基于PSAF-LMS算法的多象限周视激光引信抗云雾干扰方法

doi:10.12382/bgxb.2024.0264

摘要/Abstract

摘要：

叠加在目标回波上的云雾后向散射信号是影响空空导弹周视激光引信测距精度的重要因素。针对目前抗云雾干扰方法适应性差、处理时效低等问题,提出一种基于可暂停样条自适应滤波的最小均方(Pauseable Spline Adaptive Filter-Least Mean Square,PSAF-LMS)算法,并设计了算法在现场可编程门阵列(Field-Programmable Gate Array,FPGA)与ARM的联合实现方案。PSAF-LMS算法可有效减少滤波器的稳态误差,并提高激光引信的时刻鉴别精度和抗干扰能力。此外,利用不同信噪比的目标回波信号进行仿真,并开展了云雾环境滤波效果模拟验证试验。研究结果表明:所提算法能够在34.85μs内有效滤除后向散射,并保留目标波峰原始变化趋势,滤波前后信噪比平均可提高25.15dB以上。

关键词: 激光引信, 后向散射, 自适应滤波, 样条最小均方算法

Abstract:

Cloud backscattered signals superimposed on the target echo is an important factor affecting the ranging accuracy of laser fuzes.Aiming at the problems of poor adaptability and low time-efficiency of the current anti-cloud and fog interference methods,this paper proposes a nonlinear backscattering filtering algorithm based on the pauseable spline adaptive filter-least mean square (PSAF-LMS) with variable step size,and designs a Zynq implementation scheme of the algorithm.According to the characteristics of laser detection echo signals in cloudy environments,a variable learning step function with positive correlation with the time error is proposed.A filtering algorithm pause module based on threshold detection is set to retain the original change trend of target echo and improve the moment discrimination accuracy and anti-jamming ability of laser fuse.The,simulations and environmental tests are carried out to verify the filtering effect by using the echo signals with different signal-to-noise ratios.The results show that the proposed algorithm can effectively filter out the back scattering within 34.85μs and retain the original change trend of the target waveform,and the signal-to-noise ratio before and after filtering can be improved by more than 25.15dB on average.

Key words: laser fuze, backscattering, adaptive filter, spline least mean square algorithm

查冰婷, 徐光博, 秦建新, 张合. 基于PSAF-LMS算法的多象限周视激光引信抗云雾干扰方法[J]. 兵工学报, 2025, 46(2): 240264-.

ZHA Bingting, XU Guangbo, QIN Jianxin, ZHANG He. An Anti-cloud Backscattering Method Based on PSAF-LMS Algorithm for Circumferential-scanning Laser Fuze[J]. Acta Armamentarii, 2025, 46(2): 240264-.

图/表 18

图1 SAF-LMS算法流程

Fig.1 Block diagram of SAF-LMS algorithm

图2 自适应后向散射滤波算法暂停机制流程

Fig.2 Flow chart of pause mechanism for adaptive backscatter filtering algorithm

图3 滤波器暂停阈值与滤波器停止阈值选择示意图

Fig.3 Schematic diagram of filter pause threshold and filter stop threshold selection

图4 本文提出的后向散射滤波算法原理

Fig.4 Block diagram of the proposed backscatter filtering algorithm

表1 初始化参数

Table 1 Initialization parameters

参数名称	参数初始值
w₀	[0.2,0.2,0.2,0.2,0.2]
Q	13
μ_w(0)	0.08
μ_q(0)	0.71
e_m	0.9
e_s	0.2
μ_q_min	0.6
M	5

图5 算法学习步长随原始差值信号的变化规律

Fig.5 Change of algorithm learning step-length with the original difference signals

图6 有、无目标回波信号

Fig.6 Comparison of target echo signal and non-target echo signal

图7 不同滤波算法滤波效果图

Fig.7 Filtering effect diagrams of different filtering algorithms

图8 目标波峰与后向散射波峰区域示意图

Fig.8 Schematic diagram of target wave crest and backscattered wave crest area

图9 不同SBR原始回波信号滤波前后信号对比

Fig.9 Comparison of signals before and after filtering different SBR original echo signals

图10 滤波前后SBR对比

Fig.10 Comparison of SBRs before and after filtering

图11 算法的Zynq实现方案

Fig.11 Zynq implementation scheme of the algorithm

图12 AMP运行模式示意图

Fig.12 Schematic diagram of AMP operation mode

图13 信号处理Block Design

Fig.13 Signal processing Block Design

图14 试验场地

Fig.14 Test site

图15 试验设备

Fig.15 Test equipment

图16 两组相对象限的回波信号

Fig.16 Echo signals of two groups of relative quadrants

图17 15m时两组相对象限回波信号处理结果

Fig.17 Processing results of echo signals of two groups of relative quadrants at 15m

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