School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Science and Technology on Information Systems Engineering Laboratory, Nanjing 210007, China.
Sensors (Basel). 2022 Apr 27;22(9):3335. doi: 10.3390/s22093335.
The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method.
多传感器分布式融合系统已广泛用于联合检测和跟踪海洋目标。在实际场景中，由于杂波干扰和多目标的存在，本地传输轨迹的起源是不确定的。此外，由于不同声纳的内部处理时间不同，以及声纳和融合中心之间的通信延迟不同，轨迹不可避免地会以时间失序（OOS）的方式到达融合中心，这两个问题都对融合系统提出了重大挑战。在具有记忆的分布式融合框架下，本文提出了一种新的多步向前预测集成等效量测融合（MFP-IEMF）方法，它以最优的方式融合了多步 OOST 和轨迹起源不确定性，并且能够在同步和异步多声纳轨迹融合系统中实现。此外，还提出了一种随机中央轨迹初始化技术，通过快速启动和确认真实轨迹，及时检测到随机出现的海洋目标。数值结果表明，所提出的算法与现有的 OOS 重处理方法具有相同的最优性，并在 ANCTT 和估计精度方面与现有的 OOST 丢弃融合方法和 ANF-IFPFD 方法相比，显著提高了检测和跟踪性能。
