A Review of Spatial Parameter Estimation Techniques Using a Single Hydrophone

Localizing an underwater acoustic source with a single hydrophone eliminates the cost, synchronization, and deployment burdens of multi-sensor arrays, but the absence of spatial sampling renders the inverse problem underdetermined: positional information must be recovered from the structure that propagation, motion, and vector sensing imprint on a single time series. This review organizes the field along four dimensions. First, the estimation tasks—direction finding, ranging, depth estimation, and tracking—are formalized. Second, the measurement models that generate exploitable structure are derived in a unified notation, covering multipath delays, modal dispersion and waveguide-invariant striations, Doppler and synthetic-aperture effects, and pressure–particle-velocity vector observables. Third, the inference paradigms that map these measurements to position estimates are reviewed, spanning model-based matching, closed-form geometric inversion, Bayesian filtering and batch inversion, and data-driven learning. Fourth, reported accuracies, computational costs, environmental sensitivities, and prior-knowledge requirements are compared across method families. The evidence indicates that relative range errors of 1–10% are routinely reported, whereas depth estimation is structurally harder and remains the principal accuracy bottleneck. Environmental mismatch, multipath resolvability, multi-source interference, and the out-of-distribution generalization of learned models constitute the main open challenges; physics–learning hybridization, uncertainty-aware estimation, vector-hydrophone designs, and joint localization–environment inversion are identified as promising directions.