In order to resolve the state estimation problem of nonlinear/non-Gaussian systems,a new kind of quadrature Kalman particle filter(QKPF) is proposed.In this new algorithm,quadrature Kalman filter(QKF) is used for generating the importance density function.It linearizes the nonlinear functions using statistical linear regression method through a set of GaussianHermite quadrature points.It need not compute the Jacobian matrix and is easy to be implemented.Moreover,the importantce density function integrates the latest measurements into system state transition density,so the approximation to the system posterior density is improved.The theoretical analysis and experimental results show that,compared with the unscented partcle filter(UPF) ,the estimation accuracy of the new particle filter is improved almost by 18%,and its calculation cost is decreased a little.So,QKPF is an effective nonlinear filtering algorithm. In order to resolve the state estimation problem of nonlinear / non-Gaussian systems, a new kind of quadrature Kalman particle filter (QKPF) is proposed. In this new algorithm, quadrature Kalman filter (QKF) is used for generating the importance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian Hermite quadrature points. It requires not compute the Jacobian matrix and is easy to be implemented. Moreover, the important measurement of density function integrates the latest measurements into system state transition density, so the approximation to the system posterior density is improved. The theoretical analysis and experimental results show that, compared with the unscented partcle filter (UPF), the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little.So, QKPF is an effective nonlinear filtering algorithm.