基于高斯随机矢量统计特性，推导出一种适用于探测距离较远、系统误差时变、雷达上报目标不一致等复杂环境的空基多雷达航迹抗差关联算法。分解航迹距离矢量，对消系统误差矢量得出适用于3个及3个以上雷达的航迹抗差关联条件和流程。分别设置了目标密集程度、随机误差和系统误差适应性实验验证算法性能。仿真结果表明所提算法的关联准确性和复杂环境适应性相比现有的基于参照拓扑特征的航迹关联算法（RET算法）和基于距离检测的可信关联算法（confidential算法）有较大幅度的提升。
An air-platform multi-radar anti-bias tracks association algorithm is proposed based on the statistical feature of the Gaussian random vector. The algorithm is adaptive to complicated environment such as far detection range, time-varied measurement biases, and different targets reported by different radars. The anti-bias tracks association conditions for three or more radars and the multi-radar anti-bias tracks association flow are proposed based on the difference between bias vectors. Adaptability experiments are established based on three factors including the targets density, random error and sensor bias to validate the performance of the algorithm. Monte Carlo simulations demonstrate that the proposed algorithm can significantly improve the association accuracy and the adaptability to complicated environment compared with the algorithm based on the reference topology feature (RET algorithm) and the confidential algorithm based on bias detection (confidential algorithm).
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