{
  "abstract": "This paper presents an information-gain-guided reinforcement-learning sensor-selection framework for single-vessel tracking in heterogeneous maritime sensor networks. The proposed approach is motivated by information-theoretic sensor management: instead of activating all sensors or repeatedly performing computationally expensive online expected-information-gain evaluation, a learned policy selects one tracking-relevant sensor at each decision epoch. A Bayesian sequential Monte Carlo tracker estimates the vessel state from noisy measurements and provides a belief representation for scheduling under nonlinear and non-Gaussian conditions. A Proximal Policy Optimization agent selects one of five sensors in a georeferenced simulation of the CMMI Smart Marina testbed at Ayia Napa Marina, Cyprus. The policy is trained on the testbed's actual five-sensor configuration. The agent observes belief-state, detection-history, coverage, sensor-geometry, and realized-information-gain features. The reward is defined as a realized-information-gain term gated by an observability mask. Final-test simulations compare the proposed framework with random single-sensor selection, always-on sensing using all sensors simultaneously, and the expected-information-gain sensor-selection baseline proposed in our previous work. Results show that the learned policy achieves tracking performance close to always-on sensing while activating only one sensor per decision time step and avoiding the computationally expensive online entropy search required by expected-information-gain selection. Additional zero-shot evaluation without retraining on ten moderately perturbed versions of actual layout configuration showed broadly stable tracking, with any increase in positional tracking error remaining below 1 meter across all perturbations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.22667",
  "author_total": 5,
  "authors": [
    {
      "name": "Andrei Starodubov"
    },
    {
      "name": "Yaqub Aris Prabowo"
    },
    {
      "name": "Andreas Hadjipieris"
    },
    {
      "name": "Roberto Galeazzi"
    },
    {
      "name": "Ioannis Kyriakides"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5g23v9gj5zx5g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.22667",
  "fulltext_url": null,
  "id": "5g23v9gj5zx5g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.22667",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
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      "arxiv",
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      "openalex",
      "1702|Target Tracking and Data Fusion in Sensor Networks"
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  "openalex": "W7171519211",
  "pdf_url": "https://arxiv.org/pdf/2607.22667",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5g23v9gj5zx5g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:20Z",
      "native_id": "2607.22667",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.22667"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance",
  "title_text": "Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:20Z",
  "url": "https://research.draconitic.com/paper/5g23v9gj5zx5g",
  "venue": "arXiv",
  "versions": []
}