L1-norm constraint kernel adaptive filtering framework for precise and robust indoor localization under the internet of things
L1-norm constraint kernel adaptive filtering framework for precise and robust indoor localization under the internet of things
复制标题
L1范数约束内核自适应滤波框架，用于物联网下精确鲁棒的室内定位
DOI：
10.1016/j.ins.2021.12.026
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发表时间：
2021-12
影响因子：
8.1
通讯作者：
F Alenezi
中科院分区：
文献类型：
--
作者：
Zhao Xin;Li Xifeng;Bi Dongjie;Wang Haojie;Xie Yongle;A Alhudhaif;F Alenezi
室内环境中广泛存在着高斯噪声和突变噪声等混合噪声，这往往导致物联网环境下定位系统性能下降的问题。针对这一问题，提出了一种新的核函数--广义学生t核(GST)及其稀疏广义学生t核自适应滤波器(SGStKAF)。该算法采用了具有L 1范数惩罚的核平均p次方误差准则。拟议的SGStKAF有三个重要特征。首先，广义学生t核可以有效地抑制突变噪声。其次，L 1-范数罚函数保证了不动点子迭代是可行的，从而可以在几次迭代中得到更精确的解。最后，通过L 1的约束，也可以得到实现该方法所需的稀疏神经网络结构。通过三个实验和比较，证明了所提出的定位框架在模拟场景和真实室内环境下的准确性和鲁棒性。
The mixed noise such as Gaussian noise together with the abrupt noise widely exists in the indoor environment, which always leads to the problem of performance degradation of the positioning system under the Internet of Things (IoT). In this paper, a novel kernel function named generalized Student’s t kernel (GSt) and a resulting sparse generalized Student’s t kernel adaptive filter (SGStKAF) is proposed to attack this problem. The proposed SGStKAF utilizes the kernel mean p-power error criterion (KMPE) with the L 1-norm penalty. The proposed SGStKAF has three significant features. Firstly, the generalized Student’s t kernel can suppress the abrupt noise effectively. Secondly, the L 1-norm penalty guarantees that the fixed-point sub-iteration is available so that the more precise solution can be obtained in a few iterations. At last, a sparse structure of neural networks for the implementation of the proposed method can also be obtained via the L 1 constraint. Three experiments and comparisons are carried out to prove the effectiveness of the proposed positioning framework in terms of accuracy and robustness in both the simulation situation and the real-world indoor environments.
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DOI：
10.1007/3-540-60188-0_67
发表时间：
1995
期刊：
--
影响因子：
--
作者：
A. Weron;R. Weron
通讯作者：
A. Weron;R. Weron
影响因子：
11.8
作者：
Xiaoxuan Lu;Han Zou;Hongming Zhou;Lihua Xie;G. Huang
通讯作者：
Xiaoxuan Lu;Han Zou;Hongming Zhou;Lihua Xie;G. Huang
基于卡尔曼滤波器的 IMU 和 UWB 集成，用于高精度室内定位和导航
影响因子：
10.6
作者：
Feng, Daquan;Wang, Chunqi;Xia, Xiang-Gen
通讯作者：
Xia, Xiang-Gen
影响因子：
5.4
作者：
Chen, Badong;Xing, Lei;Principe, Jose C.
通讯作者：
Principe, Jose C.
影响因子：
5.4
作者：
Engel, Y;Mannor, S;Meir, R
通讯作者：
Meir, R