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Maritime vehicle tracking using underwater gliders and particle filters

机译:使用水下滑翔机和粒子过滤器进行海上车辆跟踪

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摘要

A major difficulty in tracking unidentified vessels at sea is due to the need to observe large areas for long periods of time for sparsely appearing targets. A possible solution is to use a network of energy efficient underwater gliders that use buoyancy engines and are equipped with sensor array, processing, and communication hardware. Gliders, however, have a limited size and payload which limits the length of their sensor array and the capabilities of their power, processing, and communication modules. This reduces the resolution of measurements that can be collected and processed on board of gliders and be communicated to a fusion center for accurate target tracking. In this work, we propose the use of adaptive compressive sensing and processing to enable accurate target tracking while using low signal acquisition, processing, and communication rates, short sensor array lengths, and simplified sensing and processing hardware hosted on gliders. Moreover, we propose a particle filtering method to fuse data from multiple sensing nodes and process sensor array measurements with non-linear, non-Gaussian relationship to the target state. A simulation based scenario of tracking a single target by four underwater gliders is used to evaluate the effectiveness of the method.
机译:跟踪海上未识别的船只的重大困难是由于需要长时间观察大面积,以稀疏出现目标。可能的解决方案是使用使用浮力发动机的节能水下滑翔机网络,配备传感器阵列,加工和通信硬件。然而,滑翔机的尺寸和有效载荷限制了它们的传感器阵列的长度和其电源,处理和通信模块的能力。这减少了可以在滑翔机上收集和加工的测量的分辨率,并将其传送到融合中心以精确的目标跟踪。在这项工作中,我们提出了使用自适应压缩感测和处理,以便在使用低信号采集,处理和通信速率,短传感器阵列长度,简化的传感器阵列长度以及在滑翔机上托管的简化感应和处理硬件来实现准确的目标跟踪。此外,我们提出了一种粒子滤波方法,以熔化来自多个感测节点的数据和利用非线性,非高斯关系到目标状态的传感器阵列测量。通过四个水下滑翔机跟踪单个目标的基于模拟的场景来评估该方法的有效性。

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