Xuehao Zhang

Marine radar plan position indicator (PPI) images may be the only observation source available for image-domain post-processing, yet weak target-like echoes are often obscured by sea clutter and persistent structural returns. Missed detections can fragment tracks, whereas permissive front-end processing can generate excessive candidates. This study presents an online robust low-rank enhancement and structure-aware tracking method. A finite history window supports background estimation by Huber iteratively reweighted projection, and raw intensity, local contrast, energy, and positive sparse-residual evidence are fused to generate ordered-statistics constant false alarm rate (OS-CFAR)-type candidates. Structural occupancy, compact-echo protection, multi-evidence aggregation, and multi-window motion cues then suppress structure-induced pseudo-motion. Active-track-first association, two-stage reassociation, and constrained candidate recovery are used to preserve track continuity. Across three PPI sequences acquired at different times at one radar site, structure-aware processing reduced the mean candidate load from 61.16 to 19.01 per frame. Against frame-by-frame dense visual references, the complete method achieved an F1 score of 88.24% and a multiple object tracking accuracy (MOTA) of 76.09%. The results show that, under the evaluated image-domain conditions, the method balances weak-echo retention, suppression of structure-induced false activations, and track continuity. Generalization across devices, sites, and sea states remains to be established.

Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a compass (SINS/DVL/COMPASS) can provide essential state information, but the accuracy and fault tolerance of such systems are constrained by weak observability of position/heading errors and strong dependence on DVL measurements. This study proposes a fault-tolerant optimization method based on perceptual information compensation. First, radar imagery and electronic chart data are fused at the feature level using a weighted wavelet strategy to enhance the environmental feature saliency for shoreline extraction. Second, characteristic coastline inflection points are detected and tracked using a dual-curvature and distance-constrained procedure, generating external position observations via radar–chart matching. These observations are incorporated into the SINS/DVL/COMPASS framework to improve its state observability and robustness. Simulation results show that under nominal conditions, perceptual compensation mitigates error divergence and promotes the convergence of position errors, improving the positioning stability. In terms of robustness, the proposed method delivered more stable state-error behavior than the baseline under DVL speed faults of +2 m/s, −2 m/s, and +2 m/s injected at 301–330, 701–730, and 1101–1130 s, respectively. Quantitatively, the 3σ bounds of velocity and position-related errors are reduced under fault conditions, indicating improved fault tolerance and suitability for short-term nearshore autonomous navigation during GNSS outages.