An Anti-cloud Backscattering Method Based on PSAF-LMS Algorithm for Circumferential-scanning Laser Fuze

doi:10.12382/bgxb.2024.0264

Abstract

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

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-.

Figures/Tables 18

Fig.1 Block diagram of SAF-LMS algorithm

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

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

Fig.4 Block diagram of the proposed backscatter filtering algorithm

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

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

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

Fig.7 Filtering effect diagrams of different filtering algorithms

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

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

Fig.10 Comparison of SBRs before and after filtering

Fig.11 Zynq implementation scheme of the algorithm

Fig.12 Schematic diagram of AMP operation mode

Fig.13 Signal processing Block Design

Fig.14 Test site

Fig.15 Test equipment

Fig.16 Echo signals of two groups of relative quadrants

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

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