基于SLSTM网络的两级修正机动目标跟踪方法

doi:10.19665/j.issn1001-2400.20241015

Abstract

Abstract:

In terms of maneuver model modeling,traditional maneuvering target tracking methods achieve matching between the model and the real motion of the target through adaptive interaction of the model set.When tracking non-cooperative targets,the maneuvering state changes at any time and the maneuvering forms are diverse.When the limited models in the model set cannot accurately represent its real motion,the tracking performance will degrade.This paper integrates the two level neural network of model correction and state correction into the filtering recursion process,and proposes a two-level modified maneuvering target tracking method(TLM-MTT) based on the stacked long short-term memory(SLSTM) network.The first-level model correction network perceives the maneuver of the target in real time,adjusts the model parameters,and realizes accurate modeling of the maneuver model.The second-level state correction network compensates the state estimation in real time to improve the accuracy of the filter output.The network is trained offline,and the trained network is used for online real-time tracking.Compared with traditional methods and other intelligent filtering methods,this method has a better tracking performance for high-maneuvering target tracking.

Key words: target tracking, long short-term memory(LSTM), kalman filter

CLC Number:

TN957.52

WANG Jin, SU Hongtao, WANG Shengli, LU Chao. Two-level modified maneuvering target tracking method based on the SLSTM network[J].Journal of Xidian University, 2025, 52(1): 37-49.

Figures/Tables 16

References 21

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场景	机动形式	最大过载/g	轨迹时长/s
1	蛇形	4.9	258
2	蛇形	9.0	210
3	爬升	3.5	330
4	爬升	5.0	160
5	俯冲	3.0	420
6	俯冲	5.9	350

场景	机动形式	最大过载/g	初始速度/(m·s^-1)	高度/m	备注
1	俯冲	6.0	220	4 000~6 000
2	爬升	5.5	200	500~1 000	低仰角模式
3	蛇形	2.0	190	3 000
4	圆周	1.5	80	300	低仰角模式

场景	IMM/m	RNN-MTT/m	DeepMTT/m	RKNN/m	TLM-MTT/m
1	22.3	17.4	16.8	17.1	12.1
2	15.0	12.2	11.6	11.8	11.0
3	7.0	6.4	6.8	6.8	6.2
4	10.6	8.2	8.0	8.2	7.8

场景	IMM/(°)	RNN-MTT/(°)	DeepMTT/(°)	RKNN/(°)	TLM-MTT/(°)
1	0.095	0.074	0.071	0.073	0.043
2	0.072	0.065	0.065	0.061	0.053
3	0.044	0.038	0.035	0.035	0.035
4	0.040	0.038	0.037	0.036	0.037

场景	IMM/(°)	RNN-MTT/(°)	DeepMTT/(°)	RKNN/(°)	TLM-MTT/(°)
1	0.092	0.066	0.043	0.044	0.031
2	0.185	0.157	0.155	0.152	0.140
3	0.043	0.040	0.040	0.038	0.037
4	0.129	0.094	0.087	0.090	0.082

Two-level modified maneuvering target tracking method based on the SLSTM network

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