A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features
With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environm...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/304 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587581464772608 |
|---|---|
| author | Ziyao Ma Zhongliang Deng Zidu Tian Yingjian Zhang Jizhou Wang Jilong Guo |
| author_facet | Ziyao Ma Zhongliang Deng Zidu Tian Yingjian Zhang Jizhou Wang Jilong Guo |
| author_sort | Ziyao Ma |
| collection | DOAJ |
| description | With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods. |
| format | Article |
| id | doaj-art-891702ee42d447bd9e905d3637a8f403 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-891702ee42d447bd9e905d3637a8f4032025-01-24T13:48:26ZengMDPI AGSensors1424-82202025-01-0125230410.3390/s25020304A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive FeaturesZiyao Ma0Zhongliang Deng1Zidu Tian2Yingjian Zhang3Jizhou Wang4Jilong Guo5School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWith the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods.https://www.mdpi.com/1424-8220/25/2/3045G CIRLOS/NLOS recognitiontime delay neural networkmambahierarchical features |
| spellingShingle | Ziyao Ma Zhongliang Deng Zidu Tian Yingjian Zhang Jizhou Wang Jilong Guo A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features Sensors 5G CIR LOS/NLOS recognition time delay neural network mamba hierarchical features |
| title | A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features |
| title_full | A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features |
| title_fullStr | A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features |
| title_full_unstemmed | A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features |
| title_short | A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features |
| title_sort | line of sight non line of sight recognition method based on the dynamic multi level optimization of comprehensive features |
| topic | 5G CIR LOS/NLOS recognition time delay neural network mamba hierarchical features |
| url | https://www.mdpi.com/1424-8220/25/2/304 |
| work_keys_str_mv | AT ziyaoma alineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT zhongliangdeng alineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT zidutian alineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT yingjianzhang alineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT jizhouwang alineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT jilongguo alineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT ziyaoma lineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT zhongliangdeng lineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT zidutian lineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT yingjianzhang lineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT jizhouwang lineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures AT jilongguo lineofsightnonlineofsightrecognitionmethodbasedonthedynamicmultileveloptimizationofcomprehensivefeatures |