UAV Positioning Based on Multi-Sensor Fusion

Jing Peng,Ping Zhang,Jia Tan,Lanxiang Zheng

doi:10.1109/access.2020.2974285

Jing Peng, Ping Zhang + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.2974285

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 47	License type: CC BY 4.0

Affiliation: South China University of Technology

Abstract

Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability.

Highlights

Positioning is the core of UAV, which plays an important role in UAV navigation [1], autonomous control [2] and the execution of various tasks
Aiming at the above problems, this paper proposes a new multi-sensor fusion adaptive Kalman filter framework and applies it to the positioning system of UAV
The proposed adaptive Kalman filtering method based on multi-sliding window noise estimation can estimate the measurement noise of multiple sensors simultaneously

Summary

INTRODUCTION

Positioning is the core of UAV, which plays an important role in UAV navigation [1], autonomous control [2] and the execution of various tasks. Aiming at the above problems, this paper proposes a new multi-sensor fusion adaptive Kalman filter framework and applies it to the positioning system of UAV. Aiming at the problem that various sensor data needs time synchronization in data fusion, this paper proposes a filter update method based on timestamp sort, which effectively avoids the problem of multi-sensor time synchronization. The proposed adaptive Kalman filtering method based on multi-sliding window noise estimation can estimate the measurement noise of multiple sensors simultaneously. Different from traditional adaptive filtering, this paper can estimate the measurement noise of multiple sensors in one filter at the same time. Aiming at the multi-sliding window noise estimation, the adaptive Kalman filter cannot effectively deal with the filter divergence caused by time-varying noise.

RELATIVE WORK

DATA PREPROCESSING

TIME-VARYING NOISE PROCESSING

Findings

VIII. CONCLUSION