Motion Parameter Estimation Combined Smoothing Filter Algorithm Based on Gaussian Mixture Probability Hypothesis Density

HUANG Qingdong; LI Xiaorui; CAO Yiyuan; LIU Qing

doi:10.11999/JEIT210439

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2488-2495

HUANG Qingdong, LI Xiaorui, CAO Yiyuan, LIU Qing. Motion Parameter Estimation Combined Smoothing Filter Algorithm Based on Gaussian Mixture Probability Hypothesis Density[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2488-2495. doi: 10.11999/JEIT210439

Citation:

HUANG Qingdong, LI Xiaorui, CAO Yiyuan, LIU Qing. Motion Parameter Estimation Combined Smoothing Filter Algorithm Based on Gaussian Mixture Probability Hypothesis Density[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2488-2495. doi: 10.11999/JEIT210439

Citation:

HUANG Qingdong, LI Xiaorui, CAO Yiyuan, LIU Qing. Motion Parameter Estimation Combined Smoothing Filter Algorithm Based on Gaussian Mixture Probability Hypothesis Density[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2488-2495. doi: 10.11999/JEIT210439

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Motion Parameter Estimation Combined Smoothing Filter Algorithm Based on Gaussian Mixture Probability Hypothesis Density

doi: 10.11999/JEIT210439 cstr: 32379.14.JEIT210439

1.
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China

Funds: The Basic Research Project of Information Security Laboratory for National Defense Research and Experiment (2018XXAQ09)

Received Date: 2021-09-15
Rev Recd Date: 2021-09-09

Available Online: 2021-09-15

Publish Date: 2022-07-25

Abstract

Abstract

Considering poor performance of target state estimation for Gaussian Mixture Probability Hypothesis Density(GM-PHD) filter when the target velocity is unknown or inaccurate, a combined smoothing filtering algorithm for motion parameter estimation based on GM-PHD is proposed. The velocity information is extracted from the target state, and the accuracy of velocity estimation is improved through the combined processing of median smoothing and linear smoothing. Then, the velocity is fed back to the state transition equation of the GM-PHD filter to improve the accuracy of state prediction. Simulation results show that the proposed algorithm can significantly improve the state estimation performance of GM-PHD filter when the target velocity is unknown or inaccurate.
- Target tracking,
- Gaussian Mixture Probability Hypothesis Density(GM-PHD) filtering,
- Parameter estimation,
- Combination of smooth

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References(20)

References

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