基于双变量自适应“当前”统计模型的场面4D轨迹跟踪预测

doi:10.12305/j.issn.1001-506X.2024.02.31

Abstract

Abstract:

In order to solve the problem that the maneuvering target tracking performance of the "current" statistical model is degraded in complex airport environment due to fixed maneuvering frequency and assumed acceleration limit value, a bivariate adaptive "current" statistical model filtering algorithm is proposed. Initially, the maneuvering frequency of real-time online adjustment is calculated by using the first-order time dependent process model of acceleration noise. Then, according to the position state estimation value and the acceleration change rate, the acceleration variance of real-time online update is derived through the kinematics theoretical model and the position filtering residual, which theoretically realizes the adaptive update of the model. Finally, based on the real automatic dependent surveillance-broadcast (ADS-B) trajectory data of the scene, the verification results show that the improved "current" statistical model can achieve adaptive parameter adjustment on the basis of unequal interval prediction, and the trajectory fitting accuracies in position, velocity, and acceleration have been improved, and the tracking errors in velocity and acceleration have been converged.

Key words: target tracking, automatic dependent surveillance-broadcast (ADS-B), maneuver frequency, acceleration variance, tracking error

CLC Number:

Qixing LU, Xinmin TANG, Yang ZHOU. Airport surface 4D trajectory tracking prediction based on bivariate adaptive "current" statistical model[J]. Systems Engineering and Electronics, 2024, 46(2): 675-683.

Figures/Tables 11

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直角坐标双维度		位置/m	速度/(m/s)	加速度/(m/s²)
RMSE	CS-AF	7.682 2	1.643 1e+06	1.378 8e+05
	MCS-AF	7.022 5	11.626 6	2.109 1
	精度提高	8.6%	99.9%	99.9%
${\rm{\bar E}}$	CS-AF	3.483 7	4.987 8e+05	4.174 6e+04
	MCS-AF	3.111 2	7.822 5	1.061 8
	精度提高	10.7%	99.9%	99.9%
σ	CS-AF	6.853 8	1.567 1e+06	1.315 4e+05
	MCS-AF	6.302 0	8.610 1	1.824 1
	精度提高	8.1%	99.9%	99.9%
最高点	CS-AF	99.548 6	1.272 8e+07	7.632 7e+05
	MCS-AF	53.098 1	89.771 1	20.059 1
	精度提高	46.7%	99.9%	99.9%

X方向		位置/m	速度/(m/s)	加速度/(m/s²)
RMSE	CS-AF	6.494 3	515.517 1	30.756 5
	MCS-AF	6.477 9	7.974 9	2.042 2
	精度提高	0.3%	98.5%	93.4%
${\rm{\bar E}}$	CS-AF	2.917 3	96.015 6	7.657 6
	MCS-AF	2.570 3	4.876 2	0.931 3
	精度提高	11.9%	94.9%	87.8%
σ	CS-AF	5.952 1	507.004 0	29.817 8
	MCS-AF	5.807 9	6.316 7	1.819 2
	精度提高	2.4%	98.8%	93.9%
最高点	CS-AF	99.271 4	8.894 7e+03	498.009 0
	MCS-AF	48.195 5	85.894 9	20.059 1
	精度提高	51.5%	99.0%	95.9%

Y方向		位置/m	速度/(m/s)	加速度/(m/s²)
RMSE	CS-AF	5.338 0	1.643 1e+06	1.378 8e+05
	MCS-AF	3.995 4	8.460 4	0.527 0
	精度提高	25.2%	99.9%	99.9%
${\rm{\bar E}}$	CS-AF	2.638 5	4.987 1e+05	4.174 1e+04
	MCS-AF	2.600 0	5.496 2	0.319 6
	精度提高	1.5%	99.9%	99.9%
σ	CS-AF	4.666 7	1.567 2e+06	1.315 4e+05
	MCS-AF	3.003 3	6.438 4	0.419 5
	精度提高	35.6%	99.9%	99.9%
最高点	CS-AF	75.108 0	1.272 8e+07	7.632 7e+05
	MCS-AF	22.284 6	30.407 4	3.058 4
	精度提高	70.3%	99.9%	99.9%

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Airport surface 4D trajectory tracking prediction based on bivariate adaptive "current" statistical model

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