Peng Duo, Xie Kun, Liu Mingshuo
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
Sensors (Basel). 2024 Jun 30;24(13):4261. doi: 10.3390/s24134261.
Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios.
针对传统无线传感器网络由于噪声干扰在机动目标定位与跟踪中产生误差的问题，本文提出一种基于卷积双向长短期记忆神经网络和扩展卡尔曼滤波的机动目标跟踪方法，该方法利用从卷积双向神经网络到传感器锚节点收集的机动目标的模拟RSSI值和实际目标值进行训练，以获得更准确的机动目标初始值，同时采用扩展卡尔曼滤波方法对实时目标进行精确定位和跟踪，从而获得实时目标的精确位置和跟踪信息。通过实验仿真可以看出，本文提出的算法在直线机动目标跟踪场景和非直线机动目标跟踪场景中均具有良好的跟踪效果。
