After a shipwreck (ship collision, explosion, disappearance, etc.), people rely on the marine search and rescue wireless sensor networks (MSR-WSNs) as one key weapon to locate and track drowning targets to save lives and assets. However, due to the complex and dynamic ocean environment, marine sensors could be captured by malicious attackers and then become Byzantine sensors. Byzantine sensors may tamper with the actual sensor data at a fixed or time-varying probability and transmit it to the data fusion center (DFC), which renders the normal localization and tracking function of the search and rescue system downgrade or even fail. To address this issue, we develop a New robust marine mobile Multi-Target LocAlization and Tracking scheme called NMTLAT by eliminating abnormal measurement data from the initial measurement data. NMTLAT works in several steps. By analyzing and mining sensors data and behavior, we firstly employ the information entropy of the system composed of a single sensor and their neighbor sensors to develop an efficient dynamic threshold based Byzantine node identification method. After migrating Byzantine sensors, the DFC utilizes sensor-aware and preprocessed marine data of the beacon or honest sensors to complete target localization and trajectory tracking. Specifically, NMTLAT consists of a novel distributed and cooperative multi-target localization and tracking algorithm using both received signal strength indication (RSSI) and priori information of sensors location. NMTLAT employs the importance sampling method to approximate the posterior probability distribution of the sensors and targets location. Furthermore, when the marine target is outside MSR-WSNs coverage, a piecewise function is utilized to characterize the likelihood of finding a drowning target in the search and rescue sea area. Finally, the Lyapunov’s second stability theorem is adopted to measure the stability of the NMTLAT. Our extensive simulation experiments validate that the NMTLAT performance is superior to existing solutions in various marine search and rescue scenarios.

在发生海难（船舶碰撞，爆炸，失踪等）之后，人们依靠海上搜索和救援无线传感器网络（MSR-WSN）作为定位和跟踪溺水目标以挽救生命和财产的关键武器。但是，由于复杂而动态的海洋环境，海洋传感器可能会被恶意攻击者捕获，然后成为拜占庭式传感器。拜占庭式传感器可能以固定或时变的概率篡改实际的传感器数据，并将其传输到数据融合中心（DFC），这会使搜索和救援系统的正常定位和跟踪功能降级甚至失效。为了解决这个问题，我们通过消除初始测量数据中的异常测量数据，开发了一种新的强大的海上移动多目标定位和跟踪方案NMTLAT。NMTLAT分几个步骤工作。通过分析和挖掘传感器数据和行为，我们首先利用由单个传感器及其相邻传感器组成的系统的信息熵来开发一种基于动态阈值的高效拜占庭节点识别方法。迁移拜占庭式传感器后，DFC使用信标或诚实传感器的传感器感知和预处理海洋数据来完成目标定位和轨迹跟踪。具体来说，NMTLAT由使用接收信号强度指示（RSSI）和传感器位置的先​​验信息的新颖的分布式协作多目标定位和跟踪算法组成。NMTLAT采用重要性抽样方法来近似估计传感器和目标位置的后验概率分布。此外，当海洋目标不在MSR-WSNs覆盖范围内时，将使用分段函数来表征在搜索和救援海域中发现溺水目标的可能性。最后，采用李雅普诺夫的第二个稳定性定理来度量NMTLAT的稳定性。我们广泛的仿真实验证明，NMTLAT在各种海上搜索和救援场景中的性能均优于现有解决方案。