Table 1 From Maneuvering Target Tracking Algorithm Based On Interacting

Maneuvering Target Tracking Using Extended Kalman Filter 1991 Pdf Our tracking algorithm is based on the interacting multiple model kalman filter (imm kf) and generates a satisfactory target estimate in a time efficient manner. The improved unscented kalman filter algorithm is proposed to enhance filtering effect. the simulation and actual tracking experiment results indicate that the algorithm in maneuvering target tracking is valid.

Figure 2 From Maneuvering Target Tracking Algorithm Based On In this paper, along with reviewing and analyzing the maneuvering target tracking model, the multiple model interacting multiple model algorithm is used to solve the maneuvering target tracking problem in the presence of measurement noise. To enhance the tracking accuracy of multi ellipse extended target tracking for maneuvering targets, we propose a variational bayesian based interacting multiple model maneuvering extended target tracking algorithm. Three single scan probabilistic data association (pda) based algorithms for tracking manoeuvering targets in clutter, derived by integrating the interacting multiple model (imm) estimation algorithm with the pda approximation, are presented. The system simulation results show that this algorithm improved the tracking precision of system with the imm kf. there are currently no refbacks.

Figure 4 From Tracking A Maneuvering Target Using Interacting Multiple Three single scan probabilistic data association (pda) based algorithms for tracking manoeuvering targets in clutter, derived by integrating the interacting multiple model (imm) estimation algorithm with the pda approximation, are presented. The system simulation results show that this algorithm improved the tracking precision of system with the imm kf. there are currently no refbacks. Aiming at improving the accuracy and quick response of the filter in nonlinear maneuvering target tracking problems, the interacting multiple models cubature information filter (immcif) is proposed. An interacting multiple model method for tracking maneuvering targets is designed in this paper. in order to solve the nonlinear problems, the unscented kalman filter is used to update the state equation and measurement equation. In this paper, an adaptive interacting multiple model algorithm based on information weighted consensus (imam uicf) is proposed. this algorithm further improves the estimation accuracy of tracking maneuvering target on the basis of imam, and the consensus filter is the key to the improvement. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information weighted consensus filter for maneuvering target tracking is proposed.