本文選題：智能移動終端 + 室內(nèi)定位　； 參考：《浙江大學》2017年碩士論文

【摘要】：近年來,基于室內(nèi)位置的服務需求變得越來越強烈�；诼曅盘柕氖覂�(nèi)定位技術(shù),由于具有兼容性好、穩(wěn)定性和定位精度高,使其成為近幾年的前沿研究課題。當前大多數(shù)的智能移動終端均具有揚聲器和麥克風,這使得此類系統(tǒng)極易在實際環(huán)境中得到應用與推廣。但實際上在真實的室內(nèi)環(huán)境中,存在人員走動、家具遮擋、墻壁反射等等復雜的因素,使得聲源與接收器之間的直接路徑被遮擋,稱為非視距(Non-Line-of-Sight,NLOS)。由于距離的量測依賴于聲信號的時延估計,NLOS傳播往往會給距離的估計帶來較大誤差,使得傳統(tǒng)基于視距(Line-of-Sight,LOS)環(huán)境下提出的定位算法失效,導致定位精度急速下降。本文針對室內(nèi)NLOS/LOS復雜環(huán)境,提出了一種基于NLOS判別的非視距定位方法,以及基于聲信號信道統(tǒng)計特征NLOS識別的定位算法。本文的主要工作內(nèi)容和貢獻包含以下幾個方面:首先,基于室內(nèi)聲音傳播模型,對LOS環(huán)境和NLOS環(huán)境下聲音信號信道特性進行研究。針對室內(nèi)復雜環(huán)境中的強多徑傳輸以及多普勒頻移等因素,提出了基于互相關(guān)的聲信道相對參數(shù)估計方法(相對時延及相對增益),降低了多普勒效應的影響,降低了計算復雜度。并提出一種基于信噪比(SNR)的自適應閾值函數(shù),對聲信號第一徑到達時刻進行判別,降低了多徑傳輸?shù)挠绊�。其�?基于聲信道參數(shù)估計,對信道特性的特征提取進行研究,包括時延特性、波形形狀特性、RicianK系數(shù),以及增益的幅值分布特性,共9個用于NLOS識別的特征值,利用基于支持向量機的分類器,實現(xiàn)對NLOS信號的識別分類,并對其核函數(shù)及特征值組合進行了最優(yōu)選取。最后,在NLOS信號識別的基礎(chǔ)上,針對靜態(tài)目標的NLOS定位,提出了基于NLOS識別剔除的定位策略和基于NLOS后驗概率的加權(quán)最小二乘定位策略。針對移動目標提出了基于NLOS識別的修正卡爾曼濾波追蹤算法和基于NLOS后驗概率修正卡爾曼濾波追蹤算法,以實現(xiàn)室內(nèi)NLOS/LOS混合環(huán)境下的魯棒定位追蹤。
[Abstract]:In recent years, the service demand based on indoor location has become more and more intense. Because of its good compatibility, high stability and high positioning accuracy, acoustic signal based indoor positioning technology has become a frontier research topic in recent years. At present, most intelligent mobile terminals have loudspeakers and microphones, which makes such systems easy to be applied and popularized in real environment. But in the real indoor environment, there are complicated factors such as walking of personnel, furniture occlusion, wall reflection and so on, which make the direct path between the sound source and the receiver blocked, which is called Non-Line-of-SightNLOS (Non-Line-of-SightNLOS). Because the measurement of distance depends on the time delay estimation of acoustic signal, NLOS propagation often brings great error to the estimation of distance, which makes the traditional localization algorithm based on Line-of-SightLos environment invalid, resulting in the rapid decline of location accuracy. In this paper, a non-line-of-sight location method based on NLOS discrimination and a localization algorithm based on the statistical characteristics of acoustic signal channel NLOS are proposed for the indoor NLOSP-Los complex environment. The main contents and contributions of this paper are as follows: firstly, based on the indoor sound propagation model, the channel characteristics of acoustic signals in Los and NLOS environments are studied. In view of the factors such as strong multipath transmission and Doppler frequency shift in complex indoor environment, a cross-correlation-based method for estimating the relative parameters of acoustic channels (relative delay and relative gain) is proposed, which reduces the influence of Doppler effect. The computational complexity is reduced. An adaptive threshold function based on signal-to-noise ratio (SNR) is proposed to distinguish the first arrival time of acoustic signal and reduce the influence of multipath transmission. Secondly, based on the estimation of acoustic channel parameters, the characteristic extraction of channel characteristics is studied, including delay characteristic, waveform shape characteristic and RicianK coefficient, as well as amplitude distribution characteristics of gain. Nine characteristic values are used for NLOS recognition. A classifier based on support vector machine (SVM) is used to realize the recognition and classification of NLOS signals, and the combination of kernel function and eigenvalue is selected optimally. Finally, on the basis of NLOS signal recognition, the location strategy based on NLOS recognition and elimination and the weighted least square location strategy based on NLOS posteriori probability are proposed for NLOS localization of static targets. A modified Kalman filter tracking algorithm based on NLOS recognition and a modified Kalman filter tracking algorithm based on NLOS posteriori probability are proposed to achieve robust location tracking in NLOS- Los hybrid environment.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN912.3

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本文編號：2087696