This paper addresses the problem of simultaneously localizing multiple targets and estimating the positions of the sensors in a sensor network using particle filters. We develop a new technique called multitarget simultaneous localization and mapping (MSLAM) that has better performance than the well-known FastSLAM when there are several targets in the surveillance area. The proposed algorithm is based on the parallel partition particle filter, especially designed for multiple target tracking, and the truncated unscented Kalman filter for updating the sensors’ positions.
This paper addresses the problem of simultaneously localizing multiple targets and estimating the positions of the sensors in a sensor network using particle filters. We develop a new technique called multitarget simultaneous localization and mapping (MSLAM) that has better performance than the well-known FastSLAM when there are several targets in the surveillance area. The proposed algorithm is based on the parallel partition particle filter, especially designed for multiple target tracking, and the truncated unscented Kalman filter for updating the sensors’ positions. Read More
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