融合双频段信息的林业无线传感网节点测距算法

无线传感网节点测距技术是基于测距法节点定位的基础,针对林区中单一频段节点测距存在误差大、精确度低的问题,基于接收信号强度(received signal strength indication, RSSI),提出一种融合433 MHz和2.4 GHz双频段信息的节点测距方法。建立引入环境参数预测的林区信号衰减模型,利用高斯滤波修正偶然误差,选择合适的融合参数γ对双频段测距信息进行融合,通过在平均胸径接近、林分密度不同的场地中实验归纳γ的取值规律。实验结果表明,在林分密度600株/hm~2以下的林场中,γ=0.4最优;在林分密度600～1 000株/hm~2之间的林场中,γ=0.5最优,在林分密度1 000株/hm~2以上的林场中,γ=0.6最优。在γ取最优值的情况下,实验表明本文提出的方法测距结果的均方根误差值为1.74,较单一频段的测距误差减小了31.3%,提高了基于RSSI节点测距的精度。

Node Ranging Algorithm of Forestry Wireless Sensor Network Based on Dual-band Information

The wireless sensor network node ranging technology is based on the node positioning based on the ranging method. Aiming at the problems of large errors and low accuracy in single frequency band node ranging in forest areas, this article is based on the Received Signal Strength Indication (RSSI), Proposed a node ranging method that integrates 433MHz and 2.4GHz dual-band information. Establish a forest signal attenuation model that introduces environmental parameter prediction, use Gaussian filtering to correct accidental errors, select appropriate fusion parameters to fuse the dual-band ranging information, and summarize through experiments in sites with close average breast diameters and different forest density The value rule of, the experimental results show that in a forest farm with a forest density of less than 600 plants per hectare, is the best value; in a forest farm with a forest density of 600-1000 plants per hectare, is the best value. In a forest farm with a forest density of more than 1,000 trees per hectare, is the best value. In the case of taking the optimal value , experiments show that the root mean square error of the ranging result of the method proposed in this paper is 1.74, which is 31.3% less than the ranging error of a single frequency band, which improves the accuracy of RSSI-based node ranging.