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<p>In this paper, an Unmanned Aerial Vehicle (UAV)-assisted location and velocity estimation algorithm of a moving land vehicle based on both Received Signal Strength (RSS) and Doppler measurements is proposed. Using RSS measurements in addition to Doppler measurements makes the related Least Square (LS) optimization problem convex and quadratic. Thus, the closed-form solution is obtained by solving a system of linear equations which makes the proposed algorithm efficient. Analysis of the proposed RSS-Doppler algorithm is presented. An uncertainty-like inequality; stating that simultaneous high position and velocity accuracy cannot be achieved is derived and verified by simulations. A modified two-step approach is suggested to first estimate the position accurately by selecting a small time step which leads to inaccurate velocity estimation. Then, the estimated position is utilized to estimate the velocity accurately. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison to the counterparts.</p>

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UAV-aided vehicle location and velocity estimation using doppler and received signal strength

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Samaneh Motie,
  • Hadi Zayyani,
  • Mohammad Salman,
  • Hasan Abu Hilal,
  • Mostafa Rashdan

摘要

In this paper, an Unmanned Aerial Vehicle (UAV)-assisted location and velocity estimation algorithm of a moving land vehicle based on both Received Signal Strength (RSS) and Doppler measurements is proposed. Using RSS measurements in addition to Doppler measurements makes the related Least Square (LS) optimization problem convex and quadratic. Thus, the closed-form solution is obtained by solving a system of linear equations which makes the proposed algorithm efficient. Analysis of the proposed RSS-Doppler algorithm is presented. An uncertainty-like inequality; stating that simultaneous high position and velocity accuracy cannot be achieved is derived and verified by simulations. A modified two-step approach is suggested to first estimate the position accurately by selecting a small time step which leads to inaccurate velocity estimation. Then, the estimated position is utilized to estimate the velocity accurately. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison to the counterparts.