A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912421384290304 |
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| author | Sun, Hao Huang, Weiming Yu, Xianghao Chen, Junting |
| author_facet | Sun, Hao Huang, Weiming Yu, Xianghao Chen, Junting |
| contents | This paper presents a non-cooperative source localization approach based on received signal strength (RSS) and 2D environment map, considering both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Conventional localization methods, e.g., weighted centroid localization (WCL), may perform bad. This paper proposes a segmented regression approach using 2D maps to estimate source location and propagation environment jointly. By leveraging topological information from the 2D maps, a support vector-assisted algorithm is developed to solve the segmented regression problem, separate the LOS and NLOS measurements, and estimate the location of source. The proposed method demonstrates a good localization performance with an improvement of over 30% in localization rooted mean squared error (RMSE) compared to the baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04237 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization Sun, Hao Huang, Weiming Yu, Xianghao Chen, Junting Signal Processing This paper presents a non-cooperative source localization approach based on received signal strength (RSS) and 2D environment map, considering both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Conventional localization methods, e.g., weighted centroid localization (WCL), may perform bad. This paper proposes a segmented regression approach using 2D maps to estimate source location and propagation environment jointly. By leveraging topological information from the 2D maps, a support vector-assisted algorithm is developed to solve the segmented regression problem, separate the LOS and NLOS measurements, and estimate the location of source. The proposed method demonstrates a good localization performance with an improvement of over 30% in localization rooted mean squared error (RMSE) compared to the baseline methods. |
| title | A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2501.04237 |