Analysis of EKF, SR-UKF and Particle filter for ToF/AoA Local Navigation System and IMU Measurements

Today there is a growing need for solutions which allow to determine location of consumers indoors, such as tracking the location of workers and equipment in workshops/warehouses. Due to the fact that global navigation satellite systems (GNSS), still being the standard for outdoor navigation, are no...

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Bibliographic Details
Main Authors: Petukhov, Nikita I., Kochka, Kirill V., Evseev, Artyom D., Brovko, Tatiana. A., Orobchenko, Stepan V., Korogodin, I. V.
Format: Conference Proceeding
Language:English
Subjects:
AoA
Atmospheric measurements
EKF
Global navigation satellite system
integrated system
local navigation system
Measurement uncertainty
particle filter
Particle filters
Particle measurements
PDR
Power measurement
Radio navigation
ToF
UKF
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Summary:Today there is a growing need for solutions which allow to determine location of consumers indoors, such as tracking the location of workers and equipment in workshops/warehouses. Due to the fact that global navigation satellite systems (GNSS), still being the standard for outdoor navigation, are not designed to operate in such conditions and can't provide the specified accuracy, research and development of indoor positioning systems are being actively conducted. As the most preferred option for solving such problems, one can single out integrated solutions based on UWB radio system and an inertial measuring unit. In this paper three types of filters were synthesized (Extended Kalman filter (EKF), Square-Root Unscented Kalman filter (SR-UKF) and Particle filter (PF)) and were tested in two simulation experiments (closed trajectory with maneuvers and trajectory with constant velocity and yaw angle) and for two observation vectors. All types of filters provide relatively equal results for the same type of measurement vector. An improvement in DRMS for observation vector with combined radio and PDR measurements was noticed. In simulation, in which user moves with constant yaw angle and velocity, the gain in precision of estimates of filter with combined observation vector comes to naught.
ISSN:2831-7262
DOI:10.1109/REEPE57272.2023.10086716