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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.16020 |
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| _version_ | 1866910618379878400 |
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| author | Lin, Mushen Yan, Fenggang Ren, Lingda Meng, Xiangtian Greco, Maria Gini, Fulvio Jin, Ming |
| author_facet | Lin, Mushen Yan, Fenggang Ren, Lingda Meng, Xiangtian Greco, Maria Gini, Fulvio Jin, Ming |
| contents | In the process of tracking multiple point targets in space using radar, since the targets are spatially well separated, the data between them will not be confused. Therefore, the multi-target tracking problem can be transformed into a single-target tracking problem. However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to directly establish the association between radar measurements and real targets. To address this issue, the Probabilistic Data Association (PDA) algorithm is used to calculate the association probability between each radar measurement and the target, and the measurements are fused based on these probabilities. Finally, an extended Kalman filter (EKF) is used to predict the target states. Additionally, we derive the Bayesian Cramér-Rao Lower Bound (BCRLB) under the PDA fusion framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BCRLB Under the Fusion Extended Kalman Filter Lin, Mushen Yan, Fenggang Ren, Lingda Meng, Xiangtian Greco, Maria Gini, Fulvio Jin, Ming Signal Processing In the process of tracking multiple point targets in space using radar, since the targets are spatially well separated, the data between them will not be confused. Therefore, the multi-target tracking problem can be transformed into a single-target tracking problem. However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to directly establish the association between radar measurements and real targets. To address this issue, the Probabilistic Data Association (PDA) algorithm is used to calculate the association probability between each radar measurement and the target, and the measurements are fused based on these probabilities. Finally, an extended Kalman filter (EKF) is used to predict the target states. Additionally, we derive the Bayesian Cramér-Rao Lower Bound (BCRLB) under the PDA fusion framework. |
| title | BCRLB Under the Fusion Extended Kalman Filter |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.16020 |