自适应平方根无迹粒子滤波算法及其应用

Adaptive Square Root Unscented Particle Filter Algorithm and Its Application

目标跟踪所面对的动态定位观测方程具有非线性,随机模型具有未知性,目标在运动过程中受到的随机扰动较大,先验方差很难确定,这可能导致在更新迭代过程中参数估计产生错误,从而导致滤波发散。针对上述问题,本文提出了改进的自适应平方根无迹粒子滤波算法(ASRUPF),该算法融合了自适应滤波理论、平方根无迹卡尔曼滤波算法(SRUKF)和粒子滤波(PF)多种算法,确定系统量测和状态噪声的概率密度函数,确保其方差阵的非负定性。算法有效地提高了单点动态定位精度。

Target tracking in dynamic positioning observation equations is nonlinear and stochastic model with uncertainty,and goals in the process of movement by the random disturbance is larger.It is difficult to determine a priori variance,which may lead to the update parameter estimation errors in iterative process,which can lead to filter divergence.According to the above problem,this paper presents an improved adaptive square root unscented particle filter algorithm(ASRUPF).This algorithm combines the adaptive fihering theory,square root unscented kalman filter (SRUKF)and particle filter (PF),which determine the system measurement and the probability density function of state noise,to ensure variance matrix is non-negative qualitative.The algorithm effectively improves the single point dynamic positioning accuracy.