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- W2384167568 abstract "Gaussian Particle Filter (GPF) is asymptotically optimal for nonlinear system estimation problems. The advantage of GPF over the Particle Filter (PF) is that it does not need the re-sampling step and avoids the particle degeneracy phenomenon. A new current statistical model tracking algorithm is proposed, which is applied to maneuvering target tracking in non-linear and non-Gaussian system. In the new algorithm, replacing Kalman Filter with GPF in current statistical model adaptive tracking algorithm integrates the advantages of GPF with the ones of current statistical model. A simulation of a maneuvering target tracking model is presented. The simulation shows that the tracking accuracy of current statistical model adaptive tracking algorithm based on GPF is superior to that of standard interacting multiple model algorithm and current statistical model adaptive tracking algorithm, the tracking speed of which is better than that of Interacting Multiple Model Particle Filter algorithm." @default.
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- W2384167568 date "2007-01-01" @default.
- W2384167568 modified "2023-09-26" @default.
- W2384167568 title "Current statistical model tracking algorithm based on gaussian particle filtering" @default.
- W2384167568 hasPublicationYear "2007" @default.
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