本文選題：接收信號強度 + 模型參數(shù)�。� 參考：《華中科技大學》2014年碩士論文

【摘要】：無線傳感器網(wǎng)絡中節(jié)點的部署環(huán)境通常比較復雜，導致節(jié)點間的通信模型無法預測，不同時刻節(jié)點間信號的傳輸方式存在很大的差異。正是傳感器網(wǎng)絡在實際環(huán)境中的這些動態(tài)性變化，以及季節(jié)，溫度等對節(jié)點的影響，因此，需要提出一種對環(huán)境自適應求解模型參數(shù)的算法。僅僅通過環(huán)境中的實驗數(shù)據(jù)或者經(jīng)驗模型來得到模型參數(shù)都是不準確的，本文旨在將當前環(huán)境下的實測數(shù)據(jù)特征和經(jīng)驗模型結合，設計參數(shù)自適應算法。同時，針對復雜環(huán)境中存在不可忽視的非視距誤差，通過濾波器對接收信號強度值進行濾波。最后，基于參數(shù)自適應和非視距誤差抑制模型設計一種適用于復雜環(huán)境的高精度定位算法。 本文首先搭建了多組基于Mica2平臺的實驗，包括視距傳播實驗、單方向傳播實驗等，通過實驗數(shù)據(jù)研究節(jié)點接收信號強度值的傳播特性，同時分析對無線傳播模型參數(shù)造成的影響。在對比現(xiàn)有傳播模型參數(shù)求解的方法基礎上，設計一種自適應求解模型參數(shù)的算法。將當前環(huán)境下的實測數(shù)據(jù)特征和經(jīng)驗模型相結合，保證模型參數(shù)更能適應環(huán)境的動態(tài)性變化。同時，針對復雜環(huán)境中非視距路徑存在，導致節(jié)點間的測量值存在較大的非視距誤差問題，比較現(xiàn)有非視距誤差判定的方法，分析優(yōu)缺點在此基礎上進行改進，使之更適應于實際環(huán)境。并且設計有偏卡爾曼濾波器對非視距誤差進行抑制，減小節(jié)點間的測距誤差。最后，將粒子群優(yōu)化算法與基于估計距離的加權質(zhì)心算法結合，在對數(shù)-正態(tài)模型下進行定位效果仿真，，同時結合實際實驗數(shù)據(jù)對算法進行定位性能分析，通過實驗驗證了該算法在復雜環(huán)境下得到了較好的定位效果。
[Abstract]:The deployment environment of nodes in wireless sensor networks is usually complex, which leads to the unpredictable communication model between nodes, and there are great differences in the transmission modes between nodes at different times. It is precisely the dynamic changes of sensor networks in the real environment and the effects of seasons and temperatures on the nodes. Therefore, an adaptive algorithm to solve the model parameters is proposed. It is not accurate to get the model parameters only from the experimental data or the empirical model in the environment. This paper aims to combine the characteristics of the measured data in the current environment with the empirical model to design an adaptive parameter algorithm. At the same time, the received signal strength is filtered by filter in view of the non-line-of-sight error which can not be ignored in complex environment. Finally, based on parametric adaptive and non-line-of-sight error suppression model, a high precision localization algorithm is designed for complex environment. In this paper, we first set up several experiments based on Mica2 platform, including line-of-sight propagation experiment, single-direction propagation experiment, etc. Through the experimental data, we studied the propagation characteristics of the received signal intensity value. At the same time, the influence on the parameters of wireless propagation model is analyzed. On the basis of comparing the existing methods to solve the parameters of the propagation model, an adaptive algorithm for solving the parameters of the model is designed. The characteristics of measured data and the empirical model are combined to ensure that the parameters of the model are more adaptable to the dynamic changes of the environment. At the same time, in view of the non-line-of-sight path existing in complex environment, which leads to the larger non-line-of-sight error problem between nodes, this paper compares the existing non-line-of-sight error determination methods, and analyzes the advantages and disadvantages of the improved method. Make it more suitable for the actual environment. A biased Kalman filter is designed to reduce the range error between nodes. Finally, the particle swarm optimization algorithm is combined with the weighted centroid algorithm based on the estimated distance, and the localization effect is simulated in logarithmic normal model. At the same time, the localization performance of the algorithm is analyzed based on the actual experimental data. The experimental results show that the algorithm has better localization effect in complex environment.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN929.5;TP212.9

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