基于改进粒子滤波的无人机编队协同导航算法

doi:10.7527/S1000-6893.2022.27995

Abstract

Abstract:

To address the problem of poor cooperative navigation of master-slave UAV swarms in complex environments with time-varying non-Gaussian noise due to external interference， an improved Particle Filter （PF） algorithm is proposed to improve navigation accuracy and reduce the accuracy requirements of the slave measurement equipment. Firstly， the observation model is established by taking the high-precision Inertial Navigation System （INS） and Global Position System （GPS） navigation information from the host machine as the reference and combining it with the low-precision sensors on the slave machine. Then， the fusion of multi-source navigation information is achieved using an improved PF algorithm. For the importance probability density function selection and particle degradation problems of PF， the Levenberg-Marquardt iterative method is introduced on the basis of Extended Particle Filter （EPF） to ensure the stability and convergence of the filter. In the resampling phase， the fast resampling method is proposed to classify the obtained particle set， and the medium weight particles are not resampled anymore， while the rest of the particles are optimized in the normalization process using adaptive weight factors to make the obtained sample particle weights more uniform， thus improving the computational efficiency and navigation real-time. The simulation results show that compared with other PFs， the proposed algorithm can effectively improve the accuracy of Unmanned Aerial Vehicle （UAV） formation cooperative navigation and has certain practical value.

Key words: UAV formation, cooperative navigation, extended particle filter, Levenberg-Marquardt iteration, resampling

CLC Number:

V249.32

Jingxuan YUE, Hongru WANG, Dongqin ZHU, Chupalov ALEKSANDR. UAV formation cooperative navigation algorithm based on improved particle filter[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(14): 327995-327995.

Figures/Tables 12

Fig.1

Table 1

Comparison of operational operands of different resampling algorithms［26］

重采样算法	产生随机数所需操作				其余部分所需操作
重采样算法	加法运算	乘法运算	指数运算	所需随机数	加法运算	比较运算	乘法运算	循环次数
多项式重采样	$N$	$2 N$	$N$	$N$
随机重采样	$2 N$	$2 N$	0	$N$	$N$	$N$	0	$2 N$
残差重采样	$2 M$	$2 M$	0	1	$2 N$	$2 N$	$3 N$	$> 2 M + N$

Table 1

Fig.2

Table 2

Parameters of sensors configuration

无人机	传感器	参数	数值
主机	陀螺仪	随机常值/（（°） $⋅ h - 1$ ）	0.01
		白噪声/（（°） $⋅ h - 1$ ）	0.01
		一阶马尔可夫驱动白噪声/（（°） $⋅ h - 1$ ）	0.01
	加速度计	一阶马尔可夫零偏误差/g	0.000 1
	RTK	位置误差/m	0.1
	RTK	速度误差/（ $m ⋅ s - 1$ ）	0.001
从机	激光测角传感器	相对角度噪声/（ $°$ ）	0.01
	GPS	位置误差/m	20
	GPS	速度误差/（ $m ⋅ s - 1$ ）	0.2
	UWB	测距噪声/m	0.15

Table 2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 3

Table 4

Three-dimensional error of improved PF algorithm in complex environment

场景	三维位置误差均值/m	三维速度误差均值/（ $m ⋅ s - 1$ ）
GPS可用	9.89	0.94
GNSS拒止	10.38	1.32

Table 4

Fig.8

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滤波算法	三维位置误差均值/m	平均单步计算时间/s
PF	31.47	0.045
EPF	22.11	0.054
UPF	24.72	0.073
改进PF	11.27	0.051

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UAV formation cooperative navigation algorithm based on improved particle filter

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