UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation.

Jingjuan Zhang,Xueyun Wang,Wenxiang Zhou

doi:10.3390/s21165374

Jingjuan Zhang, Xueyun Wang + Show 1 more

Open Access

https://doi.org/10.3390/s21165374

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Journal: Sensors (Basel, Switzerland)	Publication Date: Aug 9, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: Beihang University

Abstract

Aiming to improve the positioning accuracy of an unmanned aerial vehicle (UAV) swarm under different scenarios, a two-case navigation scheme is proposed and simulated. First, when the Global Navigation Satellite System (GNSS) is available, the inertial navigation system (INS)/GNSS-integrated system based on the Kalman Filter (KF) plays a key role for each UAV in accurate navigation. Considering that Kalman filter’s process noise covariance matrix Q and observation noise covariance matrix R affect the navigation accuracy, this paper proposes a dynamic adaptive Kalman filter (DAKF) which introduces ensemble empirical mode decomposition (EEMD) to determine R and adjust Q adaptively, avoiding the degradation and divergence caused by an unknown or inaccurate noise model. Second, a network navigation algorithm (NNA) is employed when GNSS outages happen and the INS/GNSS-integrated system is not available. Distance information among all UAVs in the swarm is adopted to compensate the INS position errors. Finally, simulations are conducted to validate the effectiveness of the proposed method, results showing that DAKF improves the positioning accuracy of a single UAV by 30–50%, and NNA increases the positioning accuracy of a swarm by 93%.

Highlights

In recent years, the unmanned aerial vehicle (UAV) swarm has been given more and more attention due to the tremendous application prospects
The most popular navigation system usually consists of an inertial navigation system (INS) and a Global Navigation Satellite System (GNSS) [3]
Huang invented a signal processing algorithm called empirical mode (EMD) that can decompose a nonlinear signal into a series of near-orthogonal intrinsic mode functions (IMFs) [23]

Summary

Introduction

The unmanned aerial vehicle (UAV) swarm has been given more and more attention due to the tremendous application prospects. Abdolkarimi et al [17] introduced a neural network based on an extreme learning machine to compensate for INS errors during a GNSS outage, and improve the signal-to-noise ratio of the sensor measurements through wavelet transform. The main contents are as follows: First, aiming at the situation that R and Q will affect each other when the adaptive Kalman filter works [10], the dynamic adaptive Kalman filter (DAKF) based on ensemble empirical mode decomposition (EEMD) is designed This method applies EEMD to the noise extraction of multi-channel GNSS signals and estimates the noise variance to dynamically adjust the observation noise covariance matrix R. Compared with the neural network-based algorithm mentioned in [14,15,16,17,18], this paper introduces the NNA to compensate the divergent position error of the UAV swarm during a GNSS outage.

Dynamic Adaptive Kalman Filter

Ensemble Empirical Mode Decomposition

Findings

Acquisition of Observation Noise Covariance Matrix R