A Multi-Station Emitter TDOA Deinterleaving Method for Severe Pulse-Loss Environments

LIU Yuchen; ZHAO Yaqin; WU Longwen

doi:10.11999/JEIT260401

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LIU Yuchen, ZHAO Yaqin, WU Longwen. A Multi-Station Emitter TDOA Deinterleaving Method for Severe Pulse-Loss Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260401

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LIU Yuchen, ZHAO Yaqin, WU Longwen. A Multi-Station Emitter TDOA Deinterleaving Method for Severe Pulse-Loss Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260401

LIU Yuchen, ZHAO Yaqin, WU Longwen. A Multi-Station Emitter TDOA Deinterleaving Method for Severe Pulse-Loss Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260401

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A Multi-Station Emitter TDOA Deinterleaving Method for Severe Pulse-Loss Environments

doi: 10.11999/JEIT260401 cstr: 32379.14.JEIT260401

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62571163, 62571167 and 62071153)

Received Date: 2026-04-04
Accepted Date: 2026-08-10
Rev Recd Date: 2026-08-03

Available Online: 2026-08-18

Abstract

Abstract

Objective Modern electronic reconnaissance systems must deinterleave dense and overlapping radar pulse streams in non-cooperative environments. As radar emitters increasingly employ agile waveforms, similar pulse descriptor words, and low-intercept-probability strategies, conventional single-station methods based on carrier frequency, pulse width, and Pulse Repetition Interval (PRI) become less reliable. Multi-station deinterleaving based on Time Difference of Arrival (TDOA) provides a more stable geometric observable, but severe pulse loss still causes sparse cross-station pairing, weak true TDOA peaks, ambiguity-induced spurious peaks, isolated pulses, and fragmented trajectories across time slices. These effects increase false alarms and weaken track continuity. To address these issues, a closed-loop multi-station emitter TDOA deinterleaving method is proposed for severe pulse-loss environments, with Time of Arrival (TOA) sequences used as the core observables. Methods A slice-based framework is developed for continuous reconnaissance. Residual unmatched pulses are carried forward by a sliding window to alleviate cross-slice misalignment. First, candidate pulse pairs satisfying geometric TDOA constraints are generated, and pulse descriptor word constraints on carrier frequency and pulse width are used to remove inconsistent pairs. To reduce the sparsity and binning sensitivity of conventional histograms, multiscale Kernel Density Estimation (KDE) is introduced to reconstruct the TDOA density from sparse candidate differences. Gaussian kernels with different bandwidths are fused, and candidate peaks are adaptively extracted using local statistics and peak widths. Second, a dynamic memory matrix is designed to suppress ambiguity-induced spurious peaks in high pulse repetition frequency scenarios. Since dependent spurious peaks collapse after the dominant peak is extracted and removed, a collapse-rate criterion is defined, and the spurious regions are recorded in a memory mask for subsequent iterations. Third, Dynamic Time Warping (DTW) is used to compare incomplete TOA sequences of isolated pulses with extracted pulse sequences, enabling reassignment of unequal-length and incomplete sequences. Finally, a Kalman-filter-based state-space model tracks multi-baseline TDOA trajectories across successive slices. Predicted and observed TDOA residuals are jointly used for association, so intermittent observations can still be linked to the correct track. In this way, the proposed method forms a closed-loop processing chain that links weak-peak reconstruction, spurious-peak suppression, isolated-pulse reassignment, and trajectory association (Fig. 3). Results and Discussions Four simulation scenarios are designed: a high pulse repetition frequency scenario dominated by ambiguity-induced spurious peaks, a parameter-overlapping scenario dominated by isolated pulse reassignment, an ablation scenario for evaluating the memory matrix and DTW modules, and a 10-emitter mixed-regime scenario including fixed PRI, staggered, jittered, frequency-agile, pulse-group frequency-agile, frequency-agile jittered-PRI, linear-sliding, and sinusoidal-sliding PRI signals. In the mixed-regime scenario, the total reconnaissance duration is 1 s and the slice duration is 0.1 s. The environmental pulse loss rate is fixed at 10%, and the receiver-specific loss rate increases from 0% to 40%. Both loss rates are calculated with respect to the initial theoretical number of transmitted pulses; therefore, the total loss rate is their sum, ranging from 10% to 50%. The proposed method is compared with an extended TDOA histogram method under constrained criteria, a cloud-model-based multi-station sorting method, and a Dirichlet Process Mixture Model (DPMM)-based method (Table 5). In the high pulse repetition frequency scenario, the proposed method maintains near-zero false alarms by identifying the collapse of dependent spurious peaks and suppressing them through the memory matrix, whereas the comparison methods show severe false alarms (Figs. 4 and 5). In the parameter-overlapping scenario, DTW-based reassignment improves isolated-pulse recovery, while the memory matrix suppresses spurious TDOA peaks. Their combination improves extraction reliability and reduces false alarms (Figs. 6 and 7). The ablation results verify their complementary roles: at a 50% loss rate, the memory matrix reduces the TDOA false alarm rate from 29.92% to 3.32%, DTW increases pulse extraction accuracy from 66.71% to 91.99%, and the complete method achieves a TDOA detection rate of 99.25% with a false alarm rate of 0.88% (Fig. 8). In the 10-emitter mixed-regime scenario, the proposed method achieves a favorable overall trade-off. At an overall pulse loss rate of 50%, its pulse extraction accuracy remains 92.72%, and the TDOA false alarm rate is limited to 7.75%, lower than 35.39%, 35.05%, and 37.58% for the DPMM, cloud-model, and constrained recursive histogram methods, respectively. After cross-slice trajectory association, the mean number of identity switches decreases to 3.83, compared with 10.64, 9.85, and 12.64 for the three comparison methods (Fig. 9 and Table 5). Conclusions A closed-loop multi-station emitter TDOA deinterleaving method is proposed for severe pulse-loss environments. By integrating multiscale KDE-based weak peak reconstruction, dynamic memory-matrix-based spurious peak suppression, DTW-based isolated pulse reassignment, and Kalman-filter-based trajectory association, the method addresses the coupled failure mechanisms caused by severe pulse loss. Simulation results demonstrate high extraction accuracy, low TDOA false alarm rates, and strong trajectory continuity in high-loss and mixed-regime scenarios. These results demonstrate the effectiveness of the method under the simulated conditions and indicate its application potential for persistent multi-station passive reconnaissance.
- Multi-station emitter deinterleaving,
- Pulse loss,
- Time difference of arrival,
- Kernel density estimation,
- Dynamic time warping

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