本文關(guān)鍵詞： 無線定位 RSS指紋 超分辨率 受限空間 Kriging算法　出處：《中國礦業(yè)大學》2016年博士論文　論文類型：學位論文

【摘要】：隨著物聯(lián)網(wǎng)的廣泛應(yīng)用,越來越多的無線傳感器被部署,眾所周知,傳感器除感知被測參數(shù)外,還需明確其位置信息,沒有位置信息的感知信息是沒有意義的,因此無線傳感器的精確定位已成為物聯(lián)網(wǎng)技術(shù)發(fā)展中的共性關(guān)鍵技術(shù)問題。在眾多無線定位方法中,基于接收信號強度RSS(Received Signal Strength)指紋模型的定位方法因其部署簡單、硬件成本低、適用范圍廣等特點受到研究者的關(guān)注。但該方法存在定位誤差分布不均勻、RSS指紋建模工作量大和精度低、定位精度不高的問題,本論文圍繞解決以上問題展開研究,研究內(nèi)容主要包括:針對定位誤差分布不均勻問題。研究RSS指紋模型定位方法的定位誤差分布模型,得到誤差分布規(guī)律。利用該誤差分布規(guī)律得到不同精度區(qū)域的劃分,為基于超分辨率原理的低分辨率區(qū)域劃分奠定基礎(chǔ),同時利用該規(guī)律亦可進一步提高定位方法的定位精度。針對傳統(tǒng)RSS指紋建模方法工作量大、精度低的問題。提出一種基于Kriging的RSS指紋生成算法,該算法首先對定位區(qū)域的信號強度空間場進行結(jié)構(gòu)分析,在充分了解該場的性質(zhì)前提下,選擇理論變差函數(shù)模型;然后,在無偏估計和最小估計方差的準則下,利用觀測值求解理論變差函數(shù),得到相應(yīng)的權(quán)值系數(shù);最后對待估點上的RSS指紋利用Kriging估計器進行計算。相比傳統(tǒng)逐點采集RSS指紋的方法,只需在定位區(qū)域內(nèi)采集少量點的RSS指紋,就能準確估計出其它待估點的RSS指紋,解決傳統(tǒng)RSS指紋建模工作量大、精度低的問題。針對RSS指紋定位算法定位精度不高的問題。提出了一種基于模糊核聚類FKC(fuzzy kernel clustering)支持向量機SVM(Support Vector Machine)的定位算法,在不提高定位算法計算復雜度的前提下,采用支持SVM升維技術(shù),提高指紋分辨率,從而提升算法的定位精度,采用模糊技術(shù)加快支持向量機的訓練過程。為進一步提高定位算法的定位精度,在上述定位算法的基礎(chǔ)上,根據(jù)定位誤差模型將定位區(qū)域劃分成若干個低分辨率LR(Low-Resolution)區(qū)域方案,提出了利用超分辨原理的新的定位算法,使定位算法的定位精度最高可提升50%。以上的研究成果解決了傳統(tǒng)基于RSS指紋模型定位方法存在的問題,不僅為物聯(lián)網(wǎng)中無線傳感器的精確定位提供技術(shù)支撐,還為無線精確定位方法的研究提供了新思路。
[Abstract]:With the wide application of the Internet of things, more and more wireless sensors are deployed, as everyone knows, in addition to the perception of sensor parameters to be measured, is required to clear the location information without location information awareness information is of no significance, therefore the accurate positioning of the wireless sensor has become a common problem in the development of networking technology in many. Wireless positioning method, the received signal strength based on RSS (Received Signal Strength) positioning method of fingerprint model because of its simple deployment, low cost, wide application range by researchers attention. But this method has the positioning error distribution is not uniform, RSS fingerprint modeling workload and low precision, the positioning accuracy is not high the problem, this paper focuses on solving above problems are studied, the research content mainly includes: Aiming at the problem of the uneven distribution of the positioning error method to study the localization of RSS fingerprint model. The positioning error distribution model, get the error distribution. Using the error distribution are divided into different precision area, low resolution region super resolution based on the principle of laying the foundation, positioning at the same time using the law can enhance the precision of positioning method. According to the workload of the traditional fingerprint RSS modeling method, the problem of low accuracy. This paper puts forward a RSS fingerprint generation algorithm based on Kriging algorithm, the first signal intensity space of the positioning area field structure analysis, to fully understand the nature of the field under the premise, the variogram model selection theory; then, the unbiased estimation and minimum variance criterion, using the observed value theory for solving the poor function, get the corresponding weight coefficient; finally to estimate RSS fingerprint point was calculated by using the Kriging estimator. Compared with the traditional methods of sampling point by point RSS fingerprint, only To collect RSS fingerprint of a few points in the positioning area, can accurately estimate the other estimated RSS fingerprint point, solve the traditional RSS fingerprint modeling workload, the problem of low accuracy. The RSS fingerprint positioning accuracy is not high. This paper proposes a fuzzy kernel clustering algorithm based on FKC (fuzzy kernel clustering) support vector machine SVM (Support Vector Machine) algorithm, without increasing the computational complexity of the localization algorithm under the premise, to support the SVM dimension raising technology, improve the fingerprint resolution, so as to enhance the positioning accuracy of the algorithm, the support vector machine to speed up the training process using fuzzy technology. In order to further improve the accuracy of the algorithm, in the based on the above algorithm, according to the positioning error model positioning area is divided into several low resolution LR (Low-Resolution) regional scheme is proposed by using the principle of the new super resolution An algorithm, the positioning accuracy of the positioning algorithm can improve the highest research results more than 50%. to solve the traditional RSS fingerprint model positioning method based on the problems, not only provide technical support for the accurate positioning of wireless sensor networking, provides a new idea for wireless location method research.

【學位授予單位】：中國礦業(yè)大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP212.9;TP391.44;TN929.5

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