| Paper: | SPTM-P11.10 | ||
| Session: | Nonlinear Systems and Signal Processing | ||
| Time: | Friday, May 19, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Nonlinear Systems and Signal Processing | ||
| Title: | Maneuvering Target Tracking Using the Nonlinear Non-Gaussian Kalman Filter | ||
| Authors: | Igal Bilik, Joseph Tabrikian, Ben-Gurion University, Israel | ||
| Abstract: | The problem of maneuvering target tracking is addressed in this paper. The main challenge in maneuvering target tracking stems from the nonlinearity and non-Gaussianity of the problem. The Singer model was used to model the maneuvering target dynamics and abrupt changes in the acceleration. According to this model, the heavy-tailed Cauchy distribution driving noise is used to model the abrupt changes in the target acceleration. The nonlinear, non-Gaussian Kalman filter (NL-NGKF) was applied to this problem. The algorithm is based on the Gaussian mixture model for the posterior state vector. The NL-NGKF for this problem was tested using simulations, and it is shown that it outperforms both the particle filter and the extended Kalman filter. | ||
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