基于改进变异萤火虫优化粒子滤波的无人机目标定位

doi:10.12382/bgxb.2024.0549

摘要/Abstract

摘要：

针对无人机光电平台受到严重非线性因素影响,从而导致目标定位精度显著降低的问题,提出一种基于改进变异萤火虫优化粒子滤波(Improved Mutant Firefly Algorithm-Particle Filter,IMFA-PF)算法,用于无人机对地面目标精确定位。首先,建立无人机光电平台目标观测的状态方程和测量方程;利用IMFA-PF算法对目标地理位置进行估计,通过引入多重变异策略和弹力机制来改变粒子之间的相互作用模式,解决由严重非线性因素以及过度优化导致的粒子退化问题;通过一维非线性不稳定仿真系统和实测飞行实验验证了该算法的有效性。实验结果表明,所提算法能够改善粒子分布受观测非线性的影响,有效解决粒子退化的问题,与已有算法相比具有更好的鲁棒性和定位精度。

关键词: 无人机, 目标定位, 粒子滤波, 群智能优化, 非线性因素

Abstract:

In response to the significant reduction in target positioning accuracy caused by severe nonlinear factors affecting UAV electro-optical platforms, an algorithm based on improved mutant firefly algorithm-particle filter (IMFA-PF) is proposed for UAVs to accurately locate ground targets. Firstly, the state equations and measurement equations for target observation from UAV electro-optical platform are established. And then the IMFA-PF algorithm is utilized to estimate the geographic locatio of a target, and the interaction patterns among particles are altered by introducing multiple mutation strategies and an elasticity mechanism, thereby addressing the particle degradation issues caused by severe nonlinear factors and excessive optimization. Finally, the effectiveness of the algorithm is verified through a one-dimensional nonlinear unstable simulation system and actual flight experiments. Experimental results indicate that the proposed algorithm can improve the particle distribution’s resilience to observational nonlinearity and effectively tackle particle degradation issues, showing better robustness and positioning accuracy compared to the existing positioning methods.

Key words: unmanned aerial vehicle, target positioning, particle filter, swarm intelligence optimization, nonlinear factor

中图分类号:

V249

闫啸家, 朱惠民, 孙世岩, 石章松, 姜尚. 基于改进变异萤火虫优化粒子滤波的无人机目标定位[J]. 兵工学报, 2025, 46(5): 240549-.

YAN Xiaojia, ZHU Huimin, SUN Shiyan, SHI Zhangsong, JIANG Shang. An Improved Mutant Firefly Algorithm Optimized Particle Filter Algorithm for UAV Target Positioning[J]. Acta Armamentarii, 2025, 46(5): 240549-.

图/表 13

图1 基本坐标系示意图

Fig.1 Basic coordinate systems

图2 不同光强衰减系数的粒子吸引度变化曲线

Fig.2 Variation of particle attraction for different light intensity attenuation coefficients

表1 不同算法参数设置

Table 1 Parameter settings for different algorithms

各滤波方法	α	λ	f_min	f_max	ε_s	N_i	β₀	γ	r_j	ρ_th	k	F
BAPF	0.5	0.5	0	2
SFA-PF					0.5	10	1	0.01	1	0.6	1
IMFA-PF								0.1	10	0.6	1	0.5

图3 系统滤波状态估计

Fig.3 System filter state estimation

图4 估计误差绝对值

Fig.4 Absolute value of estimation error

图5 不同时刻粒子分布

Fig.5 Particle distributions at different moments

表2 不同PF算法仿真结果比较

Table 2 Comparison of simulated results of different PF algorithms

滤波算法	RMSE_mean/m			RMSE_var/m			T_mean/s
滤波算法	50	100	200	50	100	200	50	100	200
PF	4.1681	3.7164	3.5702	0.2642	0.3028	0.0935	0.0879	0.0936	0.1154
BA-PF	2.9312	2.5331	2.4170	0.0906	0.0693	0.0742	0.1117	0.1421	0.2132
SFA-FA	2.2711	2.1028	2.0057	0.0842	0.0730	0.0704	0.1313	0.1759	0.2698
IMFA-PF	2.0693	1.9371	1.9022	0.0791	0.0734	0.0699	0.1549	0.2037	0.3214

图6 实测飞行实验系统和飞行航线

Fig.6 Test flight experimental systems and flight paths

图7 无人机的成像图像

Fig.7 Imaging of UAVs

图8 PF与IMFA-PF算法实测结果对比(N=50)

Fig.8 Measured results of PF and IMFA-PF algorithm (N=50)

图9 PF与IMFA-PF算法实测结果对比(N=500)

Fig.9 Measured results of PF and IMFA-PF algorithm (N=500)

图10 最终定位误差曲线图

Fig.10 Final positioning error curve

表3 不同算法定位精度和运算时间

Table 3 Positioning accuracies and computation times of different algorithms

算法	性能指标	目标
算法	性能指标	T₁	T₂	T₃	T₄	T₅
PF	平均RMSE/m	29.21	29.45	31.69	34.54	35.97
	运算时间/s			0.926
BA-PF	平均RMSE/m	17.33	17.99	17.46	18.97	19.31
	运算时间/s			1.024
SFA-FA	平均RMSE/m	14.20	15.63	15.44	17.69	18.14
	运算时间/s			1.309
IMFA-PF	平均RMSE/m	10.82	10.69	11.34	12.46	12.90
	运算时间/s			1.554

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