發(fā)布時間：2018-03-02 11:02

  本文關鍵詞： 無線傳感器網絡 室內定位 最小二乘算法 質心算法 DV-Hop定位算法　出處：《江南大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,隨著無線傳感器網絡技術的不斷發(fā)展以及人們對于室內定位的迫切需求,傳感器網絡的易于部署、可擴展性高、成本低等優(yōu)點,使得基于無線傳感器網絡的室內定位技術受到廣泛關注。然而,由于室內環(huán)境復雜多變造成信號傳播反射、散射和遮蔽等影響,傳統(tǒng)的傳感器定位技術在室內難以獲得精準而有效的位置信息。目前基于無線傳感器網絡的室內定位技術逐漸由理論邁向實際應用。但仍面臨許多亟待解決的問題,其中包括如何解決定位過于依賴錨節(jié)點的密度,如何有效地利用測距技術,如何優(yōu)化定位算法中的非線性計算問題,如何平衡定位精度、計算復雜度和定位穩(wěn)定性之間的關系�；谏鲜鰡栴},本文的研究內容主要包括以下幾個方面:(1)針對最小二乘算法定位過程中易受到測距誤差影響進行研究。分析了粒子群算法的缺陷,提出一種基于自適應差分的粒子群定位算法改進方案。該算法首先利用環(huán)境補償擬合未知節(jié)點到錨節(jié)點的距離,求解適應值函數;然后利用改進的自適應差分算法生成新的種群,再用粒子群算法和新的變異策略進行局部搜索,與適應值比較反復迭代逐漸收斂;最后得出未知節(jié)點位置。仿真結果表明,在室內定位中,與其它改進粒子群算法相比改進后的算法在抑制誤差累積與定位效果方面有了極大地改善。(2)針對質心定位算法中定位精度低的問題進行研究。分析了現(xiàn)有的質心定位算法存在的缺陷,提出一種基于最大似然估計加權的質心定位算法改進方案。該算法首先把錨節(jié)點與未知節(jié)點之間距離的最大似然估計值作為權值;然后在權值模型中,引進一個參數k優(yōu)化未知節(jié)點周圍錨節(jié)點分布;最后對未知節(jié)點的估計位置修正。仿真結果表明,基于最大似然估計的加權質心算法具有定位精度高和成本低的特點,優(yōu)于基于距離倒數的質心加權和基于RSSI倒數的質心加權算法,適用于大面積的室內定位。(3)針對DV-Hop定位算法中誤差累積的問題進行研究。分析了傳統(tǒng)的DV-Hop定位中的缺陷,提出一種基于跳數和跳距修正的遺傳算法優(yōu)化DV-Hop定位算法改進方案。該算法首先通過錨節(jié)點之間的RSSI均值對最小跳數進行約束;然后利用錨節(jié)點之間的最小跳數改善平均跳距;最后利用改進的遺傳算法優(yōu)化位置估計結果。仿真結果表明,與基于跳距加權DV-Hop算法和遺傳優(yōu)化的DV-Hop算法相比,定位精度有明顯提高。綜上所述,本文針對幾種室內定位算法的定位效果差的問題,致力于改進定位精度的研究并提出相應的改進方案,利用MATLAB對其仿真驗證。實驗結果表明,改進后的算法在定位誤差方面有了一定的改善,定位效果與同類型的算法相比明顯優(yōu)越。
[Abstract]:In recent years, with the continuous development of wireless sensor network technology and the urgent need for indoor positioning, sensor networks are easy to deploy, high scalability, low cost and so on. The indoor localization technology based on wireless sensor network has attracted wide attention. However, because of the complex indoor environment, the effects of signal propagation, reflection, scattering and shadowing are caused by the complexity of indoor environment. Traditional sensor positioning technology is difficult to obtain accurate and effective location information in indoor. At present, the indoor location technology based on wireless sensor network is gradually moving from theory to practical application. However, there are still many problems to be solved. It includes how to solve the density of anchor nodes, how to make use of ranging technology effectively, how to optimize the nonlinear calculation problem of location algorithm, how to balance the accuracy of location, and how to solve the problem of how to solve the problem. The relationship between computational complexity and location stability. The main contents of this paper include the following aspects: 1) to study the vulnerability of the least-squares algorithm to ranging errors in the localization process, and to analyze the defects of the particle swarm optimization algorithm (PSO). An improved Particle Swarm Optimization (PSO) algorithm based on adaptive difference is proposed. Firstly, the distance from unknown node to anchor node is fitted by environment compensation, and the fitness function is solved. Then the improved adaptive difference algorithm is used to generate the new population, then the particle swarm optimization algorithm and the new mutation strategy are used to carry out local search. Compared with the fitness value, iterative iteration gradually converges. Finally, the unknown node position is obtained. The simulation results show that, In indoor positioning, Compared with other improved particle swarm optimization algorithm, the improved algorithm has greatly improved the error accumulation and localization effect. (2) the problem of low positioning accuracy in centroid localization algorithm is studied. The existing centroid is analyzed. The shortcomings of the localization algorithm, An improved centroid localization algorithm based on weighted maximum likelihood estimation (MLE) is proposed, in which the maximum likelihood estimation of the distance between anchor node and unknown node is taken as the weight, and then in the weight model, A parameter k is introduced to optimize the distribution of anchor nodes around unknown nodes. Finally, the estimated location of unknown nodes is modified. The simulation results show that the weighted centroid algorithm based on maximum likelihood estimation has the characteristics of high positioning accuracy and low cost. Better than the centroid weighting algorithm based on the reciprocal distance and the centroid weighting algorithm based on the RSSI reciprocal, it is suitable for large area indoor positioning. (3) the problem of error accumulation in the DV-Hop location algorithm is studied, and the defects in the traditional DV-Hop location are analyzed. A genetic algorithm based on number of hops and modified hops is proposed to optimize the DV-Hop localization algorithm. Firstly, the minimum hops are constrained by the RSSI mean between anchor nodes, and then the average hops are improved by using the minimum hops between anchor nodes. Finally, the improved genetic algorithm is used to optimize the location estimation results. The simulation results show that compared with the hopping weighted DV-Hop algorithm and the genetic optimization DV-Hop algorithm, the positioning accuracy is obviously improved. Aiming at the problem of poor localization effect of several indoor positioning algorithms, this paper is devoted to the research of improving the positioning accuracy and puts forward the corresponding improvement scheme, which is verified by MATLAB. The experimental results show that, The improved algorithm has a certain improvement in localization error, and the localization effect is obviously superior to that of the same kind of algorithm.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.5;TP212.9

【引證文獻】

相關期刊論文 前1條

1 石魯生;朱慧博;;一種基于RSSI的區(qū)域重疊質心室內定位算法[J];智能計算機與應用;2017年03期

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本文編號：1556243