Recognition Method of Target and Sweep Jamming Signal for FM Radio Fuze Based on BAS-BPNN

doi:10.12382/bgxb.2022.0248

Abstract

Abstract:

In order to solve the problem that the FM radio fuze is unable to counter AM frequency sweep jamming signals on battlefield in a complex electromagnetic environment, a target recognition method based on frequency domain information entropy, norm entropy and cepstrum entropy is proposed. Based on the output signal of the FM radio fuze under the action of the target and AM frequency sweep jamming signal, the frequency information entropy,norm entropy and cepstrum entropy are extracted to construct the feature matrix. The BAS algorithm is used to optimize the initial weight values and threshold of the back propagation neural network (BPNN). Then the optimized BPNN is used to classify and recognize the target and AM frequency sweep jamming signal. The experimental results with the measured data show that the feature matrix formed by feature extraction has separability between the target and the jamming signal. When the BPNN with optimal parameters is obtained by the optimization of the BAS algorithm, the recognition accuracy of the classifier can reach 99.96%, which significantly improves the ability of the FM radio fuze to counter AM frequency sweep jamming signals.

Key words: FM radio fuze, entropy features, target recognition, BPNN, beetle antennae search algorithm

CLC Number:

TJ43⁺

LIU Bing, HAO Xinhong, ZHOU Wen, YANG Jin. Recognition Method of Target and Sweep Jamming Signal for FM Radio Fuze Based on BAS-BPNN[J]. Acta Armamentarii, 2023, 44(8): 2391-2403.

Figures/Tables 17

Fig.1 Fuze output signal under the action of the target and different jamming signals

Fig.2 Fuze output signal preprocessing

Fig.3 Relationship between norm entropy P value and output of significance test of difference

Fig.4 Frequency information entropy, norm entropy and cepstrum entropy probability density distributions

Fig.5 Frequency information entropy, norm entropy and cepstrum entropy box plots of target and jamming signal

Fig.6 Using BAS algorithm for minimum value result of Ackley function

Fig.7 Using BAS algorithm for minimum value result of Cross-In-Tray function

Fig.8 Flow chart of BAS determining initial parameters of optimal network

Fig.9 Schematic diagram of experimental scene

Table 1 Experimental parameters setting

部件	参数	参数设置
	载波频率	f₀
引信	调制频偏	ΔF
	调制信号频率	f_m
干扰机	扫频带宽	f₀±ΔF
	扫频步长	$2 Δ F N$

Table 1 Experimental parameters setting

部件	参数	参数设置
	载波频率	f₀
引信	调制频偏	ΔF
	调制信号频率	f_m
干扰机	扫频带宽	f₀±ΔF
	扫频步长	$2 Δ F N$

Fig.10 Sigmoid activation function

Table 2 Recognition accuracy varying with the number of iterations

迭代次数	识别准确率/%	迭代次数	识别准确率/%
1	58.13±2.2316×10^-16	16	99.71±0.0070
2	58.13±2.2316×10^-16	17	99.72±0.0063
3	58.13±2.2316×10^-16	18	99.73±0.0057
4	58.13±2.2316×10^-16	19	99.84±0.0053
5	58.19±0.0026	20	99.86±0.0050
6	59.38±0.0241	21	99.92±0.0023
7	64.70±0.0590	22	99.91±0.0026
8	76.04±0.0949	23	99.93±0.0026
9	84.18±0.0835	24	99.94±0.0026
10	92.72±0.0621	25	99.95±0.0019
11	94.74±0.0461	26	99.96±0.0016
12	98.09±0.0211	27	99.93±0.0026
13	98.83±0.0142	28	99.94±0.0024
14	99.43±0.0098	29	99.96±0.0015
15	99.50±0.0104	30	99.96±0.0016

Fig.11 Recognition accuracy and standardn deviation of different iterations

Table 3 Recognition accuracy varying with the proportion of training samples

训练样本占比	1/10	1/9	1/8	1/7	1/6	1/5	1/4	1/3	1/2
识别准确率/%	99.78± 0.0077	99.81± 0.0056	99.85± 0.0045	99.90± 0.0023	99.84± 0.0037	99.94± 0.0015	99.89± 0.0030	99.92± 0.0022	99.93± 0.0027

Fig.12 Recognition accuracy and standardn deviation of different training sample rates

Table 4 Recognition accuracy varying with different methods

训练样本占比	迭代次数	识别准确率/%
训练样本占比	迭代次数	BAS-BPNN算法	BPNN算法
	15	99.50±0.0104	97.8619±0.0097
2/3	20	99.86±0.0050	98.3886±0.0042
	25	99.95±0.0019	98.5979±0.0013
	15	99.29±0.0123	97.6666±0.0116
1/3	20	99.78±0.0050	98.1846±0.0047
	25	99.94±0.0016	98.5865±0.0006

Fig.13 Recognition accuracy of different methods

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