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Volume 45 Issue 3
Mar.  2023
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XIU Jianjuan, DONG Kai, XU Cong’an. Number and Position Estimation Algorithm of Space Group Targets Based on Probability Hypothesis Density Filter and Dynamic Model[J]. Journal of Electronics & Information Technology, 2023, 45(3): 968-976. doi: 10.11999/JEIT211600
Citation: XIU Jianjuan, DONG Kai, XU Cong’an. Number and Position Estimation Algorithm of Space Group Targets Based on Probability Hypothesis Density Filter and Dynamic Model[J]. Journal of Electronics & Information Technology, 2023, 45(3): 968-976. doi: 10.11999/JEIT211600

Number and Position Estimation Algorithm of Space Group Targets Based on Probability Hypothesis Density Filter and Dynamic Model

doi: 10.11999/JEIT211600
Funds:  The National Natural Science Foundation of China(62171453), The Postdoctoral Science Foundation Project of China (2020M680631), The Natural Science Foundation Youth Project of Shandong Province (ZR2021QF103)
  • Received Date: 2021-12-30
  • Rev Recd Date: 2022-08-29
  • Available Online: 2022-09-03
  • Publish Date: 2023-03-10
  • Space targets have the characteristics of wide coverage, fast speed, high target density and similar movement, which lead to that in a relatively long time these targets can not be effectively distinguished. How to distinguish effectively the number and position of these non-cooperative space targets as soon as possible is very important. Therefore, based on Random Finite Set (RFS) theory and dynamic model of space targets, the number and position estimation method of unresolved space group targets is studied in this paper, which can effectively estimate the number and position of space group targets with high-speed and small spatial distribution range in the early stage of target monitoring. This method makes full use of the characteristics of Probability Hypothesis Density (PHD) filter, which can solve the number and state estimation of targets in unknown time-varying environment. The Gaussian Mixture PHD (GM-PHD) filter is combined with the space target dynamic equation to estimate the number of unresolved space targets, and the target state are estimated more effectively by the constraint of the dynamic equation. At the same time of target tracking, the resolution problem of unresolved space group targets can be solved. The correlation algorithms can provide data basis for state estimation, continuous stable tracking and reliable trajectory prediction of special value individual target in the group.
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