Elevation-Dependent Stochastic Localization Algorithm for GNSS-based Passive Radar
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摘要: 针对GNSS外辐射源雷达定位时不同卫星对定位的误差贡献不同的问题，提出基于高度角随机模型的定位算法，理论分析目标位置估计量的克拉美罗界和统计特性，并计算卫星位置误差和地面站位置误差对定位误差的影响。仿真结果表明：所提出算法对不同GNSS卫星的直射、反射路径的伪距差测量值误差进行了合理分配，定位性能达到了克拉美罗界，且不会因为选星方案的改变而大幅恶化。对地面站位置误差和卫星位置误差的分析表明：地面站位置的标准差小于10 cm、卫星位置的标准差小于1 km时对定位总误差的贡献可以忽略不计。Abstract: An elevation-dependent stochastic localization algorithm is proposed to address the problem of different satellites contributing differently to the localization error in Global Navigation Satellite System (GNSS)-based passive radar. Herein, the Cramer-Rao Lower Bound (CRLB) and statistical characteristics of the target position estimator are theoretically analyzed, and the contributions of satellite position and ground-station position errors to passive localization error are calculated. Simulation results show that the proposed algorithm can reasonably distribute the error from the pseudo-range measurements of multiple GNSS satellites with different directions and reflective paths. The localization performance reaches the CRLB but will not deteriorate considerably due to a change in the star selection scheme. Analysis of the ground-station position and satellite position errors show that their contributions to the total positioning error can be ignored if the standard deviation of the ground-station position is less than 10 cm and that of the satellite position is less than 1 km.
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表 1 目标定位的仿真条件
编号 x(m) y(m) z(m) 高度角(°) 地面站 0 0 0 / 卫星1 2899814.62 18284781.58 12217268.81 60.91 卫星2 –17764738.34 15083494.90 27484289.18 71.13 卫星3 –10828315.49 20312609.37 –3037755.37 42.41 卫星4 –1449209.82 15222521.44 15431127.53 73.64 卫星5 –12017255.44 21647192.40 25857148.55 83.56 卫星6 –18426601.68 32494825.40 –3751263.00 44.14 卫星7 –14055816.53 –1406462.43 18392755.00 42.57 卫星8 –12585819.57 35077860.61 –4631158.40 42.44 卫星9 –2312759.23 22721092.71 28108988.86 71.93 卫星10 –32147920.16 20112722.76 –4037168.82 35.58 
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表 3 目标定位算法对选星方案的敏感性
卫星数量 选星方案的总数 RMSE序列的标准差(m) RMSE序列的极差(m) 高度角随机模型 等权随机模型 高度角随机模型 等权随机模型 5 252 13.48 134.20 103.38 1025.10 6 210 6.89 52.74 44.85 495.38 7 120 3.96 20.83 26.36 119.24 8 45 2.24 12.47 9.23 52.37
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