SparseWake: A Benchmark for Multi-Source State Estimation from Local Flow Measurements

Local flow sensing can help underwater robots locate a target of interest, but the measured signal is shaped by nearby sources and may not be dominated by the target. A controlled testbed with source-level records would allow us to diagnose where localization fails and to isolate the effect of source competition, guiding the design of local perception for future underwater agents. Here, we introduce SparseWake, a benchmark for state estimation from sparse, body-oriented velocity histories generated by a physics-informed dynamic model of fish wakes. It defines three task families of increasing competition. A single-neighbor task (canonical) establishes a reference, controlled mixtures add interference around a fixed nearest target, and symmetric source sampling (common-prior) selects the nearest source only after the scene is generated. Alongside the mixed observations, SparseWake retains each source's flow contribution, so that predictions can be diagnosed against source-level ground truth and individual contributions can be edited without changing the target. To demonstrate its use, we evaluate four architectures and find a shared failure mode, in which predictions far from every source dominate squared localization error. We further characterize the roles of temporal and measurement structure, and use paired source interventions to show that fitted estimators respond to relative source contribution beyond overall signal scale. We package the observations, source records, splits, baselines, and evaluation tools as a controlled testbed for developing local perception in underwater agents.