This letter investigates the problem of simultaneous state and unknown input estimation in a nonlinear system within multi-sensor networks. To avoid the linearization errors caused by existing methods, such as statist...
详细信息
This letter investigates the problem of simultaneous state and unknown input estimation in a nonlinear system within multi-sensor networks. To avoid the linearization errors caused by existing methods, such as statistical linear regression techniques and first-order Taylor approximation, a novel optimal distributed nonlinear filter is proposed based on the unscented transformation and unbiased minimum variance criterion. First, the unscented transformation is applied to generate predicted estimates and the covariance matrix. Second, input and state estimation are carried out in an unbiased minimum variance manner using measurements from the nonlinear system. Third, a distributed strategy leveraging average consensus is employed to incorporate local estimates from neighboring sensors. Finally, simulation results confirm that the proposed method achieves substantial improvements in estimation accuracy compared to existing methods.
This paper deals with the issue of state estimation for nonlinear non-Gaussian networks suffering from deception attacks. A novel distributed maximum correntropy filter with variance correction is proposed, which cons...
详细信息
This paper deals with the issue of state estimation for nonlinear non-Gaussian networks suffering from deception attacks. A novel distributed maximum correntropy filter with variance correction is proposed, which consists of local estimation and diffusion fusion. Firstly, the estimates from neighbor nodes are fused to obtain a common prediction estimate, as well as the upper bounds of the corresponding covariances are derived to avoid calculating correlated information. The chi-square test is employed to rectify the inaccurate noise variance caused by non-Gaussian noise and deception attacks. Based on minimum error and maximum correntropy criteria, distinct consensus gains and filtering gain for the local filter are derived. Then, local estimates of neighbor nodes are fused based on covariance intersection to enhance the accuracy of the node itself. The designed distributed structure combines the merits of consensus and diffusion filters, which can further enhance the estimation performance of the whole system. The statistical linearization and the cubature rule are applied to deal with nonlinear functions, avoiding Jacobi matrices and additional uncertain parameters. In addition, it is proved that the filtering covariance is bounded under certain conditions. Finally, simulation results verify the effectiveness and accuracy of the presented algorithm.
暂无评论