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Simultaneous Self-Localization and Base Station Localization with Resonant Beam

Guangkun Zhang, Wen Fang, Mingliang Xiong, Qingwen Liu, Mengyuan Xu, Yunfeng Bai, Mingqing Liu, Siyuan Du

TL;DR

A distributed resonant beam positioning system that simultaneously estimates the base station and mobile target (MT) positions and facilitates self-positioning on the MT side, enabling dynamic expansion of both the number of base stations and the coverage area.

Abstract

High-precision positioning in GPS-denied environments is a demanding but challenging technology. Resonant Beam Positioning (RBP) utilizes a resonant beam with properties such as energy focusing, self-establishment, self-alignment, and passive operation, offering a promising solution for this task. However, traditional RBP algorithms require a fixed number of resonant beam base stations, which can be costly to expand coverage. To address this limitation, we propose a distributed resonant beam positioning (DRBP) system that simultaneously estimates the base station and mobile target (MT) positions. Firstly, the MT receives resonant beam samples to locate the base station in the limited field of view (FoV) region. Subsequently, it estimates self-position based on the known locations of the base stations. During moving, the DRBP system facilitates self-positioning on the MT side, enabling dynamic expansion of both the number of base stations and the coverage area. Numerical results demonstrate that DRBP achieves a positioning root mean square error (RMSE) of $0.1$ m and a rotation RMSE of 2$^\circ$, validating the system's high accuracy.

Simultaneous Self-Localization and Base Station Localization with Resonant Beam

TL;DR

A distributed resonant beam positioning system that simultaneously estimates the base station and mobile target (MT) positions and facilitates self-positioning on the MT side, enabling dynamic expansion of both the number of base stations and the coverage area.

Abstract

High-precision positioning in GPS-denied environments is a demanding but challenging technology. Resonant Beam Positioning (RBP) utilizes a resonant beam with properties such as energy focusing, self-establishment, self-alignment, and passive operation, offering a promising solution for this task. However, traditional RBP algorithms require a fixed number of resonant beam base stations, which can be costly to expand coverage. To address this limitation, we propose a distributed resonant beam positioning (DRBP) system that simultaneously estimates the base station and mobile target (MT) positions. Firstly, the MT receives resonant beam samples to locate the base station in the limited field of view (FoV) region. Subsequently, it estimates self-position based on the known locations of the base stations. During moving, the DRBP system facilitates self-positioning on the MT side, enabling dynamic expansion of both the number of base stations and the coverage area. Numerical results demonstrate that DRBP achieves a positioning root mean square error (RMSE) of m and a rotation RMSE of 2, validating the system's high accuracy.
Paper Structure (27 sections, 45 equations, 14 figures, 3 tables)

This paper contains 27 sections, 45 equations, 14 figures, 3 tables.

Table of Contents

  1. Introduction
  2. System Overview
  3. System Design
  4. Process of Positioning
  5. System Initialization and Recursive Process
  6. Base Station Positioning Method
  7. Angle Estimation
  8. Static Cavity Model and Field Distribution
  9. Dynamic Cavity Model with MT Motion
  10. MUSIC Algorithm for AoA
  11. Depth Calculation
  12. Partial Base Station Occlusion Solution
  13. Self Positioning Method
  14. RBS Integration and Coordinate Transformation
  15. Base Station Position Association
  16. ...and 12 more sections

Figures (14)

  • Figure 1: A typical application scenario of the DRBP. Mobile target (MT) at state i (S$_j$) using base station localization (BSL) method to estimate unknown base stations' positions in MT coordinate system. The self-localization (SL) method calculates the positional changes of base stations within the field of view in the MT coordinate system to estimate MT rotation and displacement, i.e., MT position.
  • Figure 2: System diagram (TIM: telescope internal modulator; $S_{AoA}$: optical field distribution on wavefront sensor); Att: attenuator.
  • Figure 3: Schematic of the AoA estimation process, depicting the distribution of elevation and azimuth angles on the wavefront sensor, and the resolution of the optical signal using the MUSIC algorithm.
  • Figure 4: Geometric model for BS depth estimation with directional vectors and deployment pattern.
  • Figure 5: Calibration of the DRBP and the UAV.
  • ...and 9 more figures