發(fā)布時(shí)間：2019-07-04 14:36

【摘要】：針對現(xiàn)有精細(xì)農(nóng)業(yè)傳感器網(wǎng)絡(luò)監(jiān)測系統(tǒng)中的終端節(jié)點(diǎn)模塊定位算法易陷入局部最優(yōu)、定位精度低等缺陷,提出了一種改進(jìn)無線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)定位算法針對大豆農(nóng)田Zig Bee無線網(wǎng)絡(luò)終端節(jié)點(diǎn)進(jìn)行定位,采用高斯數(shù)據(jù)篩選模型修正接收信號(hào)強(qiáng)度測量距離。同時(shí),在標(biāo)準(zhǔn)粒子群算法基礎(chǔ)上引入混合變異策略,運(yùn)用混合策略中各個(gè)變異函數(shù)的優(yōu)勢在算法搜索過程中作用于種群,使粒子跳出局部最優(yōu),保證全局搜索遍歷能力。大豆實(shí)驗(yàn)田試驗(yàn)表明:標(biāo)準(zhǔn)粒子群定位算法和本文提出的混合變異粒子群定位算法的總體定位平均誤差分別為1.746 1m和1.1 7 0 8 m,表明改進(jìn)方法的定位精度更高。
[Abstract]:In view of the shortcomings of the terminal node module positioning algorithm in the existing fine agricultural sensor network monitoring system, such as easy to fall into local optimization and low positioning accuracy, an improved wireless sensor network node location algorithm is proposed to locate the terminal nodes of soybean farmland Zig Bee wireless network, and the Gaussian data screening model is used to modify the measurement distance of received signal strength. At the same time, based on the standard particle swarm optimization algorithm, the hybrid mutation strategy is introduced, and the advantages of each mutation function in the hybrid strategy are used to act on the population in the search process of the algorithm, so that the particles can jump out of the local optimization and ensure the ability of global search traversing. The experimental results of soybean experimental field show that the average positioning errors of the standard particle swarm optimization algorithm and the hybrid variant particle swarm optimization algorithm proposed in this paper are 1.746 1m and 1.170 m, respectively, indicating that the positioning accuracy of the improved method is higher than that of the standard particle swarm optimization algorithm and the hybrid variant particle swarm optimization algorithm proposed in this paper.
【作者單位】： 東北農(nóng)業(yè)大學(xué)電氣與信息學(xué)院;
【基金】：黑龍江省自然科學(xué)基金面上項(xiàng)目(C2015006) 教育部“春暉計(jì)劃”基金項(xiàng)目(Z2012074) 黑龍江省教育廳科技項(xiàng)目(12521038)
【分類號(hào)】：S126

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本文編號(hào)：2509989