Abstract
In multipath assisted positioning, multipath components arriving at a receiver are regarded as being transmitted by a virtual transmitter in a line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks. Hence, multipath assisted positioning enables localizing a user with only one physical transmitter depending on the scenario. In this paper, we present and derive a novel filtering approach for our multipath assisted positioning algorithm called Channel-SLAM. Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters. Since data association, that is, finding correspondences among landmarks, is essential for robust long-term SLAM, we also derive a data association scheme. We evaluate our filtering approach for multipath assisted positioning by simulations in an urban scenario and by outdoor measurements.
Highlights
The amount of available and potential services requiring precise localization of a user has steadily increased over the recent years
As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks
Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters
Summary
The amount of available and potential services requiring precise localization of a user has steadily increased over the recent years. The locations of the physical and virtual transmitters are estimated simultaneously with the user position in a simultaneous localization and mapping (SLAM) [15, 16] approach. The current Channel-SLAM algorithm uses a RaoBlackwellized particle filter to estimate the user state and the location of transmitters simultaneously. This paper is an extension of [32], where we proposed a novel estimation approach for Channel-SLAM scheme based on Rao-Blackwellization and performed first simulations. We refer to this new estimation method as Rao-Blackwellized Gaussian sum particle filter (RBGSPF). (vi) c0 denotes the speed of light. (vii) ‖ ⋅ ‖ denotes the Euclidean norm of a vector
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