Results from COLLAB13 sea trial on tracking underwater targets with AUVs in bistatic sonar scenarios

G. Ferri, A. Munafò, R. Goldhahn, K. LePage
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引用次数: 12

Abstract

We describe the implementation of a novel non-myopic, receding horizon strategy to control the movement of an AUV towing a line array acting as a receiver node in a multistatic network for littoral surveillance and Anti-Submarine Warfare (ASW). The algorithm computes the vehicle heading angles to minimize the expected target position estimation error of a tracking filter. Minimizing this error is typically of the utmost interest in target state estimation since it is one way of maintaining track. The optimization solves a resulting decision tree taking into consideration a planning future horizon. In this paper, we focus on how to solve the different challenges related to the implementation of this kind of computational intensive algorithms on vehicles operating in realistic ASW scenarios and characterized by limited computational power. Specifically, we describe the multistatic network used in COLLAB13 experiments, how we simplify the solution of the resulting decision tree and the implementation of the algorithm in CMRE's software system running on AUVs and based on MOOS-IvP middleware. We conclude reporting results from COLLAB13 which demonstrate the feasibility to use the proposed algorithm in realistic operations onboard AUVs and its effectiveness over conventional predefined tracklines.
双基地声纳场景下水下航行器跟踪水下目标的COLLAB13海上试验结果
我们描述了一种新的非近视眼、后退视界策略的实现,以控制AUV拖曳线阵列作为多静态网络中用于沿海监视和反潜战(ASW)的接收节点的运动。该算法通过计算车辆航向角来最小化跟踪滤波器的目标位置估计误差。在目标状态估计中,最小化这个误差通常是最重要的,因为它是保持跟踪的一种方法。优化解决了考虑到规划未来范围的最终决策树。在本文中,我们关注的是如何解决在现实反潜战场景中运行的车辆上实现这种计算密集型算法所面临的不同挑战,这些挑战的特点是计算能力有限。具体来说,我们描述了COLLAB13实验中使用的多静态网络,我们如何简化结果决策树的求解,以及该算法在CMRE基于MOOS-IvP中间件的auv上运行的软件系统中的实现。我们总结了来自COLLAB13的报告结果,这些结果证明了在auv的实际操作中使用所提出的算法的可行性,以及它在传统预定义轨道上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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