A Simulation Method Based on Multi-source Sensors for Aircraft Type Identification

doi:10.16182/j.issn1004731x.joss.22-0967

Abstract

Abstract:

Existing simulation methods for aircraft type identification mainly focus on a single sensor and a single target. They do not consider the joint acquisition of aircraft parameters by various sensor devices such as optoelectronics, radar, and electronic detection in real scenarios, leading to the simple simulation scenarios. This paper proposes a simulation platform based on multi-source sensors. Specifically, the platform includes an infrared image simulator that uses a cycleGAN network to generate infrared images of the aircraft, a flight simulator that adopts the three-degree-of-freedom flight control method to generate the movement trajectory of the aircraft, a radar simulator, that applies ellipsoidal equations to simulate radar cross-section of the target, an electromagnetic simulator, which simulates a complex electromagnetic environment through typical signal source collection and random noise introduction. Experimental results on the simulation dataset show that the introduction of multiple sensor information can effectively improve the recognition results of target types and improve the ability of the model to assist decision-making.

Key words: multi-source sensor, aircraft simulation, infrared image simulator, trajectory simulator, radar simulator, electromagnetic simulator

CLC Number:

TP391.9

Gu Shaozhu, Ying Yuxin, Zhang Huajie, Tong Yiqi. A Simulation Method Based on Multi-source Sensors for Aircraft Type Identification[J]. Journal of System Simulation, 2024, 36(1): 149-159.

Figures/Tables 13

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Table 1

Table 2

Table 3

Performance of SSD model with different object distances

目标尺寸(像素)	距离/km	数量	准确率/%
$8 × 8$	>15	419	4.53
$16 × 16$	[10，15)	395	26.83
$32 × 32$	[5，10)	392	89.54
$64 × 64$	[2.5，5)	396	96.46
$128 × 128$	<2.5	395	97.72

Table 3

Table 4

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模拟器	光电	空情	雷达	电抗	权重
光电	1	1/3	1/3	1/5	0.08
空情	3	1	1	1/3	0.20
雷达	3	1	1	1/3	0.20
电抗	5	3	3	1	0.52

模拟器	数据类型	模型	准确率/%
光电	红外图像	SSD	62.37
空情	轨迹序列	LSTM	83.17
空情	轨迹序列	GRU	84.17
雷达	RCS序列	LSTM	80.58
雷达	RCS序列	GRU	80.00
电抗	电磁序列	LSTM	68.25
电抗	电磁序列	GRU	84.00
光电、空情、雷达、电抗	红外、轨迹、 RCS、电磁	投票	86.50

场景	缺失比例	准确率
上报信息缺失	0	86.50
	5	83.17
	10	76.33
上报信息不完整	0	86.50
	5	73.50
	10	66.33

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