The large misalignment angle error model of strapdown inertial navigation system (SINS) is a kind of nonlinear Gauss error model. The Gauss Hermite method can approach Gauss integral with any precision, and achieve high precision filtering effect. The number of Gauss Hermite integral points is exponentially related to the dimension of nonlinear system. In the process of real-time filtering, a large number of integral points of multidimensional system may lead to dimension disaster. According to the initial alignment error model of SINS with large misalignment angle, this paper presents a strong tracking reduced dimension sparse grid Gauss Hermite filtering method, which reduces the nonlinear integration dimension through separation of the linear and nonlinear terms, further reduces the number of Gauss hermit filtering integration points by sparse grid method, and ensures the stability of the system by strong tracking method.

A Large Misalignment Angle Initial Alignment Method of SINS Based on Strong Tracking Sparse Grid Gauss Hermite filter

Nonlinear System State Estimation Mechanism Based on Kalman Filter for Wireless Sensor Networks Localization

For nonlinear estimation with additive noise,we utilized the sparse-grid theory to propose a novel nonlinear filter,Sparse-grid Square-Root Quadrature Filter(SSRQF).The quadrature points for the conventional QF increases exponentially with the dimension.However,the new filter SSRQF uses the sparse-grid theory to reduce the number of quadrature points to a polynomial with the dimension,which alleviates the computation burden greatly.Through the theoretical analysis,we proposed that Unscented Kalman Filter(UKF) is only a special case for Sparse-grid Quadrature Filter(SQF),thus SSRQF is more flexible in terms of choosing points and controlling accuracy.The simulation results show that the SSRQF achieves higher accuracy than the UKF and the Extended Kalman Filter(EKF).Thus,it is a high accuracy nonlinear filter algorithm with computationally efficient.

Tian Fang-hao

Papers

Sparse-Grid Square-Root Quadrature Nonlinear Filter