.png){kind=icon}
返回
Robust Localization Method Based on Non-Parametric Probability Density Estimation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3287140.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents robust localization techniques that calculate location using distance observations. In enclosed and heavily populated urban environments, the positive measurement bias introduced by a non-line-of-sight signal can have a considerable adverse impact on estimation performance. Therefore, to mitigate the detrimental effects of the multipath effect caused by the non-line-of-sight signal, robust localization techniques are considered. In particular, the ${k}$ -nearest neighbor (KNN)-based and orthogonal series (OSERIES)-based localization approaches are proposed. The difference from conventional probability density estimation (PDF) estimation methods is that the proposed methods use the first-peak information of the estimated PDF to obtain the actual distance information, not just the PDF shape estimation. More specifically, the proposed methods use the mean calculated from observations selected by statistical testing because the mean estimate generally outperforms the mode estimate. In addition, the Rao test in the context of the two-mode Gaussian mixture model (GMM) is demonstrated to be uniformly most powerful (UMP) test. Furthermore, the conditional variance of the range measurement is derived. Also, the proposed techniques outperforms that of competing algorithms in terms of localization accuracy.
Keyword:
Location awareness
Estimation
Testing
Probability density function
Maximum likelihood estimation
Computational modeling
Measurement uncertainty
First peak
Gaussian mixture model
k-nearest neighbor
localization
non-line-of-sight
orthogonal series
probability density function
weighted least squares
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏分享收藏Accurate Localization of Multiple Sources Using Semidefinite Programming Based on Incomplete Range Matrix基于不完全距离矩阵的半定规划多源精确定位.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices在计算受限的设备上使用卷积神经网络改进室内定位IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容