【摘要】：隨著無線通信技術(shù)的發(fā)展和社會的進(jìn)步，人們對無線定位服務(wù)的需求越來越大。衛(wèi)星導(dǎo)航定位是一種傳統(tǒng)的定位方式，通常能夠?qū)κ彝鈭鼍跋碌亩ㄎ惶峁┙鉀Q方案，但在室內(nèi)等建筑物遮蔽嚴(yán)重的場景下無法完成定位，所以室內(nèi)定位問題在近年來成為一個越來越熱門的研究方向，其中，基于WLAN的室內(nèi)定位技術(shù)應(yīng)運而生，發(fā)展日趨成熟。本文提出一種基于WLAN指紋定位和慣性測量的室內(nèi)定位系統(tǒng)設(shè)計方案，并實現(xiàn)了基于Android手機(jī)終端的室內(nèi)定位系統(tǒng)。 本文提出的室內(nèi)定位系統(tǒng)設(shè)計方法融合WLAN指紋定位的結(jié)果和慣性測量的結(jié)果，并在指紋定位技術(shù)中采用終端獨立定位和網(wǎng)絡(luò)側(cè)定位兩種模式，在慣性測量中提出了一種基于加速度向量歐式距離的靜止運動判斷方法，判斷當(dāng)前行人的靜止運動狀態(tài)，在靜止時對指紋定位結(jié)果進(jìn)行均值濾波處理，之后，，提出了基于加速度峰值檢測的步態(tài)檢測算法，以及步長估計算法，采用航跡推算的方式得到用戶的計步狀態(tài)。進(jìn)一步，在指紋定位結(jié)果和慣性測量結(jié)果的融合方面，提出了一種基于擴(kuò)展Kalman濾波的融合算法。最后，在系統(tǒng)實現(xiàn)方面本文給出了定位系統(tǒng)的總體設(shè)計架構(gòu)，重點對基于Android手機(jī)終端的定位軟件設(shè)計做了闡述。 本文在系統(tǒng)設(shè)計和實現(xiàn)的基礎(chǔ)上對系統(tǒng)進(jìn)行了測試及性能分析。測試結(jié)果表明，在本文的實驗場景下，采用K值為4的WKNN算法得到的指紋定位精度較高，誤差累計概率在1處的定位誤差為1.3米左右，通過引入慣性測量的方法，當(dāng)判斷行人處于靜止?fàn)顟B(tài)時對指紋定位結(jié)果進(jìn)行均值濾波，精度在誤差累計概率處有0.2米左右的提高。在運動情況下，指紋定位加入慣性測量并進(jìn)行擴(kuò)展卡爾曼濾波融合算法后得到的定位軌跡相比單純使用指紋定位算法的軌跡更接近真實行人軌跡，驗證了基于WLAN指紋定位和慣性測量的定位系統(tǒng)的可行性和系統(tǒng)定位性能的優(yōu)越性。
[Abstract]:With the development of wireless communication technology and social progress, the demand for wireless location services is increasing. Satellite navigation and positioning is a traditional positioning method, which can usually provide a solution for outdoor location, but it can not be completed in the severely sheltered environment such as indoor buildings. Therefore, indoor positioning has become a hot research direction in recent years. Among them, the indoor positioning technology based on WLAN emerges as the times require, and the development is maturing day by day. This paper presents a design scheme of indoor positioning system based on WLAN fingerprint location and inertial measurement, and realizes the indoor positioning system based on Android mobile terminal. The indoor positioning system design method proposed in this paper combines the results of WLAN fingerprint location and inertial measurement, and adopts two modes of terminal independent positioning and network side positioning in fingerprint positioning technology. In inertial measurement, a method of judging static motion based on the Euclidean distance of acceleration vector is proposed. A gait detection algorithm based on peak acceleration detection and a step size estimation algorithm are proposed. Furthermore, a fusion algorithm based on extended Kalman filter is proposed for the fusion of fingerprint location results and inertial measurement results. Finally, in the aspect of system implementation, this paper gives the overall design framework of the positioning system, focusing on the design of location software based on Android mobile phone terminal. Based on the design and implementation of the system, this paper tests and analyzes the performance of the system. The test results show that the WKNN algorithm with K value of 4 has a high precision of fingerprint location, and the error accumulative probability is about 1. 3 meters. The method of inertial measurement is introduced. When judging that the pedestrian is in a static state, the fingerprint location results are filtered by the mean value, and the accuracy is improved by about 0.2 meters at the cumulative error probability. In the case of motion, the trajectory of fingerprint location is closer to the real pedestrian track than that of using the fingerprint localization algorithm, which is based on the inertial measurement and extended Kalman filter fusion algorithm. The feasibility and superiority of the positioning system based on WLAN fingerprint location and inertial measurement are verified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN925.93

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本文編號：2405083