Improved K-Means Algorithm for Nearby Target Localization
In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources,...
Saved in:
Main Authors: | Zongwen Yuan, Xingdi Wang, Fuyang Chen, Xicheng Ma |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10714343/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
K-Means Clustering with Local Distance Privacy
by: Mengmeng Yang, et al.
Published: (2023-12-01) -
User Segmentation Based on Purchasing Habits and Preferences on the Amazon Platform Using K-Means Clustering
by: Al Isra Denk Rimakka, et al.
Published: (2023-12-01) -
A study on the assessment of learner ability based on the combination of auto-encoder and quadratic k-means clustering algorithm
by: Junfeng Man, et al.
Published: (2024-12-01) -
Analisis Klaster Untuk Hubungan Antara Kemampuan Komunikasi Matematis Dengan Kemampuan Pemecahan Masalah Menggunkan K-Means
by: Devi Dwitasari, et al.
Published: (2024-09-01) -
Clustering OKU Timur Script Images using VGG Feature extraction and K-Means
by: Liu Toriko, et al.
Published: (2024-12-01)