Signal Sorting Method Based on Multi-station Time Difference and Dirichlet Process Mixture Model

CHEN Jinli; WANG Yanjie; FAN Yu; LI Jiaqiang

doi:10.11999/JEIT250191

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CHEN Jinli, WANG Yanjie, FAN Yu, LI Jiaqiang. Signal Sorting Method Based on Multi-station Time Difference and Dirichlet Process Mixture Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250191

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CHEN Jinli, WANG Yanjie, FAN Yu, LI Jiaqiang. Signal Sorting Method Based on Multi-station Time Difference and Dirichlet Process Mixture Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250191

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Signal Sorting Method Based on Multi-station Time Difference and Dirichlet Process Mixture Model

doi: 10.11999/JEIT250191 cstr: 32379.14.JEIT250191

School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62071238), The National Nature Science Foundation of Jiangsu Province (BK20191399), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX25_1645)

Received Date: 2025-03-24
Rev Recd Date: 2025-09-01

Available Online: 2025-09-09

Abstract

Abstract

Objective Signal sorting is a crucial technology in electronic reconnaissance that enables the de-interleaving of mixed pulse sequences emitted by multiple radar radiation sources, thereby supporting military decision-making. With the rapid advancement of electronic technology, multi-station cooperative signal sorting has received increasing attention. However, existing multi-station signal sorting methods depend heavily on manually selected parameters, which limits adaptability. Moreover, in complex environments with pulse loss and noise interference, conventional methods struggle to process unpaired pulses effectively, reducing the accuracy and stability of sorting. To address these challenges, this study applies the Dirichlet Process Mixture Model (DPMM) to multi-station cooperative signal sorting. The proposed approach enables adaptive sorting even when the number of radiation sources is unknown or measurement errors exist, thereby improving flexibility and adaptability. Furthermore, it can effectively classify unpaired pulses caused by pulse loss or noise, enhancing the robustness and reliability of sorting. This research provides a novel strategy for signal sorting in complex electromagnetic environments and holds promising application value in radar signal processing. Methods In multi-station cooperative signal sorting, the spatial distribution of multiple receiving stations detecting the same radar signal makes efficient and accurate signal pairing and classification a core challenge. To address this issue, a multi-station cooperative signal sorting method based on the DPMM is proposed. The process comprises three stages: pulse pairing, time-difference clustering and sorting, and mismatched pulse classification. In the pulse pairing stage, identical pulses originating from the same radiation source are identified from the sequences intercepted by each receiving station. To ensure accurate pairing, a dual-constraint strategy is adopted, combining a time-difference window with multi-parameter matching. Successfully paired pulses are then constructed into a time-difference vector set, which provides the data foundation for the subsequent clustering and sorting stage. In the time-difference clustering and sorting stage, DPMM is employed to cluster the time-difference vector set. DPMM adaptively determines the number of clusters to model the data structure, enabling the system to infer the optimal cluster count. Gibbs sampling is used to optimize model parameters, further enhancing clustering robustness. Based on the clustering results, radar pulse sets are constructed, achieving signal sorting across multiple radiation sources. In the mismatched pulse classification stage, unpaired pulses caused by noise interference or pulse loss during transmission are further processed. DPMM is applied to fit radar pulse parameter vectors, including pulse width, radio frequency, and bandwidth. The affiliation degree of each mismatched pulse relative to the radar pulse sets is then calculated. Pulses with affiliation degrees exceeding a predefined threshold are merged into the corresponding pulse set, whereas those below the threshold are classified as anomalous pulses, likely due to interference or noise, and are discarded. This method enhances the adaptability and robustness of multi-station cooperative signal sorting and provides an effective solution for complex electromagnetic environments. Results and Discussions In the experimental validation, radar pulse data are generated through simulation to evaluate the effectiveness of the proposed method. Compared with traditional multi-station cooperative signal sorting approaches, the method achieves high-precision sorting without requiring prior knowledge of the number of radiation sources or parameter measurement errors, thereby demonstrating strong adaptability and practicality. To comprehensively assess performance in complex environments, simulations are conducted to analyze sorting capability under varying measurement errors, pulse loss rates, and interference rates. The final sorting results are summarized in (Table. 3). The results indicate that even in the presence of noise interference and data loss, most radar pulses are accurately identified, with only a small fraction misclassified as interference signals. The final sorting accuracy reaches 98.8%, confirming the robustness and stability of the method against pulse loss, noise, and other uncertainties. To further validate its superiority, the method is compared with other algorithms under different conditions. Sorting accuracy under different Time of Arrival (TOA) measurement errors (Fig. 6) shows that stable performance is maintained even under severe noise interference, reflecting strong noise resistance. Further analyses of sorting accuracy under different pulse loss rates and interference rates (Figs. 7 and 8) demonstrate that higher efficiency and stability are achieved in handling unpaired pulses, and pulses that fail to be paired are more accurately classified. The sorting accuracy of different algorithms in various scenarios (Fig. 9) further confirms that the method performs more consistently in complex environments, indicating higher adaptability. Overall, the method adapts well to diverse application scenarios and provides efficient, stable, and reliable signal sorting for multi-station cooperative electronic reconnaissance tasks. Conclusions This study proposes a multi-station cooperative signal sorting method based on the DPMM to address the limitations of traditional approaches, which rely heavily on prior information and perform poorly in processing unpaired pulses. By applying DPMM for adaptive clustering of time-difference information, the proposed method avoids sorting errors caused by improper manual parameter settings and effectively classifies unpaired pulses based on radar pulse parameter characteristics. Simulation results show that this method not only improves the accuracy and stability of multi-station cooperative signal sorting but also maintains high sorting performance even when the number of radiation sources is unknown or measurement errors are present, highlighting its engineering application value. Future research may extend this approach to dynamic electromagnetic environments and adaptive real-time processing to meet the demands of more complex electronic reconnaissance tasks.
- Signal sorting,
- Pulse pairing,
- Time Difference Of Arrival (TDOA),
- Dirichlet Process Mixture Model (DPMM)

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References(19)

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