Article
UAV Swarm Navigation Using Dynamic Adaptive Kalman
Filter and Network Navigation
Jingjuan Zhang, Wenxiang Zhou and Xueyun Wang *
Citation: Zhang, J.; Zhou, W.; Wang,
X. UAV Swarm Navigation Using
Dynamic Adaptive Kalman Filter and
Network Navigation. Sensors 2021, 21,
5374. https://doi.org/10.3390/
s21165374
Academic Editors: Kamil Krasuski
and Damian Wierzbicki
Received: 13 July 2021
Accepted: 6 August 2021
Published: 9 August 2021
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4.0/).
School of Instrument Science and Optoelectronics Engineering, Beihang University, XueYuan Road No. 37,
HaiDian District, Beijing 100191, China; zhangjingjuan@buaa.edu.cn (J.Z.); 17806286806@buaa.edu.cn (W.Z.)
* Correspondence: wangxueyun@buaa.edu.cn
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%.
Keywords:
adaptive Kalman filter; ensemble empirical mode decomposition; process noise; observa-
tion noise; networked navigation
1. Introduction
In recent years, the unmanned aerial vehicle (UAV) swarm has been given more and
more attention due to the tremendous application prospects. Compared with a single UAV,
the UAV swarm is more efficient, reliable, flexible and intensive [
1
,
2
]. As a prerequisite
for a UAV to complete various tasks, the navigation and positioning technology has been
extensively studied by scholars. At present, the most popular navigation system usually
consists of an inertial navigation system (INS) and a Global Navigation Satellite System
(GNSS) [
3
]. This system is very effective for improving navigation accuracy; however, its
disadvantage of a fragile robustness due to the easy loss or disturbance of the GNSS signal
limits its applications. When the GNSS signal is available, the Kalman filter (KF) is the
most popular INS/GNSS data fusion algorithm. This paper aims to improve the fusion
accuracy of the traditional KF and solve the problem of the UAV swarm navigation in the
case of GNSS outage.
The Kalman filter uses the next observation to update the estimates of the current state
variables, along with their uncertainties. KF corrects the noisy sensor data to a more realistic
sensor output (with less noise) [
4
]. However, the performance of KF is affected by the
stochastic model describing the process noise and observation noise and the dynamic model
describing the dynamics of the system over time [
5
]. A variety of adaptive Kalman filters
(AKF) has been proposed by scholars to improve the fusion precision of traditional Kalman
filters when the stochastic model is inaccurate [
6
]. The AKF based on the attenuation factor
reduces the influence of historical data on the current results by adjusting the weight of
the one-step prediction covariance matrix. In fact, the problem of the inaccurate noise
Sensors 2021, 21, 5374. https://doi.org/10.3390/s21165374 https://www.mdpi.com/journal/sensors
