation) added to measurement information
距离维/m 方位维/mrad 俯仰维/mrad 速度维/(m·s−1) 10 0.2 0.2 0.01
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表 3 TLE目标集中目标更新周期分布
Table 3. Update period distribution of targets in TLE
更新周期/d 目标数量/个 数量占比/% 1 1576 96.75 2 31 1.90 3 5 0.31 4 7 0.43 其他 10 0.61 注：其他为更新周期大于等于5 d的目标个数。
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表 4 观测目标1二次关联情况
Table 4. Secondary correlation of observation target No.1
真实目标 二次关联库属目标 距离RMSE/km 速度RMSE/(m·s−1) 代价函
数值关联
结果37776 32019 202.359 0.249 36.481 37776 37207 235.142 0.402 42.483 37677 131.702 1.490 24.832 37776 0.870 0.002 0.158 41903 4.542 0.082 0.881 42662 8.661 3.126 4.118 42951 0.695 0.346 0.409 45807 624.462 258.579 324.170 46112 3.099 0.348 0.841
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表 5 观测目标4二次关联情况
Table 5. Secondary correlation of observation target No.4
真实目标 二次关联库属目标 距离RMSE/km 速度RMSE/(m·s−1) 代价函数值 关联结果 36744 29272 18.4363 0.0661 0.0831 36744 36744 0.0098 2.7727×10−4 0.0003 41034 84.4975 0.0101 0.0882 41729 54.0964 2.3420×10−4 0.0503 43432 3.3355 0.0155 0.0185 43823 74.8245 1.024×10−4 0.0693 45246 0.6103 0.1260 0.1265
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表 6 目标关联实验结果
Table 6. Results of target correlation experiments
序号 样本总数/条 关联成功概率/% 基于协方差理论的关联算法 基于距离辅助的关联算法 本文算法 1 432 90.51 96.33 99.54 2 425 88.94 95.69 99.04 3 453 90.28 96.07 99.13 4 435 90.34 94.63 99.02 5 437 89.70 97.02 99.08 6 458 91.70 97.54 99.11 7 419 89.50 96.06 99.12
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