Residual LSTM-Based Multipath-Scattered Pulse Sorting for Scatterer Localization in Maritime ESM Systems

In maritime electronic support measures (ESMS), multipath-scattered pulses are often suppressed during pulse sorting, although their delay, amplitude, and angular differences may provide information for passive scatterer localization. This paper investigates a front-end path-classification task positioned after emitter-level clustering and before multipath-assisted passive localization. Pulses produced by the same non-cooperative emitter but received through different propagation paths are classified as direct-path or multipath-scattered pulses. The task is formulated as supervised binary classification over PDW sequences. Five representative solution families are evaluated under a common protocol: FCM, DBSCAN, temporal sequence analysis (TSA), Single-LSTM, and a residual two-layer unidirectional LSTM with residual fusion. The input features are RF, PA, PW, PRI, TOA, DOA, and ΔTOA; the recurrent models use class-weighted training to address the direct/scattered class imbalance. Across 36 coupled scenarios with pulse-loss rates from 0% to 50% and parameter-jitter levels from 0.0 to 1.0, the residual LSTM obtains the highest average macro-F1 score (0.8717), compared with Single-LSTM (0.7726), DBSCAN (0.7686), TSA (0.6511), and FCM (0.5917). Repeated training over four random seeds yields a validation macro-F1 of 0.9821 ± 0.0007 on the original validation set. The ablation results indicate that ΔTOA is the principal temporal cue in this setting, while LayerNorm, residual fusion, class weighting, and augmentation mainly contribute to optimization stability and perturbation robustness. Measured-data verification suggests that the learned temporal representation can provide usable inputs for subsequent scatterer localization. The current validation is limited to a one-emitter simulation and rule-assisted measured-data annotation; mixed-emitter validation and quantitatively calibrated localization evaluation remain subjects for future study.