【摘要】：隨著科學(xué)技術(shù)的快速發(fā)展,人們對生活質(zhì)量的追求不斷提高,大數(shù)據(jù)、云計算、物聯(lián)網(wǎng)、人工智能成為研究的熱點,智能化、網(wǎng)絡(luò)化、無線化已經(jīng)走進人們生活的方方面面。當(dāng)今科技正沿著綠色環(huán)保、低耗高能、無線智能的方向發(fā)展。正是因為這種趨勢,使得無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSN)成為了現(xiàn)代社會熱門技術(shù)。由于WSN網(wǎng)絡(luò)具有大規(guī)模、自組織、高魯棒性、低功耗的特點,特別適用于室內(nèi)定位系統(tǒng)尤其是室內(nèi)移動節(jié)點的定位系統(tǒng)。正是由于高性能、低功耗的要求,所以針對定位算法的先進性研究,成為研究的熱點問題。關(guān)于這類問題的研究,論文研究主要內(nèi)容如下:由于傳統(tǒng)Voronoi圖等傳統(tǒng)定位算法在測距技術(shù)等方面存在誤差,針對此類問題提出一種在多錨節(jié)點下的Voronoi圖改進定位算法。通過引進Cayley-Menger行列式,達到對距離關(guān)系的限制,從而得到關(guān)于測距誤差的約束方程,利用拉格朗日乘數(shù)法對該方程進行求解,同時把修正后的距離信息應(yīng)用于多錨節(jié)點Voronoi圖定位算法中,使該算法得到優(yōu)化。提出一種改進的最小二乘擬合蒙特卡羅(Least Squares Fitting Monte Carlo,LSFMCL)定位算法,該算法能夠克服傳統(tǒng)蒙特卡羅定位算法由于節(jié)點采樣效率低導(dǎo)致的定位精度低的缺陷,它采用最小二乘擬合節(jié)點運動軌跡,同時對節(jié)點位置進行初步預(yù)測,優(yōu)化縮小采樣區(qū)域并且確定采樣區(qū)間,而且提出權(quán)值概念并利用預(yù)測節(jié)點的權(quán)值信息,計算未知節(jié)點的位置。對室內(nèi)移動節(jié)點的定位技術(shù)進行硬件平臺的設(shè)計搭建,然后利用VB開發(fā)室內(nèi)移動節(jié)點定位系統(tǒng)的上位機監(jiān)控系統(tǒng)。
[Abstract]:With the rapid development of science and technology, people's pursuit of quality of life continues to improve. Big data, cloud computing, Internet of things, artificial intelligence has become a hot research, intelligence, networking, wireless has entered into all aspects of people's lives. Nowadays, technology is developing along the direction of environmental protection, low energy consumption and wireless intelligence. Because of this trend, wireless sensor network (Wireless Sensor Networks,WSN) has become a hot technology in modern society. Due to the characteristics of large scale, self-organization, high robustness and low power consumption, WSN network is especially suitable for indoor positioning systems, especially for indoor mobile nodes. Because of the requirement of high performance and low power consumption, the research on the advancement of localization algorithm has become a hot issue. The main contents of this paper are as follows: because of the errors in the traditional localization algorithms such as traditional Voronoi diagrams, an improved localization algorithm based on Voronoi diagrams with multiple anchor nodes is proposed for this kind of problems. By introducing the Cayley-Menger determinant to limit the distance relation, the constraint equation about the ranging error is obtained, and the Lagrange multiplier method is used to solve the equation. At the same time, the modified distance information is applied to the multi-anchor node Voronoi map localization algorithm, so that the algorithm can be optimized. An improved least square fitting Monte Carlo (Least Squares Fitting Monte Carlo,LSFMCL) localization algorithm is proposed, which can overcome the shortcomings of the traditional Monte Carlo localization algorithm, which is caused by the low sampling efficiency of nodes. It uses the least square to fit the moving track of the node, at the same time, it makes a preliminary prediction of the node position, optimizes the sampling area and determines the sampling interval, and puts forward the concept of weight value and uses the weight information of the predicted node. Calculate the location of unknown nodes. The hardware platform of indoor mobile node positioning technology is designed and built, and then the upper computer monitoring system of indoor mobile node positioning system is developed by using VB.
【學(xué)位授予單位】：北京建筑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5

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