Nonlinear Target Tracking adaptive Algorithm Based on residual Takagi-Sugeno fuzzy

In order to solve the problem that The estimation error of the standard nonlinear Kalman algorithm is caused by the inaccuracy of the system filter initial value and the unknown noise statistic characteristic in target tracking, a nonlinear target tracking algorithm based on residual Takagi-Sugeno fuzzy adaptive is proposed. Under the framework of determining sampled filtering, the general form of approximate Gaussian weighted integral under the condition of linearization error constraint is given. The sufficient condition for the bounded convergence of the proposed algorithm is proved by the Lyapunov second method and the Takagi-Sugeno model is introduced to constructs the noise estimator combined with the method of residual matching fuzzy criterion. It is verified that the algorithm has better tracking accuracy and robustness through unknown initial value information and target tracking model of time-varying noise measurement.