Exploiting Multipath Information for Integrated Localization and Sensing via PHD Filtering
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913468298297344 |
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| author | Du, Yinuo Zhao, Hanying Liu, Yang Yu, Xinlei Shen, Yuan |
| author_facet | Du, Yinuo Zhao, Hanying Liu, Yang Yu, Xinlei Shen, Yuan |
| contents | Accurate localization and perception are pivotal for enhancing the safety and reliability of vehicles. However, current localization methods suffer from reduced accuracy when the line-of-sight (LOS) path is obstructed, or a combination of reflections and scatterings is present. In this paper, we present an integrated localization and sensing method that delivers superior performance in complex environments while being computationally efficient. Our method uniformly leverages various types of multipath components (MPCs) through the lens of random finite sets (RFSs), encompassing reflections, scatterings, and their combinations. This advancement eliminates the need for the multipath identification step and streamlines the filtering process by removing the necessity for distinct filters for different multipath types, a requirement that was critical in previous research. The simulation results demonstrate the superior performance of our method in both robustness and effectiveness, particularly in complex environments where the LOS MPC is obscured and in situations involving clutter and missed detection of MPC measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15538 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploiting Multipath Information for Integrated Localization and Sensing via PHD Filtering Du, Yinuo Zhao, Hanying Liu, Yang Yu, Xinlei Shen, Yuan Signal Processing Accurate localization and perception are pivotal for enhancing the safety and reliability of vehicles. However, current localization methods suffer from reduced accuracy when the line-of-sight (LOS) path is obstructed, or a combination of reflections and scatterings is present. In this paper, we present an integrated localization and sensing method that delivers superior performance in complex environments while being computationally efficient. Our method uniformly leverages various types of multipath components (MPCs) through the lens of random finite sets (RFSs), encompassing reflections, scatterings, and their combinations. This advancement eliminates the need for the multipath identification step and streamlines the filtering process by removing the necessity for distinct filters for different multipath types, a requirement that was critical in previous research. The simulation results demonstrate the superior performance of our method in both robustness and effectiveness, particularly in complex environments where the LOS MPC is obscured and in situations involving clutter and missed detection of MPC measurements. |
| title | Exploiting Multipath Information for Integrated Localization and Sensing via PHD Filtering |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2312.15538 |