本项目以大规模被动传感器系统中大规模、非线性、非高斯观测数据的处理需求为基本背景，探索以并行高精度为目标的并行粒子滤波理论和算法，使之基本满足大规模被动传感器系统中非线性滤波理论和应用的需要。 针对大规模大间隔观测数据的滤波处理问题，研究目标特性与并行结构模型的结合，建立融合目标特性的大规模被动传感器系统的并行粒子滤波理论；结合Gauss-Hermite积分规则和模糊数学原理，研究非周期大间隔情况下的重要性密度函数的构建、免重采样的非线性近似、先验不足条件下粒子权值的计算等问题，提出基于Gauss-Hermite积分的免重采样并行模糊积分粒子滤波算法；为解决非线性非高斯观测数据的滤波处理问题，研究利用高斯和方法近似非高斯噪声，提出适用于非线性非高斯环境的并行模糊高斯和粒子滤波算法。研究成果不仅可以解决大规模被动传感器系统的非线性滤波问题，而且可以推广到其它非线性滤波领域和目标跟踪领域。

To the large-scale, nonlinear and non-Gaussian observation data processing need in large-scale passive sensor system, this project will explore the high-precision parallel particle filter theory and algorithms, and make its basically meet the need of the nonlinear filtering theory and application for large-scale passive sensor system. To the problem of filter processing for large-scale large-interval observation data, the project will study the combination of the characteristics of the target with parallel structure model, and the parallel particle filter theory for large-scale passive sensor system will be established by integrating the target characteristics. Combined Gauss-Hermite quadrature rules with fuzzy mathematics principle, the project will research the choice of importance density function in aperiodic large-interval environment, nonlinear approximation, the weight calculation of particle on condition of priori lack, and will propose parallel fuzzy quadrature particle filter algorithm based on the Gauss-Hermite quadrature. To solve the filter problem of non-linear non-Gaussian observation data, the project will research the Gaussian sum approach that can be used to approximate non-Gaussian noise, and will propose parallel fuzzy Gaussian sum particle filter algorithm for non-linear non-Gaussian environment. The research results can not only solve the nonlinear filtering of large-scale passive sensor system, and can be extended to other field of nonlinear filtering and target tracking.