Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter
In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/15/2915 |
| _version_ | 1797525222263357440 |
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| author | Zihao Huang Shijin Chen Chengpeng Hao Danilo Orlando |
| author_facet | Zihao Huang Shijin Chen Chengpeng Hao Danilo Orlando |
| author_sort | Zihao Huang |
| collection | DOAJ |
| description | In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC–PLKF) and the instrumental variable-based PLKF (IV–PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB–PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB–PLKF, we also propose its velocity-constrained version (VC–PLKF) to further improve the performance. Simulations show that the UB–PLKF and VC–PLKF outperform the BC–PLKF and IV–PLKF both in non-manoeuvring and manoeuvring scenarios. |
| first_indexed | 2024-03-10T09:10:41Z |
| format | Article |
| id | doaj.art-fe6660d84408424987e5a2881bcbc6ca |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:10:41Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fe6660d84408424987e5a2881bcbc6ca2023-11-22T06:06:05ZengMDPI AGRemote Sensing2072-42922021-07-011315291510.3390/rs13152915Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman FilterZihao Huang0Shijin Chen1Chengpeng Hao2Danilo Orlando3Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaDepartment of Engineering, University of Niccolo Cusano, via Don Carlo Gnocchi 3, 00166 Rome, ItalyIn bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC–PLKF) and the instrumental variable-based PLKF (IV–PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB–PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB–PLKF, we also propose its velocity-constrained version (VC–PLKF) to further improve the performance. Simulations show that the UB–PLKF and VC–PLKF outperform the BC–PLKF and IV–PLKF both in non-manoeuvring and manoeuvring scenarios.https://www.mdpi.com/2072-4292/13/15/2915bearings-only trackingpseudo-linear Kalman filternorm-constrained Kalman filter |
| spellingShingle | Zihao Huang Shijin Chen Chengpeng Hao Danilo Orlando Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter Remote Sensing bearings-only tracking pseudo-linear Kalman filter norm-constrained Kalman filter |
| title | Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter |
| title_full | Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter |
| title_fullStr | Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter |
| title_full_unstemmed | Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter |
| title_short | Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter |
| title_sort | bearings only target tracking with an unbiased pseudo linear kalman filter |
| topic | bearings-only tracking pseudo-linear Kalman filter norm-constrained Kalman filter |
| url | https://www.mdpi.com/2072-4292/13/15/2915 |
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