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

摘要/Abstract

摘要：

为了解决“当前”统计模型由于固定机动频率及假定加速度极限值, 在复杂机场环境下机动目标跟踪性能降低的问题, 提出一种双变量自适应的“当前”统计模型滤波算法。首先, 利用加速度噪声一阶时间相关过程模型, 推算出实时在线调整的机动频率。然后, 根据位置状态估计值与加速度变化率, 通过运动学理论模型和位置滤波残差, 推导出实时在线更新的加速度方差, 从理论上实现了模型自适应更新。最后, 基于场面真实广播式自动相关监视(automatic dependent surveillance-broadcast, ADS-B)轨迹数据进行验证, 结果表明改进的“当前”统计模型能够在非等间隔预测的基础上实现自适应调参, 且在位置、速度和加速度上的轨迹拟合精度均得到了提高, 并在速度和加速度跟踪误差方面得到了收敛。

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

中图分类号:

鲁其兴, 汤新民, 周杨. 基于双变量自适应“当前”统计模型的场面4D轨迹跟踪预测[J]. 系统工程与电子技术, 2024, 46(2): 675-683.

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.

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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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