A constraint approach for UWB and PDR fusion

Abstract

Pedestrian Dead-Reckoning (PDR) and Radio Fre quency (RF) ranging/positioning are complementary techniques for position estimation but they usually locate different points in the body (RF in the head/hand and PDR in the foot). We propose to fuse the information from both navigation points using a constraint filter with an upper bound in the distance between the estimated positions of both sensors.For a pedestrian with an IMU for PDR in the foot and a RF positioning system in the head, the simplest bound is a maximal distance of 2 m between the positions of the sensors, this establish a spherical limit to the difference in the positions. It is also possible to use a smaller, non-symmetrical bound that establishes an ellipsoid as the limit and improves the fusion. We propose the use of a grid of particles to approximate the mean and covariance of the states.We have tested the algorithm by processing data from an Ultra Wide Band (UWB) positioning sensor attached to an IMU, both placed on the helmet of a person, and a foot-mounted IMU. Our results show that the system is able to estimate the position of a person with a limited error growth for the dead reckoning system and a better position estimate between position updates for the UWB system.

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

“…However, these high-end IMUs are too bulky and expensive for regular pedestrian use, but the results indicate that high accuracy could also be achieved in future mass-market systems when small MEMS IMUs have improved in accuracy. Zampella et al [24] proposed a new indoor PNS prototype which fuses the navigation results of a UWB-based system and a foot INS/ZUPT system through a probability density function (PDF) truncation approach, which requires a lot of calculations to approximate the truncated PDF mean and covariance. Also, this method does not consider the UWB non-line-of-sight conditions, namely when there exists the UWB signal outages.…”

“…Similarly, compared to the method given by Zampella et al [24], their constraint filter adopts a probability density function (PDF) truncation approach, which requires a lot of calculations to approximate the truncated PDF mean and covariance. To be specific, their constraint filter uses a particle filter way to calculate the propagation of the mean and covariance of the propagated states of the constraint filter.…”

“…To be specific, their constraint filter uses a particle filter way to calculate the propagation of the mean and covariance of the propagated states of the constraint filter. Moreover, in the conclusion part of [24], the authors also address that it requires many calculation burdens to implement their constraint filter.…”

Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

“…One is based on the various wireless network technologies, such as WiFi (wireless fidelity) [1,2], RFID (radio frequency identification) [3], and UWB (ultra-wideband) [4,5], which can be used to realize indoor positioning according to the intensity of received signals, the TOA (time of arrival), or TDOA (time difference of arrival). Among all of these technologies, UWB technology can achieve a decimeter-level positioning precision.…”

Particle filter for context sensitive indoor pedestrian navigation

“…The advantage with BLE is the easier control of transmission power, advertising rate and deployability. Ultra-wideband timing based ranging accuracy is approximately 30cm [5]. This depends on the environment, the amount of people and other obstacles.…”