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Expectation-Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems.

Lu Chen1, Zhigang Chen1, Zhi Ji1

  • 1Shaanxi 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, China.

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Summary
This summary is machine-generated.

We developed a new simultaneous localization and mapping (SLAM) algorithm for millimeter-wave (mmW) systems. This novel approach improves positioning and mapping accuracy by efficiently processing Angle Difference of Arrival (ADOA) data.

Keywords:
angle difference of arrival (ADOA)expectation–maximization (EM)millimeter-wave communication systemssimultaneous localization and mapping (SLAM)the stochastic Monte Carlo approximation

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Area of Science:

  • Wireless Communication Systems
  • Robotics and Navigation
  • Signal Processing

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for mobile robots and autonomous systems.
  • Millimeter-wave (mmW) systems offer high bandwidth but face challenges in localization accuracy.
  • Existing mmW SLAM methods often involve a trade-off between computational complexity and performance.

Purpose of the Study:

  • To propose a novel expectation-maximization-based SLAM algorithm tailored for mmW communication systems.
  • To enhance the accuracy and efficiency of indoor localization and mapping using mmW signals.
  • To address the limitations of existing SLAM techniques in mmW environments.

Main Methods:

  • Reformulated SLAM as a maximum likelihood joint estimation problem in a latent variable model.
  • Employed a stochastic approximation expectation-maximization (EM) method for estimating access point (AP) positions.
  • Utilized stochastic Monte Carlo approximation for the E-step and gradient descent for the M-step.
  • Estimated mobile terminal (MT) positions and constructed indoor maps based on estimated AP topology.

Main Results:

  • The proposed algorithm effectively estimates AP and MT positions and constructs indoor maps.
  • Achieved superior positioning and mapping performance compared to existing geometry-based mmW SLAM methods.
  • Demonstrated efficient processing capabilities by leveraging crowd-sourced Angle Difference of Arrival (ADOA) data.

Conclusions:

  • The novel EM-based SLAM algorithm offers improved accuracy and efficiency for mmW systems.
  • The method effectively utilizes geometric relationships and ADOA data for robust localization and mapping.
  • This approach provides a viable solution for mmW-based indoor navigation without compromising performance for computational complexity.