Paper Citation Record · LEDGER

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation

As of 9 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2502.05069.

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

pith.paper-citation-record.v1
2502.05069 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:26:15.688235Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:24:01.284134Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:24:04.282245Z

Reference resolution

55 of 55 outbound references displayed

verified exact5
verified fuzzy43
unresolved7
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e5361161-2bde-443d-a00e-9bca90047428 · outbound

This paper cites Long-distance geomagnetic navigation: Imitations of animal migration based on a new assumption,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Long-distance geomagnetic navigation: Imitations of animal migration based on a new assumption,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.264434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d5547c2-e87b-4e30-83bf-95c50a885d34 · outbound

This paper cites Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T20:26:15.445211Z

Source-reported events for the cited work

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Observation 1db5d146-df6e-49a0-be2f-a29fa3ae99e7 · outbound

This paper cites Geographic true navigation based on real-time measurements of geomagnetic fields,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Geographic true navigation based on real-time measurements of geomagnetic fields,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.250042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16c375a4-dc8b-4049-816b-e836b3af8d12 · outbound

This paper cites Geomagnetic vector pattern recognition navigation method based on probabilistic neural network,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Geomagnetic vector pattern recognition navigation method based on probabilistic neural network,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.235864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cbf3e914-9859-4964-94b4-83efb808471b · outbound

This paper cites Geomagnetic gradient-assisted evolutionary algorithm for long-range underwater navigation,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Geomagnetic gradient-assisted evolutionary algorithm for long-range underwater navigation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.222064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b6e3fe4-29a7-4031-96b4-4b1093a91ed3 · outbound

This paper cites Magnetic navigation on an F-16 aircraft using online calibration,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Magnetic navigation on an F-16 aircraft using online calibration,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.207919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9edf84a-6c1c-415c-804d-4daaab458bdd · outbound

This paper cites Promising aircraft navigation systems with use of physical fields: Stationary magnetic field gradient, gravity gradient, alternating magnetic field,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Promising aircraft navigation systems with use of physical fields: Stationary magnetic field gradient, gravity gradient, alternating magnetic field,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.193708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8c08332-7cce-45b5-a3e4-d24e3f72f213 · outbound

This paper cites A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-08T20:26:16.631308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a67229c7-ddab-48c5-bb5c-c0fac29405d0 · outbound

This paper cites Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties,

Reference 9

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T20:26:16.610687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 15575531-e016-4913-918e-17b34517b50f · outbound

This paper cites Adaptive dual control with online outlier detection for uncertain systems,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Adaptive dual control with online outlier detection for uncertain systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.178934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d6147c0-5bac-4bb9-9957-c0638474419e · outbound

This paper cites Dual control for stochastic systems with multiple uncertainties,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Dual control for stochastic systems with multiple uncertainties,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.163894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2c974ad3-cfb1-4bd2-90b5-d99549d00a85 · outbound

This paper cites Robust quadratic optimal control of linear systems with ellipsoid-set learning,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Robust quadratic optimal control of linear systems with ellipsoid-set learning,

Reference 12

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T20:26:16.419947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f967fda-1b7c-4cbf-b78b-19cf9579428c · outbound

This paper cites An outlier detection scheme for dynamical sequential datasets,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation An outlier detection scheme for dynamical sequential datasets,

Reference 13

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T20:26:16.245296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 607b0dac-afd8-4f3d-82b8-bd3dd871663b · outbound

This paper cites Sequential outlier criterion for sparsification of online adaptive filtering,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Sequential outlier criterion for sparsification of online adaptive filtering,

Reference 14

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T20:26:16.013044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b45cf312-3c44-49c9-a8d7-ff4d2280c1c5 · outbound

This paper cites Natural orthogonal component analysis of international geomagnetic reference field models and its application to historical geomagnetic models,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Natural orthogonal component analysis of international geomagnetic reference field models and its application to historical geomagnetic models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.149335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 412541cb-5dd6-45ba-bb3d-a69e91a89279 · outbound

This paper cites Deep reinforcement learning based mobile robot navigation: A review,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Deep reinforcement learning based mobile robot navigation: A review,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.134946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2c7af293-4f89-43d7-8176-0ef224c0078c · outbound

This paper cites Simulation of single element geomagnetic matching navigation based on intensified mad,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Simulation of single element geomagnetic matching navigation based on intensified mad,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.120576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b6bd3f1-0563-47ff-9c05-d0015f94112c · outbound

This paper cites A fast algorithm of the geomagnetic correlation matching based on msd,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation A fast algorithm of the geomagnetic correlation matching based on msd,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.105979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a9b4665-5482-46d1-ac66-1b07f9123458 · outbound

This paper cites A new geomagnetic matching navigation method based on multidimensional vector elements of earth’s magnetic field,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation A new geomagnetic matching navigation method based on multidimensional vector elements of earth’s magnetic field,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.090870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 50cb284d-0be0-44a1-bda6-6c977dc79ed4 · outbound

This paper cites An innovative PSO-ICCP matching algorithm for geomagnetic navigation,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation An innovative PSO-ICCP matching algorithm for geomagnetic navigation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.076549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7bfb71b2-88b6-40b8-9e92-23b0439b7f05 · outbound

This paper cites Magnetoreception in birds: two receptors for two different tasks,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Magnetoreception in birds: two receptors for two different tasks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.061802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eef9443a-5c34-4262-b325-ef01c7b5a000 · outbound

This paper cites Orientation and open-sea navigation in sea turtles,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Orientation and open-sea navigation in sea turtles,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.047664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 53a8c7af-1b84-4cd3-abcf-055b5cd716d8 · outbound

This paper cites Inherited magnetic maps in salmon and the role of geomagnetic change,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Inherited magnetic maps in salmon and the role of geomagnetic change,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.033772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e1616d8f-ee0b-4aec-806e-3933a1d38302 · outbound

This paper cites True navigation and magnetic maps in spiny lobsters,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation True navigation and magnetic maps in spiny lobsters,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.019660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 05b6b8c6-6e27-4f85-bde9-929fcde51880 · outbound

This paper cites Bio-inspired navigation based on geomagnetic,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Bio-inspired navigation based on geomagnetic,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:17.005452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f96984fb-86ba-473c-8698-66a2f0494c65 · outbound

This paper cites Bio-inspired geomagnetic navigation method for autonomous underwater vehicle,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Bio-inspired geomagnetic navigation method for autonomous underwater vehicle,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.991466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0968eb95-4306-4e65-b5f9-d86ec881b574 · outbound

This paper cites Bionic geomagnetic navigation method for auv based on differential evolution algorithm,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Bionic geomagnetic navigation method for auv based on differential evolution algorithm,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.977137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab5796c6-5920-42ad-a0c4-b355f49f7f43 · outbound

This paper cites Artificial intelligence-assisted geomagnetic navigation framework,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Artificial intelligence-assisted geomagnetic navigation framework,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.963286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee8a43f3-40f2-4206-91f5-762d279dba7d · outbound

This paper cites Geomagnetic navigation for AUV based on deep reinforcement learning algorithm,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Geomagnetic navigation for AUV based on deep reinforcement learning algorithm,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.948984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6072815-7692-4eec-a497-718578bcd9b8 · outbound

This paper cites Q-learning based linear quadratic regulator with balanced exploration and exploitation for unknown systems,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Q-learning based linear quadratic regulator with balanced exploration and exploitation for unknown systems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.935455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation edbeb926-a1e1-4187-a10b-f6b5bd61b11e · outbound

This paper cites Geomagnetic navigation with adaptive search space for AUV based on deep double-Q-network,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Geomagnetic navigation with adaptive search space for AUV based on deep double-Q-network,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.921749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c56f9600-4a66-47c2-8923-583f8b1978a1 · outbound

This paper cites Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-08T20:26:15.587854Z

Source-reported events for the cited work

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Observation 8caa6740-e625-47e9-896b-5b8715071095 · outbound

This paper cites Research on geomagnetic perceiving navigation method based on deep reinforcement learning,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Research on geomagnetic perceiving navigation method based on deep reinforcement learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.907333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c6b2b9a-2d30-422d-97c5-2e60e1481739 · outbound

This paper cites Magnetic anomalies as a reference for ground-speed and map-matching navigation,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Magnetic anomalies as a reference for ground-speed and map-matching navigation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.893319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1cff0211-5e16-48d1-886b-3ec26d9aebb0 · outbound

This paper cites The magnetic poles of the earth,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation The magnetic poles of the earth,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.878926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f2c59493-2ce0-4568-be64-641675621532 · outbound

This paper cites Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T20:26:15.606395Z

Source-reported events for the cited work

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Observation adc1d03b-fce2-4483-91a2-cfcca5d17c73 · outbound

This paper cites Addressing function approximation error in actor-critic methods,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Addressing function approximation error in actor-critic methods,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.864518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72dcf5bd-c84d-4b68-b2d7-5b413ac94471 · outbound

This paper cites Deterministic policy gradient algorithms,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Deterministic policy gradient algorithms,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.850582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 90f98ce0-42c0-42ad-a300-3459ef9d2040 · outbound

This paper cites Policy Distillation.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Policy Distillation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T20:26:15.619883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ace44f2a-999a-4077-8b07-f07420176da6 · outbound

This paper cites Multi-agent reinforcement learning: An overview,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Multi-agent reinforcement learning: An overview,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.836680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0eb87ceb-d056-41a0-bc13-a907d22cdb48 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Distilling the Knowledge in a Neural Network

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T20:26:15.629082Z

Source-reported events for the cited work

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Observation 6fade149-0f0f-4633-bd77-30af63012c85 · outbound

This paper cites Magnetic sensitivity of cryptochrome 4 from a migratory songbird,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Magnetic sensitivity of cryptochrome 4 from a migratory songbird,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.822839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c3069829-ba06-4d0d-89cc-a5ddae46ff05 · outbound

This paper cites Coordinated formation guidance law for fixed-wing uavs based on missile parallel approach method,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Coordinated formation guidance law for fixed-wing uavs based on missile parallel approach method,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.808496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5dd2225a-b3ff-4e12-b9ba-b44a1c046d0b · outbound

This paper cites International geomagnetic reference field: The 12th generation,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation International geomagnetic reference field: The 12th generation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.793901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8c07822-f904-4e51-a4a3-121cb7c02be2 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Optuna: A next-generation hyperparameter optimization framework,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.780165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30df3a32-f6ee-4d54-9e91-00f08fd5e650 · outbound

This paper cites Particle swarm optimization,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Particle swarm optimization,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.766433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19af61b4-d280-442d-a0ca-6dddd6341f3f · outbound

This paper cites Fuzzy adaptive artificial fish swarm algorithm,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Fuzzy adaptive artificial fish swarm algorithm,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.752178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 556f990d-c69b-461f-95bd-6d1d5084497e · outbound

This paper cites Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.736868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 29542d29-ab0b-4fd7-89bf-1504d4345e31 · outbound

This paper cites Adaptation in natural and artificial systems,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Adaptation in natural and artificial systems,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.720770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e195c00-d8d3-4b62-bc37-9b34f66aec2e · outbound

This paper cites A novel neural multi-store memory network for autonomous visual navigation in unknown environment,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation A novel neural multi-store memory network for autonomous visual navigation in unknown environment,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.706515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d76c77a-6e6d-4f7b-b30a-c103429c253e · outbound

This paper cites ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-08T20:26:15.674289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 471cd4f8-1360-480a-8c8b-745e4e8cba2d · outbound

This paper cites A low-cost dead reckoning navigation system for an auv using a robust AHRS: Design and experimental analysis,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation A low-cost dead reckoning navigation system for an auv using a robust AHRS: Design and experimental analysis,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.691754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9331693b-7204-4c01-9733-64de84c12fdd · outbound

This paper cites IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.676014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d3a7e150-b0ca-44f1-ae9b-59d0b1811ef9 · outbound

This paper cites Towards deviation-robust agent navigation via perturbation-aware contrastive learning,.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Towards deviation-robust agent navigation via perturbation-aware contrastive learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:26:16.660902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa0f2772-1501-4fe7-a3a6-ca66b8bed4db · outbound

This paper cites Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T20:26:15.450318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c30ea80c-b8b8-4e83-b325-4a544380a756 · inbound

Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning cites this paper.

Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:24:04.399680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.