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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.


The comparison of position error in y direction.
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sensors-15-17350-f007: The comparison of position error in y direction.

Mentions: The tracking performances of proposed IMMPDA-MDCM algorithm, IMMPDA-DCM and IMMPDA-EKF are compared via 100 Monte Carlo simulations. All the algorithms are implemented using a personal computer (Windows 7 2009, Intel Core2 Duo CPU, 2.94 GHz, 4.0 GB of RAM, and MATLAB R2012a programming environment). The results of the root mean square error (RMSE) and runtime test of the target’s position for the three algorithms are shown in Table 2. Figure 6, Figure 7 and Figure 8 show the obtained position estimation error of three algorithms in x, y, and z direction, respectively.


Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter.

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

The comparison of position error in y direction.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17350-f007: The comparison of position error in y direction.
Mentions: The tracking performances of proposed IMMPDA-MDCM algorithm, IMMPDA-DCM and IMMPDA-EKF are compared via 100 Monte Carlo simulations. All the algorithms are implemented using a personal computer (Windows 7 2009, Intel Core2 Duo CPU, 2.94 GHz, 4.0 GB of RAM, and MATLAB R2012a programming environment). The results of the root mean square error (RMSE) and runtime test of the target’s position for the three algorithms are shown in Table 2. Figure 6, Figure 7 and Figure 8 show the obtained position estimation error of three algorithms in x, y, and z direction, respectively.

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.