本文選題：室內(nèi)定位 + 位置指紋��； 參考：《蘭州交通大學》2017年碩士論文

【摘要】：室內(nèi)定位技術是指在室內(nèi)環(huán)境下,通過使用基站定位,慣性輔助定位等多種定位技術一起形成一套適用于室內(nèi)的位置定位方法,實現(xiàn)室內(nèi)環(huán)境下的位置感知。隨著無線通信技術的快速發(fā)展,人們在日常生活中對于室內(nèi)基于位置信息服務的需求越來越強烈,而高精度的室內(nèi)定位技術是獲取位置信息的關鍵,這使得高精度的室內(nèi)定位技術成為近年來的熱點課題。由于藍牙4.0具有功耗低、成本低、便于部署的特點,本文使用其作為無線技術基礎,結(jié)合有代表性的室內(nèi)位置指紋定位技術進行研究和優(yōu)化。室內(nèi)位置指紋定位技術主要有兩個階段:信息庫建立階段和實時定位階段。為了提高算法性能,傳統(tǒng)優(yōu)化方法先對信息庫的位置信息進行聚類,然后在定位階段使用K近鄰(KNN)算法進行匹配,保證定位精度的同時減少算法計算量。本文針對傳統(tǒng)基于聚類的位置指紋定位方法中使用的模糊C均值(FCM)算法存在的缺陷,如對初始值敏感,易陷入局部最優(yōu)解等,引入了粒子群優(yōu)化算法,先求解FCM算法的初始值,之后進行FCM聚類,這樣避免了算法陷入局部最優(yōu)解,提高了算法的定位精度和魯棒性。同時,針對信息庫建立階段的采樣成本問題,采用重心拉格朗日插值算法進行優(yōu)化,在保證算法精度的同時降低采樣成本;在實時定位階段,采用模糊決策的方法,避免了KNN算法造成的誤差和計算冗余。最后,本文通過Matlab平臺進行了優(yōu)化算法的總體設計及實驗。實驗對比了優(yōu)化前后定位算法的性能,包括信息庫建立階段中優(yōu)化前后的插值算法對定位性能的影響,優(yōu)化前后聚類算法性能對比,優(yōu)化前后定位算法的性能對比。根據(jù)本文實驗結(jié)果分析,改進后的位置指紋定位方法在定位準確度和定位實時性上都有了較好的改善。增加插值算法后,也能在較好地保證精度的同時顯著減少采樣成本。
[Abstract]:Indoor positioning technology refers to the indoor environment, through the use of base station positioning, inertial auxiliary positioning and other positioning techniques together to form a set of indoor location methods, to achieve indoor location perception. With the rapid development of wireless communication technology, people need more and more indoor location-based information service in daily life, and high-precision indoor positioning technology is the key to obtain location information. This makes high-precision indoor positioning technology become a hot topic in recent years. Because Bluetooth 4.0 has the characteristics of low power consumption, low cost and easy to deploy, this paper uses Bluetooth 4.0 as the wireless technology foundation, combined with the representative indoor location fingerprint location technology to study and optimize. There are two main phases of indoor location fingerprint location: information base establishment and real-time location. In order to improve the performance of the algorithm, the traditional optimization methods first cluster the location information of the information base, and then use the K-nearest neighbor KNN algorithm to match in the localization stage to ensure the accuracy of the location and reduce the computational complexity of the algorithm. Aiming at the shortcomings of the fuzzy C-means FCM algorithm used in the traditional location fingerprint location method based on clustering, such as being sensitive to the initial value and easy to fall into the local optimal solution, the particle swarm optimization algorithm is introduced to solve the initial value of the FCM algorithm. Then FCM clustering is carried out, which avoids the algorithm falling into local optimal solution, and improves the location accuracy and robustness of the algorithm. At the same time, aiming at the cost of sampling in the stage of information base establishment, the Lagrange interpolation algorithm of gravity center is used to optimize the algorithm to ensure the accuracy of the algorithm and to reduce the sampling cost, and in the real-time positioning stage, the fuzzy decision method is adopted. The error caused by KNN algorithm and computational redundancy are avoided. Finally, the overall design and experiment of the optimization algorithm are carried out on the Matlab platform. The performance of the pre-and post-optimization localization algorithm is compared, including the influence of the pre-and post-optimization interpolation algorithm on the location performance, the performance comparison of the pre-and post-optimization clustering algorithm, and the performance comparison of the pre-and post-optimization localization algorithm. According to the experimental results of this paper, the improved location fingerprint localization method has better accuracy and real time. After adding the interpolation algorithm, the sampling cost can be significantly reduced while the precision is better guaranteed.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN925

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