基于脱靶量预测的飞行器反拦截机动方法

doi:10.12305/j.issn.1001-506X.2023.09.32

Abstract

Abstract:

With the rapid development of anti-missile interception technology, the survivability of the end stage of high-speed gliding vehicle is increasingly severely tested. To address this issue, an anti-nterception maneuver method based on long short-term memory (LSTM) network is proposed. The maneuver mode of vehicle is specified in advance, and the evasion maneuver problem of aircraft is simplified to the choice of maneuver time. Then, taking the state time series interception and miss distance as the sample, the training set is constructed, and the LSTM network is used to learn the nonlinear mapping relationship between them. Finally, the network is used to predict the miss distance in flight, so as to select the penetration time. Simulation and comparison of the performance of the method are carried out from three aspects: simultaneous interpreting of different state time series, different LSTM neurons and different sensor noise levels. The results show that compared with traditional sinusoidal and square wave maneuvers, the proposed maneuvering method can significantly improve the survival probability of aircraft and significantly increase the speed of falling. It has certain engineering application value.

Key words: attack defense confrontation, maneuver strategy, miss distance, long short-term memory (LSTM) network

CLC Number:

Shaobo CHEN, Jiamin YAN, Kuichen BU. Anti-intercept maneuver method of vehicle based on prediction of miss distance[J]. Systems Engineering and Electronics, 2023, 45(9): 2922-2930.

Figures/Tables 17

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References 24

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初始条件参数	取值范围
x₀/m	[-80 000, -70 000]
y₀/m	[20 000, 22 000]
z₀/m	[-2 000, 2 000]
x_D0/m	[-10 000, 10 000]
y_D0/m	[0, 0]
z_D0/m	[-10 000, 10 000]
v₀/(m·s^－1)	[1 650, 1 750]
v_D0/(m·s^－1)	[450,500]
N	[3,6]
θ₀/rad	[-0.14, 0.1]
φ₀/rad	[-0.05, 0.05]
R_tf/m	[100, 12 000]

机动方式	${\bar R}$_min/m	${\bar R}$_final/m	P_surv/%	V_f/(m/s)
脱靶量预测机动	14.597 6	0.859 6	98.7	678.35
不机动	0.562 5	0.830 8	0	694.26
正弦机动	3.562 5	0.820 6	6.7	590.51
方波机动	6.133 2	0.844 9	13.6	509.01

输入序列长度	${\bar R}$_min/m	S/m	P_surv/%
u=5	14.899 8	2.753 2	98.2
u=10	14.895 6	1.480 6	98.6
u=20	14.959 8	1.004 0	100
u=40	14.848 9	0.989 4	100

神经元数量	${\bar R}$_min/m	S/m	P_surv/%
n=21	14.799 0	1.486 5	99.8
n=28	15.045 6	0.981 2	100
n=35	15.818 4	1.002 7	100
n=42	14.967 5	0.946 4	100

噪声水平	q₁^A, q₂^A/(°)	$\dot{q}_1^A, \dot{q}_2^A /\left({ }^{\circ} / \mathrm{s}\right)$	R/m	h/m	v/(m/s)
无	0	0	0	0	0
1	0.1	0.01	25	5	1
2	0.3	0.03	75	15	3

Anti-intercept maneuver method of vehicle based on prediction of miss distance

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噪声水平	神经元个数	${\bar R}$_min/m	S/m	P_surv/%
1	n=21	15.583 3	3.537 2	98.5
	n=28	15.631 0	2.753 4	99.8
	n=35	16.327 1	2.866 6	99.9
	n=42	15.610 5	2.864 9	99.7
2	n=21	18.462 1	8.866 7	93.6
	n=28	16.617 8	5.592 0	96.0
	n=35	17.302 6	6.193 9	97.4
	n=42	16.962 4	5.884 6	96.7

噪声水平	输入时间序列长度	${\bar R}$_min/m	S/m	P_surv/%
1	u=5	27.178 7	9.386 1	97.3
	u=10	17.327 9	6.217 7	98.4
	u=20	16.992 3	5.104 0	99.6
	u=40	15.095 3	2.087 4	98.8
2	u=5	24.076 9	13.582 8	93.9
	u=10	19.090 1	8.842 7	92.1
	u=20	19.778 0	7.695 0	96.9
	u=40	15.673 8	5.053 5	93.6

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