首页|RSSI室内定位在线匹配算法的研究与性能比较

RSSI室内定位在线匹配算法的研究与性能比较

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针对在基于WiFi信号强度RSSI进行室内定位的指纹库算法的在线匹配环节中存在的不足,该文利用基于阈值R0 动态筛选匹配的指纹点数,提出了一种增强加权k近邻算法(EWKNN).因为阈值R0 可以动态筛选指纹库中的样本点,所以能够提高增强加权k近邻算法的适用度和高精度.仿真结果表明:在R0 设置恰当的情况下,增强加权k近邻算法的计算量与加权k近邻算法(WKNN)相当,但定位精度更高.
The Research and Performance Comparison of RSSI Indoor Positioning Online Matching Algorithms
Focused on the online matching part in fingerprint database algorithm for indoor positioning based on WiFi signal strength RSSI,the enhanced weight k-nearest method is proposed by dynamically selecting the matching fingerprint points based on the threshold R0.The effectiveness of the enhanced weighted k-nearest neighbors algo-rithm(EWKNN)stems from the threshold,because the value of R0 can dynamically filter the sample points in the fingerprint library,which is an improvement on the weight k-nearest neighbors algorithm.The result of the simulation shows that under the appropriate setting of R0,the amount of calculation of the enhanced weight k-nearest neighbors algorithm(EWKNN)is comparable to the weighted k-nearest neighbor algorithm(WKNN),but the positioning accu-racy is higher.

indoor positioningthe online matching of fingerprint database algorithmenhanced weight k-nearest neighborhood algorithmweight k-nearest neighborhood algorithmcumulative distribution function

吴之宁、汪学刚、邹林

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电子科技大学信息与通信工程学院,四川 成都 611730

室内定位 指纹库在线匹配 增强加权k近邻算法 加权k近邻算法 累积分布函数

国家自然科学基金重大仪器专项

42027805

2024

10.16357/j.cnki.issn1000-5862.2024.01.10

江西师范大学学报(自然科学版)
江西师范大学

江西师范大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.538
ISSN：1000-5862
年,卷(期)：2024.48(1)
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