在互相关性未知的分布式融合系统中,协方差交集算法是一种有效的融合算法,但其在融合高维航迹时存在计算量大、精度低的问题,为此对高维航迹进行了降维处理,把高维航迹的融合变为多组二维航迹的融合,从而得到了一种降维的协方差交集算法(Dimensionality Reduction Intersection Algorithm,DRCI).理论分析表明该算法能有效降低运算量,仿真实验结果表明,该算法的精度高于协方差交集算法(Covariance Intersection,CI),与Kalman融合算法处于同一水平.%In distributed fusion systems with unknown cross-correlation,the covariance intersection (CI) algorithm is an effective fusion algorithm,but there is a problem of high computational complexity and lower accuracy in the fusion of high-dimensional tracking state vectors.A method of reducing the dimension of the high-dimensional state vectors is utilized to transform the fusion of high-dimensional state vectors to a set of two-dimensional state vectors fusion.Thereby,a dimensionality reduction covariance intersection (DRCI) algorithm is obtained.Theoretical analysis shows that the algorithm can effectively reduce the amount of computation.Simulation results show that the accuracy of the algorithm is more precise than the covariance intersection (CI) algorithm and almost at the same level with Kalman fusion algorithm.
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