Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems
In this paper, we proposed a novel expectation–maximization-based simultaneous localization and mapping (SLAM) algorithm for millimeter-wave (mmW) communication systems. By fully exploiting the geometric relationship among the access point (AP) positions, the angle difference of arrival (ADOA) from...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/18/6941 |
| _version_ | 1827656862323965952 |
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| author | Lu Chen Zhigang Chen Zhi Ji |
| author_facet | Lu Chen Zhigang Chen Zhi Ji |
| author_sort | Lu Chen |
| collection | DOAJ |
| description | In this paper, we proposed a novel expectation–maximization-based simultaneous localization and mapping (SLAM) algorithm for millimeter-wave (mmW) communication systems. By fully exploiting the geometric relationship among the access point (AP) positions, the angle difference of arrival (ADOA) from the APs and the mobile terminal (MT) position, and regarding the MT positions as the latent variable of the AP positions, the proposed algorithm first reformulates the SLAM problem as the maximum likelihood joint estimation over both the AP positions and the MT positions in a latent variable model. Then, it employs a feasible stochastic approximation expectation–maximization (EM) method to estimate the AP positions. Specifically, the stochastic Monte Carlo approximation is employed to obtain the intractable expectation of the MT positions’ posterior probability in the E-step, and the gradient descent-based optimization is used as a viable substitute for estimating the high-dimensional AP positions in the M-step. Further, it estimates the MT positions and constructs the indoor map based on the estimated AP topology. Due to the efficient processing capability of the stochastic approximation EM method and taking full advantage of the abundant spatial information in the crowd-sourcing ADOA data, the proposed method can achieve a better positioning and mapping performance than the existing geometry-based mmW SLAM method, which usually has to compromise between the computation complexity and the estimation performance. The simulation results confirm the effectiveness of the proposed algorithm. |
| first_indexed | 2024-03-09T22:34:16Z |
| format | Article |
| id | doaj.art-e20e2a41c4fd4fe5a0fb39e7c363404e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:34:16Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e20e2a41c4fd4fe5a0fb39e7c363404e2023-11-23T18:51:51ZengMDPI AGSensors1424-82202022-09-012218694110.3390/s22186941Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication SystemsLu Chen0Zhigang Chen1Zhi Ji2Shaanxi Key Laboratory of Deep Space Exploration Intelligent Technology, School of Information and Communications Engineering, Xi’an Jiaotong University, No. 28 West Xianning Road, Xi’an 710049, ChinaShaanxi Key Laboratory of Deep Space Exploration Intelligent Technology, School of Information and Communications Engineering, Xi’an Jiaotong University, No. 28 West Xianning Road, Xi’an 710049, ChinaShaanxi Key Laboratory of Deep Space Exploration Intelligent Technology, School of Information and Communications Engineering, Xi’an Jiaotong University, No. 28 West Xianning Road, Xi’an 710049, ChinaIn this paper, we proposed a novel expectation–maximization-based simultaneous localization and mapping (SLAM) algorithm for millimeter-wave (mmW) communication systems. By fully exploiting the geometric relationship among the access point (AP) positions, the angle difference of arrival (ADOA) from the APs and the mobile terminal (MT) position, and regarding the MT positions as the latent variable of the AP positions, the proposed algorithm first reformulates the SLAM problem as the maximum likelihood joint estimation over both the AP positions and the MT positions in a latent variable model. Then, it employs a feasible stochastic approximation expectation–maximization (EM) method to estimate the AP positions. Specifically, the stochastic Monte Carlo approximation is employed to obtain the intractable expectation of the MT positions’ posterior probability in the E-step, and the gradient descent-based optimization is used as a viable substitute for estimating the high-dimensional AP positions in the M-step. Further, it estimates the MT positions and constructs the indoor map based on the estimated AP topology. Due to the efficient processing capability of the stochastic approximation EM method and taking full advantage of the abundant spatial information in the crowd-sourcing ADOA data, the proposed method can achieve a better positioning and mapping performance than the existing geometry-based mmW SLAM method, which usually has to compromise between the computation complexity and the estimation performance. The simulation results confirm the effectiveness of the proposed algorithm.https://www.mdpi.com/1424-8220/22/18/6941simultaneous localization and mapping (SLAM)expectation–maximization (EM)millimeter-wave communication systemsangle difference of arrival (ADOA)the stochastic Monte Carlo approximation |
| spellingShingle | Lu Chen Zhigang Chen Zhi Ji Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems Sensors simultaneous localization and mapping (SLAM) expectation–maximization (EM) millimeter-wave communication systems angle difference of arrival (ADOA) the stochastic Monte Carlo approximation |
| title | Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems |
| title_full | Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems |
| title_fullStr | Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems |
| title_full_unstemmed | Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems |
| title_short | Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems |
| title_sort | expectation maximization based simultaneous localization and mapping for millimeter wave communication systems |
| topic | simultaneous localization and mapping (SLAM) expectation–maximization (EM) millimeter-wave communication systems angle difference of arrival (ADOA) the stochastic Monte Carlo approximation |
| url | https://www.mdpi.com/1424-8220/22/18/6941 |
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