基于记忆融合模式的多无人机分散式协同导航方法

doi:10.7527/S1000-6893.2023.28440

Abstract

Abstract:

To address the communication delay problem during cooperative interaction of multiple Unmanned Aerial Vehicles （UAVs）， a memory-fusion based multi-UAV decentralized cooperative navigation algorithm is proposed. On the basis of the UAV inertial and Global Navigation Satellite System （GNSS） tightly combined navigation framework， the system state vector considering the effect of communication delay is established， and the system state error and its variance are derived. Then the optimal estimation model about the state of each UAV is constructed.， and the optimal state is estimated based on the criterion of minimum variance of the state error. A decentralized cooperative navigation algorithm for multiple UAVs is established based on inter-aircraft relative measurements， and a time delay processing method is proposed based on the memory fusion model. The results show that the proposed algorithm can effectively compensate for the loss of accuracy caused by communication delay and improve the positioning accuracy.

Key words: cooperative navigation, communication delay, ultra-wideband, Kalman filtering, GNSS rejection

CLC Number:

V249.31

Zebo ZHOU, Zeliang ZHANG, Xin PENG, Gun LI, Yang TAO, Liangquan WANG, Xin LUO. Multi-UAV decentralized cooperative navigation method based on memory-fusion[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(20): 628440-628440.

Figures/Tables 17

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

RMSE of UAV1 in GNSS denied case （simulation experiments）

算法	俯仰角/（°）	滚转角/（°）	偏航角/（°）	$v E / (m ⋅ s - 1)$	$v N / (m ⋅ s - 1)$	$v U / (m ⋅ s - 1)$	$λ / m$	$L / m$	$h / m$
NDCN	5.02	2.41	2.27	22.90	23.40	8.40	457.4	1 222	346.5
DEKF	0.56	0.86	1.38	5.76	6.61	6.87	188.4	179.1	263.9
MDCN	0.53	0.41	1.19	2.97	3.94	3.19	140.4	127.1	117.4

Table 1

Fig.6

Table 2

RMSE of UAV1 in GNSS-accessible case （simulation experiments）

算法	俯仰角/（°）	滚转角/（°）	偏航角/（°）	$v E / (m ⋅ s - 1)$	$v N / (m ⋅ s - 1)$	$v U / (m ⋅ s - 1)$	$λ / m$	$L / m$	$h / m$
NDCN	0.77	0.39	1.53	0.11	0.18	0.08	1.00	7.96	0.40
DEKF	0.39	0.35	1.09	0.10	0.10	0.07	0.33	0.38	0.37
MDCN	0.36	0.35	0.99	0.11	0.12	0.07	0.28	0.29	0.36

Table 2

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 3

RMSE for UAV1 in GNSS denied case （real flight tests）

算法	俯仰角/（°）	滚转角/（°）	偏航角/（°）	$v E / (m ⋅ s - 1)$	$v N / (m ⋅ s - 1)$	$v U / (m ⋅ s - 1)$	$λ / m$	$L / m$	$h / m$
NDCN	1.13	0.77	11.97	2.95	5.80	1.34	136.2	178.7	68.8
DEKF	1.08	0.93	9.54	1.49	3.53	2.23	78.3	85.4	109.7
MDCN	0.99	0.95	9.11	1.33	2.76	0.95	72.3	60.2	45.8

Table 3

Fig.12

Table 4

RMSE for UAV1 in GNSS-accessible case （real flight tests）

算法	俯仰角/（°）	滚转角/（°）	偏航角/（°）	$v E / (m ⋅ s - 1)$	$v N / (m ⋅ s - 1)$	$v U / (m ⋅ s - 1)$	$λ / m$	$L / m$	$h / m$
NDCN	0.40	0.38	2.10	0.055	0.049	0.060	0.68	0.59	0.46
DEKF	0.39	0.37	1.69	0.040	0.038	0.035	0.59	0.51	0.30
MDCN	0.39	0.34	1.48	0.027	0.025	0.024	0.51	0.43	0.28

Table 4

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Multi-UAV decentralized cooperative navigation method based on memory-fusion

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