Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Extended Kalman filter synthesis for integrated global positioning/inertial navigation systems
Applied Mathematics and Computation
Multi model tracking for localization in wireless capsule endoscopes
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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Tracking a capsule endoscope location is one of promising application offered by implant body area networks (BANs). In this paper, we pay attention to a particle filter algorithm with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem, which assumes a nonlinear transition model on the capsule endoscope location. However, the original particle filter requires to calculate the particle weight according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated by received wireless signal. Therefore, for the purpose of applying the particle filter to the capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrates that the proposed tracking methods with the modified particle filter can improve the performance as compared with not only the conventional maximum likelihood (ML) localization but also the original particle filter-based location tracking.