基于卡尔曼滤波算法展开的飞行目标轨迹预测

doi:10.12305/j.issn.1001-506X.2023.06.25

Abstract

Abstract:

Target track prediction is a key technology to ensure the navigation safety of the target, to plan the flight path and to search the air target. It is of great significance in military and traffic control. Aiming at the problem that the traditional air target track prediction method model is relatively simplified and the prediction accuracy is low, a deep neural network based on the Kalman filter algorithm for the air target track prediction task is proposed. The model uses long short-term memory (LSTM) network to learn the target motion model from the track data of air targets, and then uses the Kalman filter algorithm to dynamically modify the estimated target state generated by LSTM, which effectively combines the advantages of the Kalman filter algorithm and deep neural network. Experiments on simulation data and real data verify the accuracy and effectiveness of the proposed model compared with other networks models for air target track prediction.

Key words: Kalman filter, track prediction, long short-term memory (LSTM) network, algorithm unfolding

CLC Number:

V212

Lican DAI, Xin LIU, Haiying ZHANG, Xiang DAI, Chenggang WANG. Flight target track prediction based on Kalman filter algorithm unfolding[J]. Systems Engineering and Electronics, 2023, 45(6): 1814-1820.

Figures/Tables 12

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网络模型	RNN	LSTM	KFU-LSTM
优化器	Adam	Adam	Adam
训练批尺寸	16	16	16
学习率	1×10^-3	1×10^-3	1×10^-3
训练轮数	40	40	20
迭代次数	6.4×10⁴	6.4×10⁴	3.2×10⁴

硬件环境	处理器	Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
	显卡	NVIDIA GTX 1660Ti, 6 GB
	内存	16 GB
软件环境	操作系统	Windows 10(64bit)
	编程语言	Python 3.6
	编程框架	Pytorch 1.1
	集成开发环境	PyCharm 2020.2.1 (Community Edition)

仿真数据	参数方程	参数描述
1	$ \left\{\begin{array}{l} x=t \cos t \\ y=t \sin t \\ z=t \end{array}\right.$	0≤t≤8π, 时间点数量为200
2	$ \left\{\begin{array}{l} x=\sin t \\ y=\cos t \\ z=\cos (4 t) \end{array}\right.$	0≤t≤6π, 时间点数量为300
3	$ \left\{\begin{array}{l} x=\sin t \\ y=\cos t \\ z=\cos (2 t) \end{array}\right.$	0≤t≤6π, 时间点数量为300

模型	RNN	LSTM	KFU-LSTM
仿真数据1	0.078 16	0.047 24	0.011 06
仿真数据2	0.035 44	0.014 15	0.008 65
仿真数据3	0.040 10	0.011 42	0.008 34
真实数据	0.061 62	0.055 79	0.004 39

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Flight target track prediction based on Kalman filter algorithm unfolding

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