基於校正之藍牙指紋室內定位;Calibration-based Bluetooth Fingerprinting Indoor Localization

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题名:	基於校正之藍牙指紋室內定位;Calibration-based Bluetooth Fingerprinting Indoor Localization
作者:	梁惠淞;Liang, Hui-song
贡献者:	資訊工程學系
关键词:	自動編碼器;主成分分析;室內定位;信標;低耗能藍牙;信標;Autoencoder;Principal Component Analysis;Indoor Localization;Fingerprint Localization method;Bluetooth Low Energy;Beacon
日期:	2020-08-17
上传时间:	2020-09-02 18:06:21 (UTC+8)
出版者:	國立中央大學
摘要:	本論文提出命名為FPFE (Fingerprint Feature Extraction)及FPFE-C(Fingerprint Feature Extraction with Calibration)的室內定位 (indoor localization)方法。這二個方法利用參考點(reference point)的低耗能藍芽(Bluetooth Low Energy, BLE) 信標指紋(beacon fingerprint)進行定位，會在室內環境中佈置4個以上的信標節點(beacon node)，而這些信標節點會週期性持續送出廣告(advertisement)封包。我們可以從不同已知位置的參考點接收這些廣告封包並記錄其接收訊號強度(Received Signal Strength Indicator, RSSI)，將這些接收訊號強度當作每個參考點的個別信標指紋，做為定位資料之用。FPFE定位法先使用自動編碼器或主成份分析進行信標指紋特徵擷取，然後再進行參考點與位置未知之目標點(target point)的特徵相似程度比較，挑選出特徵相似度較高的參考點之後以其位置平均權重計算出目標點的位置。每次進行定位時，可能受到溫度、濕度、場地不同與設備不同等各種環境因素影響，因此FPFE-C定位法另外挑選若干參考點為校正點(calibration point)，透過每次重新測量校正點的信標指紋，並計算其與相對應參考點原始信標指紋的比值之後再對定位資料進行校正，達到更好的定位精準度。若其他使用者欲使用本論文所提的定位方法時，便可以不必再收集所有參考點的信標指紋，只需透過FPFE-C方法所使用的校正方式，微調定位資料即可。本研究以長為8公尺，寬為5公尺的範圍作為實驗區域，在區域四周佈置信標節點，透過大量實驗資料評估所提方法的定位精準度。實驗結果顯示，FPFE定位法的平均定位誤差為0.68公尺；而在具有環境變化影響之校正定位實驗中，FPFE定位法的平均定位誤差為2.13公尺，當加上校正點後之FPFE-C定位法之平均定位誤差為1.63公尺，下降了0.4公尺。;The study proposes two indoor localization methods named FPFE (Fingerprint Feature Extraction) and FPFE-C (Fingerprint Feature Extraction with Calibration). These two methods use Bluetooth Low Energy (BLE) beacon fingerprints of reference points for the purpose of localization. Four or more beacon nodes are deployed in an indoor environment which periodically and continuously broadcast advertisement packets. We can measure Received Signal Strength Indicator (RSSI) values of packets of different beacon nodes for every reference point and take the combination of the RSSI values as the beacon fingerprint of the reference point with a known position. Similarly, we can obtain the beacon fingerprint of a target point with an unknown position. The FPFE method first uses the autoencoder or the principal component analysis (PCA) to extract features of beacon fingerprints, and then calculate the Minkowski distances between the feature of the target point and the features of all reference points. The FPFE method then selects k reference points with the k smallest Minkowski distances and use their positions to estimate the target point position. The FPFE-C method also uses the similar concept adopted by the FPFE method for positioning. However, the FPFE-C method additionally considers dynamically changing environmental factors. It chooses few reference points as calibration points to re-measure their beacon fingerprints periodically. The re-measurement is also conducted when there are drastic environmental changes, such as the change of packet-receiving devices, significant changes of surrounding temperature and humidity, or and even when the whole localization system is moved and applied in a brand-new place. The average ratios of beacon fingerprints of calibration points and corresponding reference points are used to adjust all beacon fingerprints. This study takes an 8 m by 5 m region with four beacon nodes deployed at four corners for evaluating the performance of the proposed methods. With 187 reference points, the FPFE method achieves the average positioning error of 0.68 m. For the scenario with different packet receiving devices, the FPFE-C method reduces the positioning error from 2.13 m to 1.63 m with 12 calibration points.
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