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Measurement level AIS/radar fusion

机译：测量水平AIS /雷达融合

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In maritime surveillance, messages from radar and the Automatic Identification System (AIS) receivers are used for vessel trafficking and monitoring. The common trend is to use radars as the primary source of surveillance and AIS as a secondary source with little interaction between these data sets. The AIS messages provide very accurate position estimates associated with ID and other vessel information. However, AIS messages arrive unpredictably and intermittently depending on the type and behavior of the vessel. In addition, the revisit interval of AIS messages could be very large and it may vary from one vessel to another. In this work, a new measurement-level fusion algorithm to combine radar and AIS messages is proposed using the Joint Probabilistic Data Association (JPDA) framework. The proposed method handles AIS ID swaps between vessels and missing IDs while effectively fusing the radar measurements with AIS messages at measurement level. The uncertainty in the AIS ID-to-track assignment is resolved by assigning multiple AIS IDs to a target and updating the ID probabilities using a Bayesian inference with radar measurements, AIS messages and other targets. The performance of the proposed measurement-level fusion is compared with that of the track-to-track fusioa A modified Posterior Cramer-Rao Lower Bound (PCRLB) is also derived for the variable-rate heterogenous AIS/Radar network. Experimental results based on simulated data demonstrate the performance of the proposed technique.
机译：在海上监视中，来自雷达和自动识别系统（AIS）接收器的消息用于船舶运输和监视。共同的趋势是使用雷达作为监视的主要来源，而使用AIS作为辅助来源，而这些数据集之间的交互很少。 AIS消息提供与ID和其他船只信息相关的非常准确的位置估计。但是，取决于船只的类型和行为，AIS消息会意外地和间歇地到达。此外，AIS消息的重访间隔可能非常大，并且可能因一艘船而异。在这项工作中，提出了一种使用联合概率数据协会（JPDA）框架将雷达和AIS消息相结合的新的测量级融合算法。所提出的方法在有效地将雷达测量值与测量级别的AIS消息融合在一起的情况下，处理了船舶和丢失的ID之间的AIS ID交换。通过向目标分配多个AIS ID并使用具有雷达测量值，AIS消息和其他目标的贝叶斯推断来更新ID概率，可以解决AIS ID到轨道分配中的不确定性。将所提出的测量级融合的性能与轨到轨fusioa的性能进行了比较。还为可变速率异质AIS / Radar网络推导了改进的后验Cramer-Rao下界（PCRLB）。基于模拟数据的实验结果证明了该技术的性能。

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