不完全量测下事件触发水面扩展目标跟踪

doi:10.12382/bgxb.2022.1017

摘要/Abstract

摘要：

已有的水面扩展目标跟踪方法虽然提高了跟踪精度,但增加了通信消耗,而且无法避免不完全量测的干扰。为解决上述问题,依据探测器获取目标信息的机理将量测向量中各分量进行分组,建立不完全量测下由随机矩阵描述的扩展目标测量模型;进而结合目标运动模型,构建不完全量测下水面扩展目标跟踪模型;基于所建立的目标跟踪模型,提出一种多通道解耦的事件触发机制调节探测器与估计中心间的数据传输;基于容积卡尔曼滤波算法,提出一种不完全量测下事件触发水面扩展目标跟踪方法。仿真实验结果表明,所提方法可在保持较高目标跟踪精度的前提下,显著降低通信消耗,同时有效避免不完全量测的干扰。

关键词: 水面扩展目标跟踪, 事件触发机制, 不完全量测, 容积卡尔曼滤波

Abstract:

Although the existing methods for surface extended target tracking have the improved estimation performance, they increase the communication costs of target tracking systems. Besides, they can’t avoid the influences of intermittent measurements. In order to solve these problems, the components of measurement vectors are divided into different groups according to the measuring methods of extended target information, and a measuring model described by a random matrix is developed. And then a surface extended target tracking model with intermittent measurements is developed by combining the proposed measuring model with the target motion model. Based on the proposed target tracking model, a multi-channel decoupled event-triggered mechanism is proposed to schedule the data transmissions between the sensor and the estimation center. Lastly, an event-triggered surface extended target tracking method is provided based on the cubature Kalman filtering algorithm. Simulated results show that the proposed method can be used to significantly reduce the communication consumptions with preserving estimation performance, and avoid the influences of intermittent measurements.

Key words: surface extended target tracking, event-triggered mechanism, intermittent measurement, cubature Kalman filtering

中图分类号:

TP202⁺.4

梁苑, 戚国庆, 陈烨, 李银伢, 盛安冬. 不完全量测下事件触发水面扩展目标跟踪[J]. 兵工学报, 2024, 45(4): 1219-1228.

LIANG Yuan, QI Guoqing, CHEN Ye, LI Yinya, SHENG Andong. Event-triggered Surface Extended Target Tracking with Intermittent Measurements[J]. Acta Armamentarii, 2024, 45(4): 1219-1228.

图/表 10

图1 椭圆形扩展目标

Fig.1 Elliptical extended target

图2 探测器与估计中心间的数据传输

Fig.2 Data transmissions between sensor and estimation center

图3 量测均正常获取时RMSE对比

Fig.3 Comparison of RMSEs without intermittent measurements

图4 量测均正常获取时的通信消耗对比

Fig.4 Comparison of communication costs without intermittent measurements

表1 量测均正常获取时的ARMSE及平均总通信消耗

Table 1 ARMSE and average total communication costs without intermittent measurements

方法	ARMSE_p/ m	ARMSE_v/ (m·s^-1)	ρ_Σ
本文ET-CKF算法	2.53	0.39	62
已有TT-UKF算法	2.62	0.43	81
已有TT-CKF算法	2.46	0.38	81

图5 存在不完全量测时的RMSE对比

Fig.5 Comparison of RMSEs with intermittent measurements

图6 存在不完全量测时的通信消耗对比

Fig.6 Comparison of communication costs with intermittent measurements

图7 不同建模误差下RMSE对比

Fig.7 Comparison of RMSEs with different modeling errors

图8 不同建模误差下通信消耗对比

Fig.8 Comparison of communication costs with different modeling errors

表2 不同建模误差下ARMSE及平均总通信消耗

Table 2 ARMSEs and average total communication costs with different modeling errors

误差/m	ARMSE_p/m	ARMSE_v/(m·s^-1)	ρ_Σ
0	2.51	0.391	62
0.5	2.53	0.394	66
1.0	2.72	0.409	74
1.5	2.80	0.412	80

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