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Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter.

Wu P, Li X, Kong J, Liu J - Sensors (Basel) (2015)

Bottom Line: The interacting multiple model (IMM) deals with the model switching.The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively.The probability data association (PDA) handles data association and measurement uncertainties in clutter.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, Nanjing University of Science and Technology, No.200, Xiaolingwei Street, Xuanwu District, Nanjing 210094, China. plwu@njust.edu.cn.

ABSTRACT
To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.

No MeSH data available.


Trajectory of target.
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sensors-15-17350-f003: Trajectory of target.

Mentions: The following example of tracking a highly maneuvering unmanned aerial vehicle is considered. The scenario of a highly maneuvering airborne target tracking is defined as follows: the sampling rate is s, the target makes five accelerating maneuver with linear segments connecting it. The initial position of the target is (10,000, 6000, 4000) m, and the velocity is (−300, −300, −100) m/s. In the first period of 1–5 s, it flies linearly by constant velocity. From 6–10 s, it makes an accelerating maneuver with (20, 50, 0) m/s2. From 11–15 s, it flies with (5, 25, 0) m/s2. From 16–20 s, it flies with (5, −25, 0) m/s2. From 21–25 s, it flies with (−25, −50, 0) m/s2. From 26–30 s, it flies with (0, 25, 0) m/s2. At last, it flies linearly from 31–35 s by constant velocity. The trajectory of target is shown in Figure 3.


Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter.

Wu P, Li X, Kong J, Liu J - Sensors (Basel) (2015)

Trajectory of target.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541938&req=5

sensors-15-17350-f003: Trajectory of target.
Mentions: The following example of tracking a highly maneuvering unmanned aerial vehicle is considered. The scenario of a highly maneuvering airborne target tracking is defined as follows: the sampling rate is s, the target makes five accelerating maneuver with linear segments connecting it. The initial position of the target is (10,000, 6000, 4000) m, and the velocity is (−300, −300, −100) m/s. In the first period of 1–5 s, it flies linearly by constant velocity. From 6–10 s, it makes an accelerating maneuver with (20, 50, 0) m/s2. From 11–15 s, it flies with (5, 25, 0) m/s2. From 16–20 s, it flies with (5, −25, 0) m/s2. From 21–25 s, it flies with (−25, −50, 0) m/s2. From 26–30 s, it flies with (0, 25, 0) m/s2. At last, it flies linearly from 31–35 s by constant velocity. The trajectory of target is shown in Figure 3.

Bottom Line: The interacting multiple model (IMM) deals with the model switching.The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively.The probability data association (PDA) handles data association and measurement uncertainties in clutter.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, Nanjing University of Science and Technology, No.200, Xiaolingwei Street, Xuanwu District, Nanjing 210094, China. plwu@njust.edu.cn.

ABSTRACT
To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.

No MeSH data available.