Wi-Fi藍牙融合定位方法研究與系統(tǒng)實現(xiàn)
發(fā)布時間：2018-09-14 15:26
【摘要】：隨著人們活動的室內(nèi)空間越來越龐大和復雜,興趣點(Point of Interest,POI)越來越豐富,停車場、商場、機場等場所的定位和導航需求愈趨強烈。此外,精準位置營銷、智能制造、機器人、無人醫(yī)療護理等行業(yè)也需要設備能夠在室內(nèi)識別特定對象的位置。這些都為室內(nèi)定位技術(Indoor Positioning System,IPS)帶來了巨大的機會。調(diào)查數(shù)據(jù)顯示,人們在室內(nèi)度過的時間占比達到80%以上。由于室內(nèi)環(huán)境日趨復雜,空間越來越大,在停車場找車、逛商場找尋特定商品、聯(lián)系走散的親朋變得越來越難,這些問題推動了室內(nèi)定位成為生活中的剛需。目前室內(nèi)定位技術呈現(xiàn)百家爭鳴的現(xiàn)象,卻缺少一種定位技術能夠在低成本的條件下滿足位置服務的定位需求。超寬帶定位、激光定位、紅外定位、地磁定位等技術或需要專門的設備,或部署復雜、成本較高,難以實現(xiàn)大規(guī)模的推廣。而基于Wi-Fi的指紋定位技術能直接利用場景中的現(xiàn)有設備,極大地減輕了定位系統(tǒng)部署的成本;藍牙4.0的低功耗性、信號廣覆蓋性、低成本性也為這種新型的無線定位技術搭建好了舞臺。然而基于Wi-Fi或者低功耗藍牙的單模定位技術仍舊存在一定的局限性,由于室內(nèi)環(huán)境高度復雜,信號的反射、衍射和多徑效應都給基于無線信號的定位技術帶來了很大困難;指紋定位技術在離線模型訓練階段大多需要相當豐富的訓練樣本才能學習出較好的定位模型,而更多的訓練樣本則意味著更長的模型學習時間和更大的數(shù)據(jù)采集工作量。針對上述問題,本文先后從指紋特征穩(wěn)定性、模型訓練的快速性和訓練樣本獲取的便捷性以及最終的定位結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)基于互相關理論提出了一種融合特征提取方法。該方法首先對原始傳感器信號采用高斯模型進行去噪處理,然后根據(jù)互相關理論進行互相關信息計算得到融合特征,最后再結合原始傳感器特征得到組合特征。實驗表明,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。首先引入超限學習機以提升模型的學習速度和泛化能力,然后引入半監(jiān)督學習方法,采用拉普拉斯正則化來對模型進行流形約束,充分吸收無標記樣本的數(shù)據(jù)特征,同時減少此類樣本對模型的負面影響,從而增強模型的定位精度和魯棒性。實驗表明,半監(jiān)督超限學習機的提出最多能將標定指紋的采集工作量減少90%,同時能將定位精度提升20%-30%。3)設計并實現(xiàn)了一種融合定位系統(tǒng)。該系統(tǒng)針對第三方應用開發(fā)者提供了一種快速集成室內(nèi)定位功能的服務。開發(fā)者利用本定位系統(tǒng)的采集工具在定位場景中采集指紋后,再使用離線定位SDK(Software Development Kit)便可以快速體驗室內(nèi)定位功能。系統(tǒng)測試和軌跡重現(xiàn)結果表明我們設計的室內(nèi)定位系統(tǒng)具有很好的實用性,也具備很好的商業(yè)價值。
[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.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN92
本文編號：2243104
[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.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN92
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