Qian Hanwang, Fu Pengcheng, Li Baoqing, Liu Jianpo, Yuan Xiaobing
Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2018 Jan 25;18(2):341. doi: 10.3390/s18020341.
Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.
跟踪移动目标旨在及时监测特定目标的入侵情况，是无线传感器网络（WSN）中最突出的应用之一。传统的仅基于静态传感器节点（SN）的WSN跟踪方法存在几个关键问题。例如，为了避免丢失移动目标，许多SN必须处于活动状态以在所有可能方向上跟踪目标，这会导致能量消耗过大。此外，当移动目标进入监测区域的覆盖空洞时，可能会丢失，在此期间其状态未知。为了解决这些问题，本文引入了一些移动传感器节点（MN）与SN协作，以形成混合WSN，因为MN能力更强且能量限制更少。然后，我们提出了一种针对混合WSN的有效目标跟踪方案，以动态调度MN和SN。此外，还提出了一种新颖的丢失恢复机制，以找到丢失的目标并在唤醒较少SN的情况下恢复跟踪。此外，为了提高恢复机制的鲁棒性和准确性，提出了一种自适应无迹卡尔曼滤波器（AUKF）算法，以动态调整过程噪声协方差。仿真结果表明，我们针对混合WSN中机动目标的跟踪方案不仅能够在目标丢失时有效跟踪目标，而且能够在激活节点较少的情况下保持出色的准确性和鲁棒性。
