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<p>To estimate the position of an underwater target without revealing one’s own asset, passive target tracking technology, known as target motion analysis (TMA), is essential. This paper proposes a three-dimensional TMA algorithm based on conic angle measurements obtained from a linear array sensor. The proposed algorithm linearizes nonlinear conic angle measurements into sets of Gaussian mixture measurement components and updates the estimated target state accordingly. Additionally, it estimates the target’s probability density function as a mixture of multiple Gaussian distributions under different hypotheses and combines them to generate the tracking result. The tracking performance of the proposed algorithm is validated through Monte Carlo simulations.</p>

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Conic Angles-only Target Motion Analysis Using Gaussian Mixture Measurements in 3D

  • 期刊论文

  • 发表日期：

    2025年6月11日

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  • Woochan Kim,
  • Taeil Suh

摘要

To estimate the position of an underwater target without revealing one’s own asset, passive target tracking technology, known as target motion analysis (TMA), is essential. This paper proposes a three-dimensional TMA algorithm based on conic angle measurements obtained from a linear array sensor. The proposed algorithm linearizes nonlinear conic angle measurements into sets of Gaussian mixture measurement components and updates the estimated target state accordingly. Additionally, it estimates the target’s probability density function as a mixture of multiple Gaussian distributions under different hypotheses and combines them to generate the tracking result. The tracking performance of the proposed algorithm is validated through Monte Carlo simulations.