Wen Yingyou, Gao Rui, Zhao Hong
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang 110179, China.
Sensors (Basel). 2016 Jan 2;16(1):29. doi: 10.3390/s16010029.
Moving target tracking in wireless sensor networks is of paramount importance. This paper considers the problem of state estimation for L-sensor linear dynamic systems. Firstly, the paper establishes the fuzzy model for measurement condition estimation. Then, Generalized Kalman Filter design is performed to incorporate the novel neighborhood function and the target motion information, improving with an increasing number of active sensors. The proposed measurement selection approach has some advantages in time cost. As such, if the desired accuracy has been achieved, the parameter initialization for optimization can be readily resolved, which maximizes the expected lifespan while preserving tracking accuracy. Through theoretical justifications and empirical studies, we demonstrate that the proposed scheme achieves substantially superior performances over conventional methods in terms of moving target tracking under the resource-constrained wireless sensor networks.
无线传感器网络中的移动目标跟踪至关重要。本文考虑了L传感器线性动态系统的状态估计问题。首先，建立了用于测量条件估计的模糊模型。然后，进行广义卡尔曼滤波器设计，将新颖的邻域函数和目标运动信息纳入其中，随着活跃传感器数量的增加而得到改进。所提出的测量选择方法在时间成本方面具有一些优势。因此，如果达到了所需的精度，优化的参数初始化就可以很容易地解决，这在保持跟踪精度的同时最大化了预期寿命。通过理论论证和实证研究，我们证明了在资源受限的无线传感器网络下的移动目标跟踪方面，所提出的方案比传统方法具有显著优越的性能。
