This paper describes the tracking filter algorithm of the Multiple targets tracking(MTT) for ground moving targets.

Identification of non-linear system parameters based on strong tracking filter;

Vehicle nonlinear state estimation based on strong tracking filter

This dissertation puts forward a method of designing the suboptimal strong tracking filter, which is based on the approximation error upper bound of nonlinear separation model and applied to design feedback corrected controller in predictive control.

In this paper,the theory of Strong tracking filtering(STF) is applied in the object prediction of autonomous robots to avoid the disadvantages of other methods by introducing fading factors.

This research paper employs the Kalman filtering and the strong tracking filtering in the optimal estimation theory to dynamically estimate Chinese listed companies permanent earnings,which are critical for stock market investment decision.

According to the model assumption,the parameters of the fault trend process can be obtained by using STF(strong tracing filter).

Therefore,it is proposed to use Strong Tracking Filter(STF) in maneuvering target tracking.

Comparing the performance of Kalman with STF,it is showed this method is with high accuracy,high anti-jamming and strong tracking ability.

Real-time application of particle filter tracking algorithm is limited due to their inherent complex and intensive computation.