A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers
Abstract The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12451 |
| _version_ | 1797630894434942976 |
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| author | Jing Yang Chengcheng Liu Jie Huang Ting Ding Dexiu Hu Chuang Zhao |
| author_facet | Jing Yang Chengcheng Liu Jie Huang Ting Ding Dexiu Hu Chuang Zhao |
| author_sort | Jing Yang |
| collection | DOAJ |
| description | Abstract The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the lp‐norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the α‐stable distribution. Then, the localisation problem is transformed into an lp‐norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the lp‐norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramér–Rao lower bound for the target position and velocity estimation are derived under α‐stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers. |
| first_indexed | 2024-03-11T11:13:26Z |
| format | Article |
| id | doaj.art-618d6358d2714cfd81d99bd2105502f6 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-03-11T11:13:26Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-618d6358d2714cfd81d99bd2105502f62023-11-11T07:40:21ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922023-11-0117111728174210.1049/rsn2.12451A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliersJing Yang0Chengcheng Liu1Jie Huang2Ting Ding3Dexiu Hu4Chuang Zhao5National Digital Switching System Engineering and Technological Research Center Zhengzhou ChinaNational Digital Switching System Engineering and Technological Research Center Zhengzhou ChinaNational Digital Switching System Engineering and Technological Research Center Zhengzhou ChinaHenan High‐speed Railway Operation and Maintenance Engineering Research Center Zhengzhou ChinaNational Digital Switching System Engineering and Technological Research Center Zhengzhou ChinaNational Digital Switching System Engineering and Technological Research Center Zhengzhou ChinaAbstract The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the lp‐norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the α‐stable distribution. Then, the localisation problem is transformed into an lp‐norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the lp‐norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramér–Rao lower bound for the target position and velocity estimation are derived under α‐stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers.https://doi.org/10.1049/rsn2.12451MIMO radarmultistatic radarradar signal processing |
| spellingShingle | Jing Yang Chengcheng Liu Jie Huang Ting Ding Dexiu Hu Chuang Zhao A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers IET Radar, Sonar & Navigation MIMO radar multistatic radar radar signal processing |
| title | A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers |
| title_full | A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers |
| title_fullStr | A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers |
| title_full_unstemmed | A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers |
| title_short | A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers |
| title_sort | robust lp norm localization of moving targets in distributed multiple input multiple output radar with measurement outliers |
| topic | MIMO radar multistatic radar radar signal processing |
| url | https://doi.org/10.1049/rsn2.12451 |
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