基於聚類演算法分析之提高室內定位信號精度模型

A Model of Improving Indoor Positioning Signal Accuracy Based on Clustering Algorithm Analysis

成航(CHENG, HANG)

指導教授 ： 曾俊元

國立臺北大學/電機資訊學院/資訊工程學系/碩士(2016年)

摘要

基於接收信號強度（RSSI）的測距方法是一項低成本、低複雜度的距離測量技術。然而，信號強度在接收過程會產生誤差，尤其是多徑衰落等長時間干擾，對定位精度會造成一定的負面影響，為消除RSSI測量誤差對定位精度的影響，本文提出一種基於聚類演算法分析的RSSI信號處理優化策略，通過構造高斯混合模型，運用赤池信息量準則評估，消除多徑干擾， 提高未知節點室內定位精度。實地實驗使用基於BLE 4.0的iBeacon節點，結果證明該方法可以有效改善系統的定位精度，在非對稱定位環境下比傳統基於高斯濾波的RSSI信號處理優化策略的定位演算法精度提高了35.6%，且定位平均誤差基本不超過0.5m，可適應室內定位精度要求。

關鍵字

接收信號強度 ； 高斯混合模型 ； 赤池信息量準則 ； 室內定位精度

並列摘要

Measuring the distance method based on the received signal strength Indicator (RSSI) is a low cost distance measurement technology with low complexity. However, RSSI values may contain measurement errors especially caused by multipath decline long interference. These measurement RSSI errors can cause negative effects and weaken the positioning accuracy. Therefore, we propose a based on a model to improve the accuracy of RSSI values. The model utilizes Gaussian mixture model to generate K components of RSSI values, adapts Akaike information criterion (AIC) to determine the best K value, and thus generate the optimized RSSI average value without the negative effects of multipath decline long interference. The experiment results are generated by iBeacon nodes and show that this model can effectively improve RSSI measurement accuracy of 35.6% better than the traditional Gaussian filter positioning algorithm, and the average positioning error is less than 0.5m, which is adequate to indoor positioning accuracy.

並列關鍵字

Received Signal Strength Indicator(RSSI) ； Gaussian mixture model(GMM) ； Akaike information criterion (AIC) ； indoor positioning accuracy

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