Multi-UAV Cooperative Navigation Method Based on Fusion of GNSS/INS/VNS Positioning Information

doi:10.12382/bgxb.2023.0860

Abstract

Abstract:

In the informationized battlefield, the unmanned aerial vehicles (UAVs) face various potential threats, including intermittent unintentional interference that disrupts the satellite signals and communication links of unmanned aircraft systems (UAS), leading to adverse effects on flight. To address this challenge, the multi-sensor data is utilized, and a joint filter is established usingthe global navigation satellite system (GNSS) and inertial navigation system (INS) combination navigation system as the main filter, and the global positioning system (GPS) and visual navigation system (VNS) as sub-filters. This approach fuses the relative navigation information from multiple UAVs’ digital image maps with the absolute navigation information acquired by each UAV platform, creating a Kalman filter-based multi-landmark relay-assisted navigation algorithm. This effectively enhances the solution accuracy of GNSS/INS relative navigation systems, reduces the computational burden on multi-UAVs, and expands the cruising range of UAVs. Additionally, a parallel distributed system framework is used to deploy the algorithm on multiple UAV platforms and facilitatethe information sharing and interaction among UAVs, thereby achieving the collaborative perception and autonomous positioning of multi-UAVs. The experiments conducted in simulated mission scenarios demonstrate that this approach meets the precision requirements, achieving an average positional estimation error of 0.66 mand maintaining the velocity estimation accuracy within ±0.4m/s in the collaborative navigation of three UAVs.

Key words: unmanned aerial vehicles, cooperative navigation, Kalman filter algorithm, GNSS/INS integration, visual navigation

CLC Number:

CAO Zhengyang, ZHANG Bing, BAI Yixuan, GOU Kenan. Multi-UAV Cooperative Navigation Method Based on Fusion of GNSS/INS/VNS Positioning Information[J]. Acta Armamentarii, 2023, 44(S2): 157-166.

Figures/Tables 16

Fig.1 Schematic diagram of multi-UAV cooperative navigation system

Fig.2 Schematic diagram of collaborative navigation framework

Fig.3 Pseudorange differential positioning diagram

Fig.4 Schematic diagram of information exchange between nodes

Table 1 Navigation devices parameters

陀螺仪	零漂:0.1(°)/h	随机游走:0.01(°)/√h
加速度计	零漂:100μg	随机游走:5μg/√Hz
GPS	伪距误差:3m	伪距率误差:0.03m/s
光电传感器	稳定精度:5μrad	精度误差:0.35mm/pixel

Fig.5 Comparison of speed estimation errors of Kalman filtering

Fig.6 GNSS/INS/VNS position estimation error Testing

Fig.7 GPS position estimation error testing

Table 2 Positioning error comparison

定位方式	平均误差/m
GPS	0.66
GNSS/INS/VNS组合	0.25

Fig.8 Velocity estimation error Testing

Fig.9 Schematic diagram of multi-UAVcollaborative navigation flight testing

Fig.10 Robustness comparison test chart of collaborative navigation algorithms

Fig.11 Test charts of collaborative navigation pose drift characteristics

Table 3 UAV 1absolute position error

方向	最大偏差/m	平均偏差/m	中间误差/m
x	1.324	0.483	0.467
y	6.057	1.948	1.985
z	1.380	0.594	0.617

Table 4 UAV 2 absolute position error

方向	最大偏差/m	平均偏差/m	中间误差/m
x	1.544	0.563	0.559
y	3.249	1.873	1.867
z	1.651	0.682	0.674

Table 5 UAV 3 absolute position error

方向	最大偏差/m	平均偏差/m	中间误差/m
x	1.475	0.522	0.507
y	2.931	1.827	1.796
z	1.275	0.537	0.512

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