Bearing Line Tracking and Bearing-Only Target Motion Analysis

F. Bonneton, C. Jauffret
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引用次数: 3

Abstract

The framework of this paper is a passive sonar system, more precisely at the core of bearing estimation and bearings-only target motion analysis (BO-TMA). A cosine of relative bearing estimation and bearings v.s. time image is first obtained by a conventional frequency-domain beam-former [D.H Johnson et al, 1993]. Computing the first two moments of each line of this image, we evaluate the likelihood of the cosine of relative bearing of the target. Then, the cosine of relative bearing and its derivative are considered as the two components of a state vector of a linear dynamic system. This state vector is then estimated (or extracted) by a classical algorithm of the hidden Markov model (HMM) arsenal, whose parameters are adjusted according to the statistical assumptions. Unlike, we consider the presence of one sole target, but the beginning and the end of the line are unknown and must be estimated too. The extracted track is used as measurements set of the BO-TMA. Finally, the confrontation of the TMA results allow us to evaluate the performance of the triplet (beam-former, bearing extraction, BO-TMA).
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方位线跟踪和纯方位目标运动分析
本文的框架是一个被动声纳系统,更准确地说,其核心是方位估计和单方位目标运动分析(BO-TMA)。首先通过传统的频域波束形成器获得相对方位估计和方位随时间图像的余弦值[D.H Johnson等,1993]。计算该图像每条线的前两个矩,我们评估目标相对方位的余弦的可能性。然后,将相对方位余弦及其导数视为线性动力系统状态向量的两个分量。然后通过隐马尔可夫模型库的经典算法估计(或提取)状态向量,隐马尔可夫模型库的参数根据统计假设进行调整。不同的是,我们考虑唯一目标的存在,但线的开始和结束是未知的,也必须估计。提取的航迹作为BO-TMA的测量集。最后,TMA结果的对抗使我们能够评估三元组(波束形成,轴承提取,BO-TMA)的性能。
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