针对水下无线传感器网络节点选择“组合爆炸冶的问题,研究了低计算复杂度节点选择问题。首先,在量化量测的条件下推导了后验克拉美罗下界(PCRLB)与节点位置的关系,为节点选择提供了准则;然后,将GBFOS 算法、贪心算法和随机算法与推导的PCRLB 相结合,设计了低计算复杂度的节点选择策略。实验结果表明,GBFOS 算法和贪心算法可以在保持跟踪性能不退化的情况下,大幅度降低计算复杂度,非常适合解决密集水下网络节点选择问题。此外,还将GBFOS 算法应用到非理想信道条件下节点选择问题,实验结果显示考虑非理想信道的影响可以大幅提高跟踪性能。

As for the problem of combinatorial explosion in node selection for target tracking based on underwater wireless sensor networks, low-complexity node selection problem is studied in this paper. At first, the PCRLB under the condition of the quantized measurements is derived, which can provide the criterion for node selection. Then, a low-complexity node selection scheme is designed by combining the GBFOS algorithm, the greedy algorithm, and the derived PCRLB. Simulation results show that GBFOS and greedy algorithm can greatly decrease the computational complexity while keeping good tracking performance and thus two algorithms are well suited to solving the node selection problem in dense network. Furthermore, the GBFOS is also applied to the imperfect channel case. Simulation results show that tracking performance can be improved by considering the effect of the imperfect communication channel.

TN957.52