An Incremental Density Clustering Method with Time-Difference Prior for Mobile Multi-Station Radar Signal Sorting

CHEN Jinli; FAN Yu; WANG Yanjie; ZHANG Jindong

doi:10.11999/JEIT260151

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CHEN Jinli, FAN Yu, WANG Yanjie, ZHANG Jindong. An Incremental Density Clustering Method with Time-Difference Prior for Mobile Multi-Station Radar Signal Sorting[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260151

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CHEN Jinli, FAN Yu, WANG Yanjie, ZHANG Jindong. An Incremental Density Clustering Method with Time-Difference Prior for Mobile Multi-Station Radar Signal Sorting[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260151

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An Incremental Density Clustering Method with Time-Difference Prior for Mobile Multi-Station Radar Signal Sorting

doi: 10.11999/JEIT260151 cstr: 32379.14.JEIT260151

CHEN Jinli^{1
,
,},
FAN Yu^{1, 2},
WANG Yanjie¹,
ZHANG Jindong³

1.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Tianchang Research Institute, Nanjing University of Information Science andTechnology, Tianchang 239300, China
3.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds: The National Natural Science Foundation of China (62071238), The National Nature Science Foundation of Jiangsu Province (BK20191399)

Received Date: 2026-02-04
Accepted Date: 2026-07-07
Rev Recd Date: 2026-07-07

Available Online: 2026-07-19

Abstract

Abstract

Objective In modern electronic warfare, radar signal sorting is a pivotal technology for electronic reconnaissance. Its primary objective is to deinterleave and categorize pulses from multiple radar emitters within dense and overlapping pulse streams. However, in practical reconnaissance missions, particularly those involving mobile platforms such as aircraft, the observation stations are in constant motion, while the target radar emitters typically remain stationary. This dynamic geometric relationship between the observation stations and the emitters causes the Time Difference of Arrival (TDOA) of intercepted signals to exhibit non-stationary characteristics that evolve over time. Conventional sorting algorithms lack the mechanism to perceive this dynamic feature drift. Consequently, continuous TDOA trajectories generated by the same emitter are prone to being incorrectly partitioned into multiple clusters during data processing, resulting in cluster proliferation and degraded sorting performance. To address these issues, an incremental density-based clustering method incorporating TDOA prior information is proposed. The proposed method effectively exploits the temporal evolution characteristics of TDOA, achieving high stability and high sorting accuracy in mobile multi-station scenarios. Methods First, by analyzing the temporal evolution characteristics of TDOA in mobile multi-station scenarios, the TDOA evolution process is approximated as a linear function of time, and a linear prior model is established to characterize its dynamic behavior. An online micro-cluster construction strategy is then adopted for multi-station TDOA samples, and macro-clusters are formed according to the spatiotemporal intersection relationships among micro-clusters. For each macro-cluster, independent Kalman filters are constructed for different TDOA dimensions. The TDOA value and its rate of change are defined as state variables, and recursive state prediction is performed to provide dynamic feature references for newly arriving samples. During sample association, a spatiotemporal joint scoring function is designed, in which prediction residuals generated by the Kalman filters are incorporated as dynamic constraints. Consequently, the matching criterion evolves from a conventional static density-based rule into a joint spatiotemporal consistency constraint, enabling more accurate assignment of newly arriving TDOA samples. To suppress cluster proliferation caused by TDOA feature evolution, a concept drift detection mechanism based on macro-cluster center evolution is further introduced. Once concept drift is detected, posterior state vectors and covariance matrices estimated by the Kalman filters are employed to construct a dual Mahalanobis-distance criterion that jointly evaluates state-distribution overlap and predicted-position overlap. Under a 95% confidence threshold, mis-split radar clusters are adaptively merged and assigned to the same radiation source, thereby effectively suppressing cluster proliferation induced by concept drift. Results and Discussions In the simulation experiments, radar pulse data distributions are generated according to the radar parameters (Table 1). The multi-station TDOA corresponding to the same radiation source exhibits a near-linear evolution trend with respect to the Time of Arrival (TOA) at the observation station(Fig. 5). To comprehensively evaluate the performance of the proposed method in complex environments, a simulation analysis on radar cluster proliferation suppression is first conducted (Fig. 6). In this scenario, radar emitters E4 and E9 are selected from the nine simulated radiation sources as representative cases for analysis. Compared with DBSCAN, Incremental Density-Based Clustering (ICDC), cloud model-based sorting, and PointNet++ sorting algorithms, the proposed method effectively suppresses radar cluster proliferation and demonstrates superior cluster stability during the sorting process. Furthermore, to further demonstrate the superiority of the proposed approach, its sorting performance was comprehensively compared with several representative algorithms, including the conventional histogram method, grid-based clustering, DBSCAN, ICDC algorithm, cloud model-based sorting, and PointNet++ sorting algorithms. The sorting accuracy of various algorithms under different TOA measurement errors (Fig. 7) indicates that the proposed method achieves a sorting accuracy exceeding 96% when the TOA measurement error ranges from 50 to 300 ns, demonstrating remarkable robustness to measurement noise. Under different pulse interference rates (Fig. 8), the sorting accuracy of the proposed method remains above 96%, exhibiting excellent interference suppression capability. To further assess the stability of different algorithms under mobile observation platforms, simulations were performed at different observation station velocities (Fig. 9). The proposed method consistently maintains a high sorting accuracy across all tested velocities. Even at relatively high observation station speeds, the sorting accuracy remains at approximately 94%, indicating that the proposed approach retains favorable stability and robustness under dynamic observation conditions. In addition, simulations on radar cluster proliferation and missed-cluster probabilities are conducted (Fig. 10), and the computational complexities of different algorithms are analyzed (Table 2). The results demonstrate that the proposed method provides a favorable balance among sorting accuracy, cluster proliferation suppression, missed-cluster control, and computational cost. Conclusions To address the sorting performance degradation caused by the temporal evolution of TDOA features in mobile multi-station scenarios, an incremental density-based clustering method incorporating TDOA prior information is proposed. By incorporating observation station motion information to construct a dynamic TDOA state model and integrating Kalman Filters to achieve recursive prediction of TDOA evolution, the proposed method effectively suppresses radar cluster proliferation and mis-splitting caused by concept drift. In addition, the spatiotemporal joint criterion and the adaptive cluster merging mechanism further enhance the robustness of the algorithm under complex and dynamic environments. Simulation results verify the effectiveness and stability of the proposed method in mobile multi-station cooperative reconnaissance scenarios. Future work will focus on real-time multi-parameter fusion-based sorting methods in complex electromagnetic environments. Furthermore, adaptive adjustment strategies for the micro-cluster spatial intersection threshold, process noise covariance matrix, and measurement noise variance will also be investigated.
- Signal sorting,
- Mobile multi-station,
- Linear prior,
- Kalman filters,
- Incremental density-based clustering(ICDC)

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References(19)

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