Identification of Non-Line-Of-Sight Signals Based on Direct Path Signal Residual and Support Vector Data Description

NI Xue; ZENG HaiYu; YANG Wendong

doi:10.11999/JEIT240960

Volume 47 Issue 6

Jun. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(6): 1873-1884

NI Xue, ZENG HaiYu, YANG Wendong. Identification of Non-Line-Of-Sight Signals Based on Direct Path Signal Residual and Support Vector Data Description[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1873-1884. doi: 10.11999/JEIT240960

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NI Xue, ZENG HaiYu, YANG Wendong. Identification of Non-Line-Of-Sight Signals Based on Direct Path Signal Residual and Support Vector Data Description[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1873-1884. doi: 10.11999/JEIT240960

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Identification of Non-Line-Of-Sight Signals Based on Direct Path Signal Residual and Support Vector Data Description

doi: 10.11999/JEIT240960 cstr: 32379.14.JEIT240960

1.
School of Communication Engineering, Army Force Engineering University, Nanjing 210007, China
2.
78156 troops, Chongqing 400050, China

Funds: The National Natural Science Foundation of China (62171461)

Received Date: 2024-10-28
Rev Recd Date: 2025-05-06

Available Online: 2025-05-21

Publish Date: 2025-06-30

Abstract

Abstract

Objective Current machine learning-based methods for Non-Line-Of-Sight (NLOS) signal recognition either require the collection of a large amount of data from two different types of signals for various scenarios, or the trained models fail to generalize across different environments. These methods also do not simultaneously address the practical challenges of low training sample acquisition cost and good scene adaptation. This paper proposes a new NLOS recognition method that collects single-class signals from a single environment to train recognition models, which then demonstrate high accuracy when recognizing signals in different scenarios. This approach offers the advantages of low sample acquisition cost and strong environmental adaptability. Methods This paper proposes Direct Path (DP) signal residual feature parameters that exhibit significant differences between two types of signals. The effectiveness of these parameters is theoretically analyzed and combined with nine feature parameters identified in typical literature, forming various feature vectors to characterize the signals. This approach effectively enhances the accuracy of the recognition model. A class of signals with high feature similarity across different scenarios is used as training data, and a single recognition model is employed as the machine learning algorithm. The model is trained on signal samples collected in typical Line-Of-Sight (LOS) channels to improve its scene adaptability. Based on the principles of Deep Support Vector Data Description (DSVDD), a reverse-expanded DSVDD model is designed for NLOS signal recognition, further improving the model’s accuracy in recognizing samples across different scenarios. Results and Discussions As shown in Table 2, in the signal recognition scenario where the test set and training set originate from the same scene, the Least Squares Support Vector Machine (LSSVM) model demonstrates the best recognition performance. This is achieved using hyperplanes trained with two types of signals, resulting in a recognition accuracy of over 95%. In comparison, the standard Support Vector Data Description (SVDD) model, which is trained using only single-class LOS signal samples, exhibits a performance loss relative to LSSVM, with a maximum accuracy decrease exceeding 5%. The recognition accuracy of the SVDD model trained with DP signal residual features improves compared to the standard SVDD model, with the highest accuracy difference remaining within 5% of the LSSVM model. Furthermore, the performance of the DSVDD model, trained with DP signal residuals, shows a further improvement, with the highest accuracy decrease decreased to less than 2% compared to the LSSVM model. In scenarios where the training set and test data come from different scenes, LSSVM requires two types of signals for training. However, the hyperplane trained with two types of signal samples from a single scene exhibits poor performance when recognizing signal samples from other scenarios, with a maximum accuracy of less than 75%. The SVDD model trained with DP signal residual eigenvalues incorporates features with significant differences between the two signal types, improving recognition accuracy to over 80%. Finally, the DSVDD model, trained with DP signal residual features and replacing the Gaussian kernel function in the SVDD model with a neural network, further enhances recognition accuracy, achieving a maximum accuracy exceeding 85%. Conclusions A recognition method based on DP signal residual feature training for DSVDD is proposed to address the challenges of low sample acquisition cost and strong environmental adaptability in typical NLOS signal recognition. Compared with the SVDD method, this approach improves upon feature parameters, models, and model structures by introducing features with significant differences between the two types of signals, resulting in a substantial improvement in recognition performance. Additionally, the paper designs a reverse dimensionality expansion for DSVDD and incorporates it into NLOS signal recognition, further enhancing the accuracy of the recognition model across different scene samples. Compared to other typical machine learning algorithms, the proposed method requires the collection of single-class signal data from a single scene and performs effectively in recognizing signal samples from other scenes. Although the proposed method outperforms typical single-recognition approaches, the overall performance still has room for improvement. The theoretical analysis regarding how neural networks can better explore potential relationships between features is insufficient, and the full potential of neural networks in single-recognition models has not been fully realized. Furthermore, due to time constraints, this study only simulated sample data collected from three scenarios, and the recognition performance in other typical scenarios requires further validation.

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References(22)

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