發(fā)布時間：2018-07-10 20:13

  本文選題：無線傳感器網(wǎng)絡(luò) + 定位算法�。� 參考：《昆明理工大學》2015年碩士論文

【摘要】：節(jié)點定位技術(shù)是無線傳感器網(wǎng)絡(luò)最主要的支撐技術(shù)之一,也是研究難點之一在大部分實際應(yīng)用中,獲取精確的節(jié)點絕對位置或者節(jié)點間的相對位置信息是至關(guān)重要的。為了達到精確定位的目的,不僅要從硬件上減小節(jié)點間距離估計誤差或者測距誤差,而且要從定位算法上進行改進,提高算法的定位精度。本文首先講述了無線傳感器網(wǎng)絡(luò)技術(shù)的發(fā)展歷程,總結(jié)了國內(nèi)外研究現(xiàn)狀。研究比較了無線傳感器網(wǎng)絡(luò)節(jié)點間測距技術(shù)及基于測距的定位算法。最后對粒子群優(yōu)化算法進行研究和改進,在基于測距的定位模型下對改進算法進行仿真分析。本文對基于改進粒子群優(yōu)化的無線傳感器網(wǎng)絡(luò)定位算法進行研究。主要的研究工作有以下兩點：首先,在有錨節(jié)點無線傳感器網(wǎng)絡(luò)中,針對傳統(tǒng)RSSI測距模型的缺點,本文采用了一種改進RSSI測距模型,并提出了一種基于線性遞減權(quán)重的混沌粒子群算法(W-CLSPSO)的定位方法。對基于錨節(jié)點選擇策略的W-CLSPSO算法與不基于錨節(jié)點選擇策略的W-CLSPSO算法進行比較,證明了錨節(jié)點選擇策略在本文仿真實驗中的可行性。對W-CLSPSO算法、CLSPSO算法(混沌粒子群算法)、PSO算法、WLS算法(加權(quán)最小二乘法)分別在不同噪聲指數(shù)和錨節(jié)點數(shù)量情況下的仿真比較,仿真結(jié)果說明在定位精度和算法穩(wěn)定性上,W-CLSPSO算法要優(yōu)于另外三種算法。其次,針對錨節(jié)點缺失的無線傳感器網(wǎng)絡(luò),本文提出了一種基于TDOA定位模型的改進PSO算法與Taylor算法協(xié)同定位的新定位方法。在不同噪聲指數(shù)下,分別對PSO算法、AsyLnCPSO算法(基于異步學習因子的粒子群優(yōu)化算法)、SAAPSO算法(基于異步學習因子的自適應(yīng)權(quán)重粒子群優(yōu)化算法)和Min-max+Taylor算法、SAAPSO算法、SAAPSO+Taylor算法的平均位置誤差和均方差兩項指標進行仿真比較,仿真結(jié)果說明本文提出的改進算法具有更高的定位精度、更小的累積誤差和更好的穩(wěn)定性。
[Abstract]:Node location is one of the most important supporting techniques in wireless sensor networks, and it is also one of the difficulties in most practical applications. In most practical applications, it is very important to obtain accurate absolute position of nodes or relative position information between nodes. In order to achieve the goal of accurate location, not only the distance estimation error or ranging error between nodes should be reduced in hardware, but also the location algorithm should be improved to improve the accuracy of the algorithm. This paper first describes the development of wireless sensor network technology and summarizes the current research situation at home and abroad. This paper studies and compares the ranging technology between nodes in wireless sensor networks and the location algorithm based on ranging. Finally, the particle swarm optimization algorithm is studied and improved, and the improved algorithm is simulated under the location model based on ranging. In this paper, the location algorithm of wireless sensor networks based on improved particle swarm optimization (PSO) is studied. The main research work is as follows: firstly, in wireless sensor networks with anchor nodes, an improved RSSI ranging model is adopted to overcome the shortcomings of the traditional RSSI ranging model. A chaotic particle swarm optimization algorithm (W-CLSPSO) based on linear decreasing weight is proposed. The W-CLSPSO algorithm based on the anchor node selection strategy is compared with the W-CLSPSO algorithm which is not based on the anchor node selection strategy. The feasibility of the anchor node selection strategy in the simulation experiment is proved. The W-CLSPSO (chaotic Particle Swarm Optimization) algorithm and WLS (weighted least squares) algorithm are compared with each other under different noise exponents and the number of anchor nodes. The simulation results show that the W-CLSPSO algorithm is superior to the other three algorithms in the accuracy and stability of the algorithm. Secondly, for wireless sensor networks with missing anchor nodes, this paper proposes a new co-localization method based on TDOA localization model, which is based on improved PSO algorithm and Taylor algorithm. At different noise numbers, The average position error of PSO algorithm (Asynchronous learning factor based particle swarm optimization algorithm) and Min-max Taylor algorithm (adaptive weighted particle swarm optimization algorithm based on asynchronous learning factor) and Min-max Taylor algorithm respectively. The difference and mean variance were compared by simulation. The simulation results show that the improved algorithm has higher positioning accuracy, smaller cumulative error and better stability.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN929.5;TP212.9

【參考文獻】

相關(guān)期刊論文 前3條

1 余向陽;;無線傳感器網(wǎng)絡(luò)研究綜述[J];單片機與嵌入式系統(tǒng)應(yīng)用;2008年08期

2 于寧;萬江文;馮仁劍;;無線傳感器網(wǎng)絡(luò)定位求精方法[J];高技術(shù)通訊;2008年10期

3 丁宏毅;柳其許;王巍;;Chan定位算法與TDOA估計精度的關(guān)系[J];通信技術(shù);2010年03期

相關(guān)碩士學位論文 前2條

1 劉麗君;無線傳感器網(wǎng)絡(luò)定位算法的研究[D];大連理工大學;2009年

2 潘文鑫;錨節(jié)點稀疏的WSN節(jié)點定位算法[D];南京航空航天大學;2012年

，

本文編號：2114565