發(fā)布時間：2018-07-26 11:57

【摘要】：隨著物聯(lián)網(wǎng)和智慧城市的快速發(fā)展,異構(gòu)數(shù)據(jù)融合技術(shù)越來越受到大家的關(guān)注,無論是在飛行目標(biāo)跟蹤、路徑規(guī)劃、環(huán)境檢測,還是決策支持和智能交通等領(lǐng)域都顯示出了巨大活力。異構(gòu)數(shù)據(jù)融合技術(shù)可以充分利用多源信息間的互補(bǔ),來更加全面、真實的反映感知對象的狀態(tài)信息,目前已成為物聯(lián)網(wǎng)的核心技術(shù)之一。本文對無線傳感網(wǎng)絡(luò)中的異構(gòu)數(shù)據(jù)融合技術(shù)進(jìn)行了比較深入、系統(tǒng)的研究,并針對異構(gòu)數(shù)據(jù)融合模型、融合精度以及異常數(shù)據(jù)處理方面存在的問題,提出了相應(yīng)的解決方案。通過算法比較和實驗分析,證明了算法的有效性和可行性。首先,對實際WSN(Wireless Sensor Network)中測得的感知數(shù)據(jù),從數(shù)據(jù)融合的角度分析感知數(shù)據(jù)的時空特性以及異常數(shù)據(jù)對融合結(jié)果的影響。在此基礎(chǔ)上,提出了灰度預(yù)測的異常數(shù)據(jù)修正算法。同時,從感知數(shù)據(jù)的三維結(jié)構(gòu)出發(fā),將無線傳感網(wǎng)絡(luò)數(shù)據(jù)融合分割成同構(gòu)數(shù)據(jù)融合和異構(gòu)數(shù)據(jù)融合兩個部分,并提出了異構(gòu)數(shù)據(jù)的層次融合模型。其次,針對異構(gòu)數(shù)據(jù)的層次融合模型,提出了一種基于混支持度和粗糙集的異構(gòu)數(shù)據(jù)融合算法。該算法在同、異構(gòu)數(shù)據(jù)類型的基礎(chǔ)上,分別提出了基于混支持度的同構(gòu)數(shù)據(jù)融合算法和基于粗糙集的異構(gòu)數(shù)據(jù)融合算法,降低了異構(gòu)數(shù)據(jù)的融合復(fù)雜性。最后,利用從實驗環(huán)境中得到的實際測量數(shù)據(jù),對本文提出的異構(gòu)數(shù)據(jù)融合算法進(jìn)行仿真實驗,從融合精度和正確識別率兩方面對算法的性能進(jìn)行評估。實驗結(jié)果表明,本文提出的基于混支持度和粗糙集的異構(gòu)數(shù)據(jù)融合算法,在提高融合精度的同時,也提高了決策支持的準(zhǔn)確性。
[Abstract]:With the rapid development of the Internet of things and smart cities, heterogeneous data fusion technology has attracted more and more attention. It has shown great vitality in the fields of flight target tracking, path planning, environment detection, decision support and intelligent transportation. Heterogeneous data fusion technology can make full use of the complementarity of multi source information. It has become one of the core technologies of the Internet of things, which is a more comprehensive and real reflection of the state information of the perceived object. This paper makes a thorough and systematic study of the heterogeneous data fusion technology in the wireless sensor network, and puts forward the problems in the heterogeneous data fusion model, the fusion precision and the abnormal data processing. According to the algorithm comparison and experimental analysis, the effectiveness and feasibility of the algorithm are proved. First, the spatial and temporal characteristics of the perceived data and the effect of abnormal data on the fusion results are analyzed from the point of view of the actual WSN (Wireless Sensor Network). On this basis, the gray level preview is proposed. At the same time, the data fusion of wireless sensor networks is divided into two parts: isomorphic data fusion and heterogeneous data fusion, and a hierarchical fusion model of heterogeneous data is proposed. Secondly, based on the heterogeneous data layer fusion model, a kind of mixed support degree is proposed. On the basis of the same and heterogeneous data types, this algorithm proposes an isomorphic data fusion algorithm based on mixed support and a heterogeneous data fusion algorithm based on rough sets, which reduces the complexity of heterogeneous data fusion. Finally, the actual measurement data obtained from the experimental environment are used in this paper. The performance of the algorithm is evaluated from the two aspects of the fusion precision and the correct recognition rate. The experimental results show that the heterogeneous data fusion algorithm based on the mixed support degree and the rough set proposed in this paper improves the accuracy of the fusion and improves the accuracy of the decision support.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP202;TN929.5;TP212.9

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本文編號：2145934