Simulation and Multi-perspective Recognition Algorithm for Typical Trajectory Shapes

doi:10.16182/j.issn1004731x.joss.25-0022

Abstract

Abstract:

Current trajectory simulation methods inadequately address geometric shape features and kinematic properties of the target trajectory. To bridge this gap, a target trajectory shape simulation algorithm based on kinematic laws was proposed. The polar coordinate equations and curvature equations of multiple trajectories were integrated. The aircraft state parameters were solved by combining kinematic equations. Angular Gaussian noise was introduced to enhance trajectory diversity and authenticity. Additionally, a multi-perspective trajectory shape recognition algorithm was designed, which could effectively integrate image and sequential multi-modal features by adopting a multilayer perceptron, enabling precise trajectory shape recognition. Experimental results demonstrate that the proposed algorithm can generate diverse trajectory data conforming to physical laws. The multi-perspective recognition models achieve higher accuracy compared to single-perspective recognition models, validating the effectiveness of both trajectory shape simulation and recognition algorithms.

Key words: trajectory shape simulation, trajectory shape recognition, multi-view learning, multi-modal feature, deep learning

CLC Number:

TP391.9

Feng Xuejian, Ding Han, Tong Yiqi, Huo Chaoying, Zhang Yanjin. Simulation and Multi-perspective Recognition Algorithm for Typical Trajectory Shapes[J]. Journal of System Simulation, 2026, 38(3): 725-735.

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变量	初始化范围
经度/(°)	[-180, 180]
纬度/(°)	[-90, 90]
海拔/m	[300, 10 000]
速度/(m/s)	[200, 500]
轨迹形状	{直线，圆形，八字，跑道}
噪声强度	{低,中,高}

模型	模型类型	准确率/%	Macro-F1/%
AlexNet	图像模型	92.27	90.65
SqueezeNet	图像模型	87.26	83.23
GRU	序列模型	92.66	81.82
LSTM	序列模型	91.89	80.81
简单平均融合模型	混合模型	91.11	85.17
自适应权重融合模型	混合模型	87.64	83.37
多层感知机融合模型	混合模型	94.59	91.72

模型	轨迹形状真值
模型	形状	线形	圆形	八字	圆弧	跑道
多层感知机融合模型	线形	22	0	0	2	0
	圆形	0	96	4	0	2
	八字	0	0	72	0	0
	圆弧	0	1	1	13	4
	跑道	0	0	0	0	42