Paper Citation Record · LEDGER

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search

As of 13 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2606.01325.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.01325 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T16:11:19.160481Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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verified fuzzy0
unresolved42
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 524e9250-fd6a-43ec-8587-bb1c4dc47287 · outbound

This paper cites Through-the-wall human respiration detection using UWB impulse radar on hovering drone,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Through-the-wall human respiration detection using UWB impulse radar on hovering drone,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59ea1978-9c21-4cb4-bd5a-a5b6b6397815 · outbound

This paper cites Respiration detection of ground injured human target using UWB radar mounted on a hovering UA V,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Respiration detection of ground injured human target using UWB radar mounted on a hovering UA V,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 7ec38d07-28d2-45c8-b789-dde2e835ef84 · outbound

This paper cites Multi-target path planning with probabilistic detection in cluttered environments,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Multi-target path planning with probabilistic detection in cluttered environments,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 471dd6e0-bcbc-4d2b-a037-8e4643d37646 · outbound

This paper cites Pdsr: Efficient uav deployment for swift and accurate post- disaster search and rescue,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Pdsr: Efficient uav deployment for swift and accurate post- disaster search and rescue,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 377e8e19-5095-4288-b8d8-2b58419c7c9e · outbound

This paper cites DRONE-RL: Dynamic reinforcement learning for on- line navigation of UA Vs in evolving environments,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search DRONE-RL: Dynamic reinforcement learning for on- line navigation of UA Vs in evolving environments,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85fb3e02-0d5c-49a7-9386-4ececa09b15a · outbound

This paper cites Hazan,Introduction to Online Convex Optimization.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Hazan,Introduction to Online Convex Optimization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c2d44b2-6230-44ce-a1c3-654f0e048f71 · outbound

This paper cites An online learning framework for UA V search mission in adversarial environ- ments,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search An online learning framework for UA V search mission in adversarial environ- ments,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4c9929e-efef-4cde-995e-7d00f2118ca9 · outbound

This paper cites Online trajectory optimization using inexact gradient feedback for time-varying environments,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Online trajectory optimization using inexact gradient feedback for time-varying environments,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Unavailable: canonical work link unavailable.

Observation 5d1e81aa-69c5-47ed-8c2f-e50a455c2e34 · outbound

This paper cites Between stochastic and adversarial online convex optimization: Improved regret bounds via smoothness,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Between stochastic and adversarial online convex optimization: Improved regret bounds via smoothness,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46be70f5-3659-4fdd-8a8f-e9a1b2c95a48 · outbound

This paper cites Online learning with predictable se- quences,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Online learning with predictable se- quences,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 60626b91-4358-4bcc-a0e6-ac6ea86651d7 · outbound

This paper cites On the dynamic regret of following the regularized leader: Optimism with history pruning,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search On the dynamic regret of following the regularized leader: Optimism with history pruning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 5f812022-e937-4f78-a935-0e012a30a792 · outbound

This paper cites Understanding adam optimizer via online learning of updates: Adam is ftrl in disguise,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Understanding adam optimizer via online learning of updates: Adam is ftrl in disguise,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 15764f7a-dcfc-4b3b-b540-46c5ec057c13 · outbound

This paper cites Reinforcement learning framework for UA V-based target localization applications,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Reinforcement learning framework for UA V-based target localization applications,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation a70b89e0-d3a4-4057-ae09-11ec8526751d · outbound

This paper cites A predictive target tracking framework for IoT using CNN–LSTM,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search A predictive target tracking framework for IoT using CNN–LSTM,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Unavailable: canonical work link unavailable.

Observation 73ac802a-e3d5-4050-8f69-043e817c1008 · outbound

This paper cites Human body detection and geolocalization for UA V search and rescue missions using color and thermal imagery,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Human body detection and geolocalization for UA V search and rescue missions using color and thermal imagery,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

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Observation 78f11b48-3fe5-43be-8a25-ae9820c3c24b · outbound

This paper cites Monocular 3D pose estimation and tracking by detection,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Monocular 3D pose estimation and tracking by detection,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

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Observation 0ec981e3-03a0-46b4-9e6a-e291409c22e8 · outbound

This paper cites Obstacle-aware human localization via channel impulse response,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Obstacle-aware human localization via channel impulse response,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 8ea15574-7fcd-4202-93e0-f743f8835082 · outbound

This paper cites Intelligent uav swarm cooperation for multiple targets tracking,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Intelligent uav swarm cooperation for multiple targets tracking,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation b1c23467-39e1-41e7-99f5-08349dd3baa0 · outbound

This paper cites A deep learning framework for target localization in error-prone environment,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search A deep learning framework for target localization in error-prone environment,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

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Observation 3817a9c3-a13c-4cf2-8adc-a364eb54e389 · outbound

This paper cites Survey on coverage path planning with unmanned aerial vehicles,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Survey on coverage path planning with unmanned aerial vehicles,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b84c156-b6db-4063-8c45-dbfb11b0aca6 · outbound

This paper cites Efficient path planning for UA V formation via comprehensively improved particle swarm optimization,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Efficient path planning for UA V formation via comprehensively improved particle swarm optimization,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aff732d-c0e4-4fcf-a908-9dc689ab6cfd · outbound

This paper cites Distributed robotic sensor networks: An information-theoretic approach,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Distributed robotic sensor networks: An information-theoretic approach,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb89c91f-c806-4067-a38f-984996ef89a9 · outbound

This paper cites Uav path planning in a dynamic environment via partially observable markov decision process,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Uav path planning in a dynamic environment via partially observable markov decision process,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f18f765-cf87-493d-be18-8669403184a8 · outbound

This paper cites Machine learning-aided operations and communications of unmanned aerial vehicles: A contemporary survey,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Machine learning-aided operations and communications of unmanned aerial vehicles: A contemporary survey,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7156820f-8236-4a24-89d8-d5feb09ac490 · outbound

This paper cites Consensus-based decentralized auctions for robust task allocation,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Consensus-based decentralized auctions for robust task allocation,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c342621-88a6-4f64-a4b3-c521313fccf1 · outbound

This paper cites Distributed multi-robot coordi- nation in area exploration,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Distributed multi-robot coordi- nation in area exploration,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Unavailable: canonical work link unavailable.

Observation 2323467e-59a4-4519-b111-32a0a74a16e8 · outbound

This paper cites Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical stud- ies,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical stud- ies,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

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Observation 52470de5-1b44-425c-8108-bb4c68415eab · outbound

This paper cites Reinforcement learning for agile active target sensing with a UA V,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Reinforcement learning for agile active target sensing with a UA V,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 9c945c58-eb2f-4984-9f03-7ace44677f5e · outbound

This paper cites Bandit submodular maximization for multi-robot coordination in unpredictable and partially observable environments,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Bandit submodular maximization for multi-robot coordination in unpredictable and partially observable environments,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation dd4de7d9-0e3d-400f-b476-9ade5bf87628 · outbound

This paper cites Online caching with optimistic learning,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Online caching with optimistic learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation e5233c76-1403-4307-85ab-d1f942a9ea5e · outbound

This paper cites A modern introduction to online learning,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search A modern introduction to online learning,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 872e2d04-6642-4ad0-be79-941be2216b8a · outbound

This paper cites Lattimore and C.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Lattimore and C

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation ccc2b7ad-be06-4fa6-8faf-06b23b623246 · outbound

This paper cites Online learning: A compre- hensive survey,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Online learning: A compre- hensive survey,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Observation 5d9ca585-343b-4572-bec5-a50b70b00589 · outbound

This paper cites Parameter-free algo- rithms for the stochastically extended adversarial model,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Parameter-free algo- rithms for the stochastically extended adversarial model,

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:56:15.185151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 572c99fe-7d3b-4ad5-9c5b-8748c254937c · outbound

This paper cites A second-order bound with excess losses,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search A second-order bound with excess losses,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Unavailable: canonical work link unavailable.

Observation 03ccf01b-8e85-47e9-b6d8-3c166b69fe50 · outbound

This paper cites Achieving all with no parameters: AdaNor- malHedge,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Achieving all with no parameters: AdaNor- malHedge,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Unavailable: canonical work link unavailable.

Observation 32b8b190-8327-4fa4-b0a6-1dedbb5ef497 · outbound

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SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Online optimization: Competing with dynamic comparators,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Unavailable: canonical work link unavailable.

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SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Online meta- learning,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Unavailable: canonical work link unavailable.

Observation 2262ecc5-d78f-49f4-81e3-3c345db17533 · outbound

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SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Continuous adaptation via meta-learning in nonstationary and competitive environments,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4b4447d-b4a9-4752-bed7-94b6a7e5d16d · outbound

This paper cites Adaptive online learning in dynamic environments,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Adaptive online learning in dynamic environments,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

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Observation c0317d0b-2181-4380-b416-cbaec9d1a860 · outbound

This paper cites Partially lazy gradient descent for smoothed online learning,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Partially lazy gradient descent for smoothed online learning,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

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Observation d6882714-e83e-47b2-b3ea-1606a1ab3796 · outbound

This paper cites Finite-time analysis of the multiarmed bandit problem,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search Finite-time analysis of the multiarmed bandit problem,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b8f859c-14e6-4f41-b131-140c7ee3d337 · outbound

This paper cites The non- stochastic multiarmed bandit problem,.

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search The non- stochastic multiarmed bandit problem,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-28T16:11:19.160481Z

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Pith citing papers

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