Event-triggered Surface Extended Target Tracking with Intermittent Measurements

doi:10.12382/bgxb.2022.1017

Abstract

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

CLC Number:

TP202⁺.4

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.

Figures/Tables 10

Fig.1 Elliptical extended target

Fig.2 Data transmissions between sensor and estimation center

Fig.3 Comparison of RMSEs without intermittent measurements

Fig.4 Comparison of communication costs without intermittent measurements

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

Fig.5 Comparison of RMSEs with intermittent measurements

Fig.6 Comparison of communication costs with intermittent measurements

Fig.7 Comparison of RMSEs with different modeling errors

Fig.8 Comparison of communication costs with different modeling errors

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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