Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering | Paper Citation Record

Observation 093ab0da-7f16-4672-85a5-dfc9160bf1e7 · outbound

This paper cites Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems

Reference 1

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Observation fa3b19ba-deb1-4fe9-9df4-0c5293112f9f · outbound

This paper cites A stochastic track maintenance scheduling model based on deep reinforcement learning approaches.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering A stochastic track maintenance scheduling model based on deep reinforcement learning approaches

Reference 2

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Observation 7987bb1b-cb38-4593-9b70-8f796d730d19 · outbound

This paper cites Identify severe track geometry defect combinations for maintenance planning.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Identify severe track geometry defect combinations for maintenance planning

Reference 3

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This paper cites Intelligent and adaptive asset management model for railway sections using the iPN method.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Intelligent and adaptive asset management model for railway sections using the iPN method

Reference 5

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Observation bffc80e6-34a5-4e58-a88e-6cc85ebeeb92 · outbound

This paper cites Track geometry degradation and maintenance modelling: A review.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Track geometry degradation and maintenance modelling: A review

Reference 6

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Observation a5e5a0d9-7679-4f5b-ae77-64af95500480 · outbound

This paper cites Position synchronization for track geometry inspection data via big-data fusion and incremental learning.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Reference 7

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Observation ad2c7795-1895-4dd4-90b1-4be1c9cba508 · outbound

This paper cites Evaluation of onboard sensors for track geometry monitoring against conventional track recording measurements.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Evaluation of onboard sensors for track geometry monitoring against conventional track recording measurements

Reference 8

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Observation 73f7e225-0b95-4e33-8db0-dbb5f67ad66a · outbound

This paper cites Peinado Gonzalo, R.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Peinado Gonzalo, R

Reference 9

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Observation 4d119336-f836-4de9-8a4b-7842c28b91a2 · outbound

This paper cites Railway Track Geometry Degradation Modelling and Prediction for Maintenance Decision Support.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Railway Track Geometry Degradation Modelling and Prediction for Maintenance Decision Support

Reference 10

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Observation 5a8f38aa-191d-4b0a-a4b3-d3ca2aaf0e0d · outbound

This paper cites An adaptive opportunistic maintenance model based on railway track condition prediction.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering An adaptive opportunistic maintenance model based on railway track condition prediction

Reference 11

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e3458a49-87d7-4d50-b12c-6f2f5cf47f6b · outbound

This paper cites Particle filter -based prognostic approach for railway track geometry.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Particle filter -based prognostic approach for railway track geometry

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:28.094622Z

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Observation aed8d5c3-3f4c-4fa5-81a5-2cb2189bd30d · outbound

This paper cites Data-driven optimization of railway maintenance for track geometry.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Data-driven optimization of railway maintenance for track geometry

Reference 13

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 80fea376-b513-480b-98c7-437addd75717 · outbound

This paper cites Modelling the evolution of ballasted railway track geometry by a two -level piecewise model.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Modelling the evolution of ballasted railway track geometry by a two -level piecewise model

Reference 14

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This paper cites Lurdes S.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Lurdes S

Reference 15

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec7cd6cc-1e5e-4a26-b107-c2ce9eeaf09b · outbound

This paper cites Bivariate Gamma wear processes for track geometry modelling, with application to intervention scheduling.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Bivariate Gamma wear processes for track geometry modelling, with application to intervention scheduling

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:27.889823Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ef6f0904-96e7-452b-8b8a-4e9425bb75a4 · outbound

This paper cites Prediction of railway track geometry defects: a case study.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Prediction of railway track geometry defects: a case study

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-07T11:54:24.704819Z

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Observation 4bc9974b-dc8d-4963-b24a-9395cfd4093b · outbound

This paper cites Principal components analysis and track quality index: A machine learning approach.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Principal components analysis and track quality index: A machine learning approach

Reference 18

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f60c4e29-f627-40f9-9a41-3d5f7eed3a50 · outbound

This paper cites Bayesian multivariate track geometry degradation modeling and its use in condition -based inspection.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Bayesian multivariate track geometry degradation modeling and its use in condition -based inspection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:27.615976Z

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Observation 17ea5b97-adae-4da3-9107-25f0a811e73b · outbound

This paper cites Estimation of railway track longitudinal irregularity using vehicle response with information compression and Bayesian deep learning.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Estimation of railway track longitudinal irregularity using vehicle response with information compression and Bayesian deep learning

Reference 20

Resolution: verified exact; doi, observed 2026-08-07T11:54:21.590629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3d31b46e-cd79-471f-9f73-7a3df7054cb3 · outbound

This paper cites Perspectives on railway track geometry condition monitoring from in-service railway vehicles.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Perspectives on railway track geometry condition monitoring from in-service railway vehicles

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:27.451426Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a0ea8f9c-efcc-4f7b-9728-b4b4e12079fd · outbound

This paper cites Condition monitoring of vertical track alignment by bogie acceleration measurements on commercial high-speed vehicles.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Condition monitoring of vertical track alignment by bogie acceleration measurements on commercial high-speed vehicles

Reference 22

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Observation 52f40eed-24ea-4bd8-8052-f28430ebd915 · outbound

This paper cites Condition monitoring of railway track from car-body vibration using time– frequency analysis.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Condition monitoring of railway track from car-body vibration using time– frequency analysis

Reference 23

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Source-reported events for the cited work

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Observation 357f02fd-7aeb-4eca-8e4a-9b0f63225877 · outbound

This paper cites Track-monitoring from the dynamic response of an operational train.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Track-monitoring from the dynamic response of an operational train

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:18.151834Z

Source-reported events for the cited work

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Observation 1f64cc64-7973-43e0-a853-977bb733e0f9 · outbound

This paper cites Perspectives of assessing the geometric condition of railway tracks using a device for measuring the topographic profile.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Perspectives of assessing the geometric condition of railway tracks using a device for measuring the topographic profile

Reference 25

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T11:54:23.563196Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c437428-bc7c-48e5-9d70-ecdd8f85b93a · outbound

This paper cites Track geometry monitoring by an on -board computer -vision-based sensor system.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Track geometry monitoring by an on -board computer -vision-based sensor system

Reference 26

Resolution: verified exact; doi, observed 2026-08-07T11:54:21.476122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e66ee3f-fb0a-4faa-9740-db3bf03762cf · outbound

This paper cites Olivier, F.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Olivier, F

Reference 27

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T11:54:23.286317Z

Source-reported events for the cited work

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Observation 41ea4a26-1dc3-4962-998a-3d2adba48301 · outbound

This paper cites Estimation of lateral track irregularity through Kalman filtering techniques.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Estimation of lateral track irregularity through Kalman filtering techniques

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:27.168527Z

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Observation 40a812f8-013f-4ad0-a6ee-46359c594926 · outbound

This paper cites Experimental measurement of track irregularities using a scaled track recording vehicle and Kalman filtering techniques.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Experimental measurement of track irregularities using a scaled track recording vehicle and Kalman filtering techniques

Reference 29

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This paper cites Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measurements.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measurements

Reference 30

Resolution: verified exact; doi, observed 2026-08-07T11:54:21.296157Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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This paper cites Kinematic modeling of a track geometry using an unscented Kalman filter.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Kinematic modeling of a track geometry using an unscented Kalman filter

Reference 31

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This paper cites A Bayesian Kalman filter algorithm for quantifying estimation uncertainty of track irregularity on bridges with randomness in system parameters.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering A Bayesian Kalman filter algorithm for quantifying estimation uncertainty of track irregularity on bridges with randomness in system parameters

Reference 32

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Observation b5c56cd4-b9ce-45f7-90f9-5fe664d30753 · outbound

This paper cites Chapter 9 - On-board monitoring for smart assessment of railway infrastructure: A systematic review.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Chapter 9 - On-board monitoring for smart assessment of railway infrastructure: A systematic review

Reference 33

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This paper cites Smartphone’s Sensing Capabilities for On -Board Railway Track Monitoring: Structural Performance and Geometrical Degradation Assessment.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Smartphone’s Sensing Capabilities for On -Board Railway Track Monitoring: Structural Performance and Geometrical Degradation Assessment

Reference 34

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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This paper cites Advances in Collaborative Filtering.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Advances in Collaborative Filtering

Reference 35

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This paper cites A Review on Kalman Filter Models.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering A Review on Kalman Filter Models

Reference 36

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Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Robust Kalman filtering for uncertain discrete -time systems

Reference 37

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Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Interacting multiple model estimation-based adaptive robust unscented Kalman filter

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:26.180907Z

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Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Cubature kalman filters

Reference 39

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Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Probabilistic Kalman filter for moving object tracking

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:25.722266Z

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Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Nonlinear predictive controllers for continuous systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:25.514793Z

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Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Robust filtering

Reference 42

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This paper cites Particle filter -based prognostics: Review, discussion and perspectives.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Particle filter -based prognostics: Review, discussion and perspectives

Reference 43

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation afb2ef3a-d823-4819-8727-69f74796e8b6 · outbound

This paper cites Gustafsson.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Gustafsson

Reference 44

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Source-reported events for the cited work

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This paper cites Principal Components Analysis Based on Multivariate MM Estimators With Fast and Robust Bootstrap.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Principal Components Analysis Based on Multivariate MM Estimators With Fast and Robust Bootstrap

Reference 45

Resolution: verified exact; doi, observed 2026-08-07T11:54:20.706781Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation af98d8b9-1156-4a60-a2c1-cf5eee1bc8e9 · outbound

This paper cites Influence functions and efficiencies of the canonical correlation and vector estimates based on scatter and shape matrices.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Influence functions and efficiencies of the canonical correlation and vector estimates based on scatter and shape matrices

Reference 46

Resolution: verified exact; doi, observed 2026-08-07T11:54:20.548149Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 63d717b1-8a0a-44bf-9f54-e47ae831b42b · outbound

This paper cites an unresolved cited work.

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering Unresolved cited work

Reference 2025

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Source-reported events for the cited work

correction dated 2025-12-03. Source: crossref record 10.1007/s40534-025-00421-4->10.1007/s40534-025-00394-4:correction, observed 2026-07-11T02:59:57.892829+00:00. This notice travels one citation hop only.

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