An Incremental Density-Based Clustering Method with TDOA 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-Based Clustering Method with TDOA 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-Based Clustering Method with TDOA 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-Based Clustering Method with TDOA 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 Radar signal sorting is a core technology in electronic reconnaissance that aims to deinterleave and classify pulses from multiple radar emitters within dense, overlapping pulse streams. In practical reconnaissance missions, especially those employing mobile platforms such as aircraft, observation stations continuously change position, whereas radar emitters are typically stationary. The resulting variation in the relative geometry causes the Time Difference of Arrival (TDOA) of intercepted signals to evolve over time. Conventional multi-station radar signal sorting methods are generally developed under a static TDOA assumption and cannot effectively characterize this temporal evolution. Therefore, pulses from the same radar emitter are easily split into multiple clusters, resulting in cluster proliferation and degraded sorting performance. To address this problem, an Incremental Density-Based Clustering (ICDC) method with TDOA prior information is proposed for mobile multi-station radar signal sorting. The proposed method exploits the temporal evolution of TDOA to improve sorting stability and accuracy in mobile multi-station scenarios. Methods The temporal evolution of TDOA in mobile multi-station scenarios is first analyzed, and the TDOA trajectory is approximated as a linear function of time to establish a linear prior state model. Online micro-clusters are then constructed from multi-station TDOA observations and aggregated into macro-clusters according to their spatiotemporal intersection relationships. For each macro-cluster, an independent Kalman Filter (KF) model is established for each TDOA dimension. The state vector consists of the TDOA value and its rate of change, and recursive state estimation is performed to provide dynamic TDOA priors for newly arriving samples. During incremental clustering, a spatiotemporal joint scoring function is developed by incorporating KF prediction residuals as dynamic constraints. The matching criterion therefore evolves from a conventional density-based rule into a joint spatiotemporal consistency criterion, enabling more accurate assignment of newly arriving TDOA observations. To suppress cluster proliferation caused by TDOA evolution, a concept drift detection strategy based on macro-cluster center evolution is further employed. When concept drift is detected, posterior state estimates and covariance matrices generated by the KF are used to construct a dual Mahalanobis distance criterion that jointly evaluates state-distribution overlap and predicted TDOA overlap. Radar clusters produced by erroneous splitting are then adaptively merged under a 95% confidence threshold, effectively suppressing cluster proliferation caused by concept drift. Results and Discussions Simulation data are generated according to the radar parameters listed in Table 1. The multi-station TDOA corresponding to the same radar emitter exhibits an approximately linear evolution with respect to the Time of Arrival (TOA) at the observation station (Fig. 5), consistent with the proposed linear prior model. The ability of the proposed method to suppress radar cluster proliferation is first evaluated (Fig. 6). Radar emitters E4 and E9 are selected from the nine simulated emitters as representative cases because their TDOA trajectories are closely spaced and difficult to separate. Compared with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), ICDC, cloud model-based sorting, and PointNet++ sorting, the proposed method more effectively suppresses radar cluster proliferation and maintains greater cluster stability. The overall sorting performance is further compared with the histogram method, grid-based clustering, DBSCAN, ICDC, cloud model-based sorting, and PointNet++ sorting. When the TOA measurement error ranges from 50 to 300 ns (Fig. 7), the proposed method consistently achieves a sorting accuracy above 96%, demonstrating strong robustness to measurement errors. Under different pulse interference rates (Fig. 8), the sorting accuracy also remains above 96%, indicating excellent interference robustness. The performance under different observation station velocities is further evaluated (Fig. 9). The proposed method maintains high sorting accuracy over the entire velocity range and still achieves approximately 94% accuracy at relatively high observation station velocities, demonstrating strong robustness under dynamic observation conditions. Radar cluster proliferation probability, missed-cluster probability (Fig. 10), and computational complexity (Table 2) are also analyzed. The results demonstrate that the proposed method achieves a favorable balance among sorting accuracy, cluster proliferation suppression, missed-cluster control, and computational complexity. Conclusions An ICDC method with TDOA prior information is proposed to address the degradation of radar signal sorting performance caused by TDOA evolution in mobile multi-station scenarios. By incorporating observation station motion into a dynamic TDOA state model and applying KF-based recursive prediction, the proposed method effectively suppresses radar cluster proliferation and erroneous cluster splitting caused by concept drift. The spatiotemporal joint criterion and the adaptive cluster merging strategy further improve robustness in complex dynamic environments. Simulation results demonstrate the effectiveness and stability of the proposed method for mobile multi-station cooperative reconnaissance. Future work will focus on real-time multi-parameter fusion-based sorting in complex electromagnetic environments. Furthermore, adaptive estimation and adjustment of the micro-cluster spatial intersection threshold, process noise covariance matrix, and measurement noise variance will be investigated to further improve performance in complex scenarios.
- Signal sorting,
- Mobile multi-station,
- Linear prior,
- Kalman filters,
- Incremental Density-Based Clustering (ICDC)

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References(19)

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