Robust State Estimation for Network Packet Dropout with Quadratic Programming

Abstract:

The paper presents a new discrete time linear time invariant state space model which considers the state estimation with the network packet dropout. Based on this model, the robust state estimation problem is transformed into a vector optimization problem. To solve this problem fast and effectively, the vector optimization problem is transformed into a scalar quadratic programming problem by the scalarization method. And with the further work, the initial problem can finally be transformed to solve a l1 regularized least squares problem, which usually has a standard and fast solution. Associating with the Kalman filter updating procedure, the new algorithm which can be adapted to the condition with the network packet dropout is proposed. The simulation results show that the proposed algorithm is effective.