Simultaneous Source Number Detection and DOA Estimation Using Deep Neural Network and K2-Means Clustering with Prior Knowledge
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| Publicat a: | Electronics vol. 14, no. 4 (2025), p. 713 |
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| Autor principal: | |
| Altres autors: | , , , , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14040713 |2 doi | |
| 035 | |a 3171007085 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Liu, Aifei |u Yangtze River Delta Research Institute and and School of Software, Northwestern Polytechnical University, Taicang 215400, China; <email>zhouyuan153@mail.nwpu.edu.cn</email> (Y.Z.); <email>lizi@mail.nwpu.edu.cn</email> (Z.L.); <email>yxxie0721@mail.nwpu.edu.cn</email> (Y.X.) | |
| 245 | 1 | |a Simultaneous Source Number Detection and DOA Estimation Using Deep Neural Network and K2-Means Clustering with Prior Knowledge | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Source number detection and Direction-of-Arrival (DOA) estimation are usually addressed in two stages, leading to high computational load. This paper proposes a simple solution to efficiently estimate the source number and DOAs using deep neural network (DNN) and clustering, named DNN-C. By observing that sources in space are usually few, DNN-C uses a simple fully connected DNN to obtain a spatial spectrum. Then, the K2-means clustering is specially designed to extract the source information from the obtained spatial spectrum. In particular, to enable the proposed DNN-C with the ability to detect the mixed sources, we first develop a new strategy for training data generation, and provide a guideline for data balance setting. We then explore the prior knowledge of array signal processing and spatial spectrum to obtain a peak vector and propose to add a virtual peak into the peak vector, and thus transform the task of source detection as a binary clustering problem of noise and sources. Overall, DNN-C provides a lightweight solution to implement source number detection and DOA estimation simultaneously and efficiently. Its testing time is about 2 times less than the classical solution (i.e., minimum descriptive length and multiple signal classification, shortened as MDL-MUSIC) when the grid step is 1° Importantly, it is robust to nonuniform noise by nature and can identify the absence of sources. The effectiveness of DNN-C is verified by simulation results. Furthermore, the DNN-C model trained by simulated data shows its generalization to real data measured by a circular array of eight sensors. | |
| 653 | |a Simulation | ||
| 653 | |a Signal processing | ||
| 653 | |a Clustering | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Signal classification | ||
| 653 | |a Classification | ||
| 653 | |a Sensor arrays | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Direction of arrival | ||
| 653 | |a Localization | ||
| 653 | |a Eigenvectors | ||
| 653 | |a Vectors (mathematics) | ||
| 700 | 1 | |a Zhou, Yuan |u Yangtze River Delta Research Institute and and School of Software, Northwestern Polytechnical University, Taicang 215400, China; <email>zhouyuan153@mail.nwpu.edu.cn</email> (Y.Z.); <email>lizi@mail.nwpu.edu.cn</email> (Z.L.); <email>yxxie0721@mail.nwpu.edu.cn</email> (Y.X.) | |
| 700 | 1 | |a Li, Zi |u Yangtze River Delta Research Institute and and School of Software, Northwestern Polytechnical University, Taicang 215400, China; <email>zhouyuan153@mail.nwpu.edu.cn</email> (Y.Z.); <email>lizi@mail.nwpu.edu.cn</email> (Z.L.); <email>yxxie0721@mail.nwpu.edu.cn</email> (Y.X.) | |
| 700 | 1 | |a Xie, Yuxuan |u Yangtze River Delta Research Institute and and School of Software, Northwestern Polytechnical University, Taicang 215400, China; <email>zhouyuan153@mail.nwpu.edu.cn</email> (Y.Z.); <email>lizi@mail.nwpu.edu.cn</email> (Z.L.); <email>yxxie0721@mail.nwpu.edu.cn</email> (Y.X.) | |
| 700 | 1 | |a Cao Zeng |u National Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi’an 710072, China; <email>czeng@mail.xidian.edu.cn</email> | |
| 700 | 1 | |a Liu, Zhiling |u Nanjing Electronic Equipment Institute, Nanjing 210007, China; <email>lzl_good@sina.com</email> | |
| 773 | 0 | |t Electronics |g vol. 14, no. 4 (2025), p. 713 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
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