A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization

Hao Sun; Weiming Huang; Xianghao Yu; Junting Chen

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arXiv · 2501.04237

A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization

Hao Sun·Weiming Huang·Xianghao Yu·Junting Chen

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Abstract

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.

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BibTeX RIS

Hao Sun, Weiming Huang, Xianghao Yu, Junting Chen. 2025-06-10. A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization. https://arxiv.org/abs/2501.04237

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