Research On Fornasini-Marchesini? State Space Model Based On MIMO Radar

At present,the research on the Fornasini-Marchesini ? state space model focuses on the low-dimensional implementation and stability of the system,aiming to simplify the implementation steps of the solution system and reduce the dimension of the implementation matrix.For the new application research of the Fornasini-Marchesini ? state space model focused on radar target detection,applying the low-order implementation method of the Fornasini-Marchesini ? state space model to multi-input multi-output radar target detection will improve the performance of the vehicle-mounted radar system.The order reduction method adopted in this thesis is to use the feature vector and invariant subspace to solve the Fornasini-Marchesini ? state space model,that is: for a given Fornasini-Marchesini ? state space model,when using features When the vector and the invariant subspace find j that meets the condition,the given FornasiniMarchesini ? state space model can be reduced in order.This thesis presents a new method for solving the Fornasini-Marchesini ? state space model system implementation.This method can simplify the steps of solving the system implementation matrix,and can get a clearer and concise representation of the system,which is conducive to system analysis.In this thesis,combined with the practical application of multi-input and multi-output vehicle radar,the Fornasini-Marchesini ? state space model is used to model the multi-input and multi-output vehicle radar system.Real-time implementation of the vehicle-mounted radar system,and the accuracy of the model was verified by simulation.