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Distributed Information Fusion in Multistatic Sensor Networks for Underwater Surveillance

机译:水下监视多静态传感器网络中的分布式信息融合

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Surveillance in antisubmarine warfare has traditionally been carried out by means of submarines or frigates with towed arrays. These techniques are manpower intensive. Alternative approaches have recently been suggested using distributed stationary and mobile sensors, such as autonomous underwater vehicles (AUVs). In contrast with the use of standard assets, these small, low-power, and mobile devices have limited processing and wireless communication capabilities. However, when deployed in a spatially separated network, these sensors can form an intelligent network achieving high performance with significant features of scalability, robustness, and reliability. The distributed information FUSION (DIFFUSION) strategy, in which the local information is shared among sensors, is one of the key aspects of this intelligent network. In this paper, we propose two DIFFUSION schemes, in which the information shared among sensors consists of: 1) contacts, generated by the local detection stage and 2) tracks, generated by the local tracking stage. In the first DIFFUSION scheme, contacts are combined at each nodes using the optimal Bayesian tracking based on the random finite set formulation. In the second DIFFUSION scheme, tracks are combined using the track-to-track association/fusion procedure, then a sequential decision based on the association events is exploited. A full validation of the DIFFUSION schemes is conducted by the NATO Science and Technology Organization—Center for maritime research and experimentation during the sea trials Exercise Proud Manta 2012–2013 using real data. Performance metrics of DIFFUSION and of local tracking/detection strategies are also evaluated in terms of time-on-target (ToT) and false alarm rate (FAR). We demonstrate the benefit of using DIFFUSION against the local noncooperative strategies. In particular DIFFUSION improves the level of TOT (FAR) with respect to the local tracking/detection strategies. In particular, the TOT - s increased over 90%–95% while the FAR is reduced of two order of magnitude. The problem of communication failures, data not available from the collaborative AUV during certain periods of time, is also investigated. The robustness of DIFFUSION with respect to these communication failures is demonstrated, and the related performance results are reported here. In particular, with 75% of communication failures the ToT is over 90%–95% with a relatively small increase of the FAR with respect to the case of perfect communication.
机译:传统上,反潜战的监视是通过带有拖曳阵列的潜艇或护卫舰来进行的。这些技术需要大量人力。最近已经提出了使用分布式固定式和移动式传感器的替代方法,例如自主水下航行器(AUV)。与使用标准资产相反,这些小型,低功耗和移动设备的处理和无线通信功能有限。但是,将这些传感器部署在空间上分隔的网络中时,它们可以形成具有高性能的智能网络,并具有可伸缩性,鲁棒性和可靠性等重要功能。其中传感器之间共享本地信息的分布式信息融合(DIFFUSION)策略是该智能网络的关键方面之一。在本文中,我们提出了两种扩散方案,其中传感器之间共享的信息包括:1)由本地检测阶段生成的接触和2)由本地跟踪阶段生成的跟踪。在第一个DIFFUSION方案中,基于随机有限集公式,使用最佳贝叶斯跟踪在每个节点处合并联系人。在第二种DIFFUSION方案中,使用轨道到轨道的关联/融合过程组合轨道,然后利用基于关联事件的顺序决策。北约科学和技术组织-海洋研究和实验中心,在进行演习Proud Manta 2012-2013期间,使用真实数据对DIFFUSION计划进行了全面验证。还根据目标时间(ToT)和误报率(FAR)评估了DIFFUSION和本地跟踪/检测策略的性能指标。我们证明了使用DIFFUSION对抗本地非合作策略的好处。特别地,相对于本地跟踪/检测策略,DIFFUSION提高了TOT(FAR)的水平。特别是,TOT-增长了90%至95%以上,而FAR降低了两个数量级。还研究了通信故障问题,即在某些时间段内无法从协作AUV获得数据的问题。演示了DIFFUSION针对这些通信故障的鲁棒性,并在此处报告了相关的性能结果。特别是,在75%的通信失败中,ToT超过90%–95%,相对于完美通信而言,FAR的增加相对较小。

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