【摘要】：隨著人們活動(dòng)的室內(nèi)空間越來(lái)越龐大和復(fù)雜,興趣點(diǎn)(Point of Interest,POI)越來(lái)越豐富,停車(chē)場(chǎng)、商場(chǎng)、機(jī)場(chǎng)等場(chǎng)所的定位和導(dǎo)航需求愈趨強(qiáng)烈。此外,精準(zhǔn)位置營(yíng)銷(xiāo)、智能制造、機(jī)器人、無(wú)人醫(yī)療護(hù)理等行業(yè)也需要設(shè)備能夠在室內(nèi)識(shí)別特定對(duì)象的位置。這些都為室內(nèi)定位技術(shù)(Indoor Positioning System,IPS)帶來(lái)了巨大的機(jī)會(huì)。調(diào)查數(shù)據(jù)顯示,人們?cè)谑覂?nèi)度過(guò)的時(shí)間占比達(dá)到80%以上。由于室內(nèi)環(huán)境日趨復(fù)雜,空間越來(lái)越大,在停車(chē)場(chǎng)找車(chē)、逛商場(chǎng)找尋特定商品、聯(lián)系走散的親朋變得越來(lái)越難,這些問(wèn)題推動(dòng)了室內(nèi)定位成為生活中的剛需。目前室內(nèi)定位技術(shù)呈現(xiàn)百家爭(zhēng)鳴的現(xiàn)象,卻缺少一種定位技術(shù)能夠在低成本的條件下滿(mǎn)足位置服務(wù)的定位需求。超寬帶定位、激光定位、紅外定位、地磁定位等技術(shù)或需要專(zhuān)門(mén)的設(shè)備,或部署復(fù)雜、成本較高,難以實(shí)現(xiàn)大規(guī)模的推廣。而基于Wi-Fi的指紋定位技術(shù)能直接利用場(chǎng)景中的現(xiàn)有設(shè)備,極大地減輕了定位系統(tǒng)部署的成本;藍(lán)牙4.0的低功耗性、信號(hào)廣覆蓋性、低成本性也為這種新型的無(wú)線定位技術(shù)搭建好了舞臺(tái)。然而基于Wi-Fi或者低功耗藍(lán)牙的單模定位技術(shù)仍舊存在一定的局限性,由于室內(nèi)環(huán)境高度復(fù)雜,信號(hào)的反射、衍射和多徑效應(yīng)都給基于無(wú)線信號(hào)的定位技術(shù)帶來(lái)了很大困難;指紋定位技術(shù)在離線模型訓(xùn)練階段大多需要相當(dāng)豐富的訓(xùn)練樣本才能學(xué)習(xí)出較好的定位模型,而更多的訓(xùn)練樣本則意味著更長(zhǎng)的模型學(xué)習(xí)時(shí)間和更大的數(shù)據(jù)采集工作量。針對(duì)上述問(wèn)題,本文先后從指紋特征穩(wěn)定性、模型訓(xùn)練的快速性和訓(xùn)練樣本獲取的便捷性以及最終的定位結(jié)果的有效性等方面著手,全文主要工作可分為以下三部分:1)基于互相關(guān)理論提出了一種融合特征提取方法。該方法首先對(duì)原始傳感器信號(hào)采用高斯模型進(jìn)行去噪處理,然后根據(jù)互相關(guān)理論進(jìn)行互相關(guān)信息計(jì)算得到融合特征,最后再結(jié)合原始傳感器特征得到組合特征。實(shí)驗(yàn)表明,該方法能有效提升指紋特征的穩(wěn)定性和定位模型的精度和魯棒性。2)提出了一種基于融合特征的半監(jiān)督流形約束定位方法。首先引入超限學(xué)習(xí)機(jī)以提升模型的學(xué)習(xí)速度和泛化能力,然后引入半監(jiān)督學(xué)習(xí)方法,采用拉普拉斯正則化來(lái)對(duì)模型進(jìn)行流形約束,充分吸收無(wú)標(biāo)記樣本的數(shù)據(jù)特征,同時(shí)減少此類(lèi)樣本對(duì)模型的負(fù)面影響,從而增強(qiáng)模型的定位精度和魯棒性。實(shí)驗(yàn)表明,半監(jiān)督超限學(xué)習(xí)機(jī)的提出最多能將標(biāo)定指紋的采集工作量減少90%,同時(shí)能將定位精度提升20%-30%。3)設(shè)計(jì)并實(shí)現(xiàn)了一種融合定位系統(tǒng)。該系統(tǒng)針對(duì)第三方應(yīng)用開(kāi)發(fā)者提供了一種快速集成室內(nèi)定位功能的服務(wù)。開(kāi)發(fā)者利用本定位系統(tǒng)的采集工具在定位場(chǎng)景中采集指紋后,再使用離線定位SDK(Software Development Kit)便可以快速體驗(yàn)室內(nèi)定位功能。系統(tǒng)測(cè)試和軌跡重現(xiàn)結(jié)果表明我們?cè)O(shè)計(jì)的室內(nèi)定位系統(tǒng)具有很好的實(shí)用性,也具備很好的商業(yè)價(jià)值。
[Abstract]:With the more and more large and complex indoor space of people's activities, the (Point of Interest,POI) is becoming more and more abundant, and the demand for positioning and navigation in parking lots, shopping malls, airports and other places is becoming more and more intense. In addition, industries such as precision location marketing, intelligent manufacturing, robotics, and unattended medical care also require equipment to identify the location of specific objects indoors. All these bring great opportunities for indoor positioning technology (Indoor Positioning System,IPS). Survey data show that people spend more than 80% of the time indoors. Because the indoor environment is becoming more and more complex and the space is more and more large, it is becoming more and more difficult to find cars in the parking lot, shopping malls to find specific goods, and to contact separated friends and relatives. These problems have pushed indoor positioning to become a rigid demand in daily life. At present, the indoor positioning technology presents the phenomenon of a hundred schools of thought, but the lack of a positioning technology can meet the location needs of location services under the condition of low cost. UWB positioning, laser positioning, infrared positioning, geomagnetic positioning and other technologies may require special equipment, or the deployment of complex, high cost, it is difficult to achieve large-scale promotion. The fingerprint location technology based on Wi-Fi can directly utilize the existing equipment in the scene, which greatly reduces the cost of location system deployment, the low power consumption of Bluetooth 4.0, the wide coverage of the signal, Low-cost also set the stage for this new wireless positioning technology. However, the single-mode localization technology based on Wi-Fi or low-power Bluetooth still has some limitations. Because of the high complexity of indoor environment, the reflection of signals, diffraction and multipath effect, the localization technology based on wireless signal is very difficult. Fingerprint localization technology in the off-line model training stage most of the training samples to learn a better location model, and more training samples mean longer model learning time and more data acquisition workload. In order to solve the above problems, this paper begins with the stability of fingerprint features, the rapidity of model training, the convenience of obtaining training samples, and the effectiveness of the final localization results. The main work of this paper can be divided into three parts: 1) A fusion feature extraction method based on cross-correlation theory is proposed. Firstly, the original sensor signal is de-noised by Gao Si model, then the fusion feature is obtained by cross-correlation information calculation based on the cross-correlation theory, and the combined feature is obtained by combining the original sensor feature. Experiments show that this method can effectively improve the stability of fingerprint features and the accuracy and robustness of the location model. 2) A semi-supervised manifold constrained location method based on fusion features is proposed. In order to improve the learning speed and generalization ability of the model, the over-limit learning machine is introduced first, and then the semi-supervised learning method is introduced. Laplacian regularization is used to constrain the model manifold, which fully absorbs the data features of unlabeled samples. At the same time, the negative effects of the samples on the model are reduced, so that the location accuracy and robustness of the model are enhanced. Experimental results show that the proposed semi-supervised over-limit learning machine can reduce the workload of fingerprint calibration by 90% at most, and at the same time, it can improve the positioning accuracy by 20- 30.3) and design and implement a fusion positioning system. The system provides a fast integrated indoor location service for third party application developers. The developer uses the acquisition tool of the positioning system to collect fingerprints in the location scene, and then use the off-line positioning SDK (Software Development Kit) to quickly experience the indoor positioning function. The results of system test and trajectory reconstruction show that the indoor positioning system designed by us has good practicability and commercial value.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN92

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 劉軍發(fā);谷洋;陳益強(qiáng);曹_g姝;;具有時(shí)效機(jī)制的增量式無(wú)線定位方法[J];計(jì)算機(jī)學(xué)報(bào);2013年07期

2 李娟娟;張金藝;張秉煜;周榮俊;唐夏;;藍(lán)牙4.0標(biāo)準(zhǔn)規(guī)范下的模糊指紋定位算法[J];上海大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年02期

3 陳國(guó)平;馬耀輝;張百珂;;基于指紋技術(shù)的藍(lán)牙室內(nèi)定位系統(tǒng)[J];電子技術(shù)應(yīng)用;2013年03期

4 徐金茍;;低能耗藍(lán)牙4.0協(xié)議原理與實(shí)現(xiàn)方法[J];微型電腦應(yīng)用;2012年10期

5 王琦;;基于RSSI測(cè)距的室內(nèi)定位技術(shù)[J];電子科技;2012年06期

6 張浩;趙千川;;藍(lán)牙手機(jī)室內(nèi)定位系統(tǒng)[J];計(jì)算機(jī)應(yīng)用;2011年11期

7 王睿;趙方;彭金華;羅海勇;陸波;陸濤;;基于WI-FI和藍(lán)牙融合的室內(nèi)定位算法[J];計(jì)算機(jī)研究與發(fā)展;2011年S2期

8 江德祥;胡明清;陳益強(qiáng);劉軍發(fā);周經(jīng)野;;基于核嶺回歸的自適應(yīng)藍(lán)牙定位方法[J];計(jì)算機(jī)應(yīng)用研究;2010年09期

9 盧恒惠;劉興川;張超;林孝康;;基于三角形與位置指紋識(shí)別算法的WiFi定位比較[J];移動(dòng)通信;2010年10期

相關(guān)碩士學(xué)位論文 前3條

1 董曉瑞;基于ZigBee定位系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)[D];哈爾濱理工大學(xué);2011年

2 王小好;基于藍(lán)牙無(wú)線技術(shù)的室內(nèi)定位方法的研究[D];浙江工業(yè)大學(xué);2007年

3 田靜;超寬帶系統(tǒng)中定位技術(shù)的研究[D];吉林大學(xué);2007年

，

本文編號(hào)：2243104