The inertial navigation system/Doppler velocity logger (INS/DVL) plays an important role in ocean navigation. Any DVL malfunction poses serious risks to navigation. A precise detection system is required to detect the initial moments of DVL signal malfunctions; moreover, with loss of DVL, a fault-tolerant scheme (FTS) is necessary for DVL signal reconstruction. In this paper, an evolutionary knowledge-based method, namely improved evolutionary TS-fuzzy (I-eTS), is adopted to build an artificial intelligence (AI)-based pseudo DVL to deal with long-term outage of DVL. By employing Gaussian process regression (GPR) models for fault detection, a new FTS is constructed. To verify the effectiveness of the new fault detection and fault tolerance system, navigation data is gathered by a test setup and algorithms are performed in the laboratory. In the tests, it is demonstrated that the proposed FTS leads to rapid detection of both gradual and abrupt faults, which leads to less interaction between fault detection and FTS.

中文翻译：

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海洋导航系统中多普勒速度记录仪中断的新故障检测和容错方案

惯性导航系统/多普勒测速仪（INS/DVL）在远洋导航中发挥着重要作用。任何 DVL 故障都会对航行造成严重风险。需要一个精确的检测系统来检测DVL信号故障的初始时刻；此外，随着 DVL 的丢失，DVL 信号重建需要容错方案 (FTS)。本文采用基于进化知识的改进进化TS-fuzzy（I-eTS）方法构建基于人工智能（AI）的伪DVL，以应对DVL的长期中断问题。通过采用高斯过程回归 (GPR) 模型进行故障检测，构建了一种新的 FTS。为了验证新故障检测和容错系统的有效性，通过测试装置收集导航数据，并在实验室执行算法。