基于QMC采样的GMPHD分布式融合方法

针对高斯混合概率假设密度分布式融合过程中高斯分量数随时间急剧增长的问题,给出了一种适用于融合过程不同阶段的两级分量混合约简算法,最大程度地减少了信息的损失。针对高斯混合概率假设密度协方差交叉融合算法中高斯混合模型求幂运算后不再服从高斯混合分布的问题,提出了一种基于拟蒙特卡罗采样的等价求解方法。仿真实验表明,所提的改进算法在保证融合计算有效性和可行性的同时提高了融合精度。

Distributed fusion of Gaussian mixture probability hypothesis density based on quasi-Monte Carlo samping

Aiming at the problem that the Gaussian component number increases rapidly with time in the density distribution fusion process, a two level component hybrid reduction algorithm is proposed for the different stages of the fusion process, which minimizes the loss of information. An equivalent method based on quasi Monte Carlo sampling is proposed to solve the problem that the Gaussian mixture model is no longer subject to Gaussian mixture distribution throughout the operation of Gaussian mixture exponentiation. The simulation results show that the proposed algorithm improves the fusion accuracy while ensuring the validity and feasibility of the fusion computation.