Paper Citation Record · LEDGER

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification

As of 19 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2606.02767.

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

pith.paper-citation-record.v1
2606.02767 v1

Coverage vector

measured 37 of 37 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-06-28T13:58:37.840951Z

measured 37 of 37 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation fe687d7b-2356-478f-8b23-df9922625ce5 · outbound

This paper cites New Results in Linear Filtering and Prediction Theory,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification New Results in Linear Filtering and Prediction Theory,

Reference 1

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Observation fc3cf9f2-a981-4793-9d84-321c4a231e18 · outbound

This paper cites Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software,

Reference 2

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This paper cites Available: https://api.semanticscholar.org/CorpusID: 108666793.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Available: https://api.semanticscholar.org/CorpusID: 108666793

Reference 3

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This paper cites Simon,Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, 1st ed.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Simon,Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, 1st ed

Reference 4

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This paper cites The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods,

Reference 5

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Observation 39c090e8-a07c-4d31-8e0c-6b6fa7abeb84 · outbound

This paper cites Approaches to adaptive filtering,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Approaches to adaptive filtering,

Reference 6

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Observation b613d71e-d81d-43d5-92cd-74e9ee5f9a81 · outbound

This paper cites On the Identification of Noise Covariances and Adaptive Kalman Filtering: A New Look at a 50 Year-Old Problem,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification On the Identification of Noise Covariances and Adaptive Kalman Filtering: A New Look at a 50 Year-Old Problem,

Reference 7

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This paper cites Generalized Variational Inference: Three arguments for deriving new Posteriors,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Generalized Variational Inference: Three arguments for deriving new Posteriors,

Reference 8

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This paper cites Multimodal dataset for indoor 3D drone tracking,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Multimodal dataset for indoor 3D drone tracking,

Reference 9

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This paper cites AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles,

Reference 10

Resolution: verified exact; doi, observed 2026-06-28T14:02:17.117007Z

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Observation 1e332856-8aa5-4eaf-9bae-4e61c6cea141 · outbound

This paper cites Optimal Estimation in the Presence of Unknown Parameters,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Optimal Estimation in the Presence of Unknown Parameters,

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:46:23.944663Z

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Observation 1bac5616-1b3c-4a43-91e8-6919421e6971 · outbound

This paper cites Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo,

Reference 12

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Observation 3bb9d7ec-7326-414a-b121-4b99fbf6ce5c · outbound

This paper cites On the identification of variances and adaptive Kalman filtering,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification On the identification of variances and adaptive Kalman filtering,

Reference 13

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Observation 81d32e5e-e153-4bc1-b54e-8cb5cb60c1ce · outbound

This paper cites Maximum Likelihood from Incomplete Data Via theEMAlgorithm,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Maximum Likelihood from Incomplete Data Via theEMAlgorithm,

Reference 14

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This paper cites An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm,

Reference 15

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This paper cites A., Hekker, S., Stello, D., Guti ´errez-Soto, J., Handberg, R., Huber, D., et al.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification A., Hekker, S., Stello, D., Guti ´errez-Soto, J., Handberg, R., Huber, D., et al

Reference 16

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Observation a6970620-2feb-4d21-a9d5-073610e54926 · outbound

This paper cites Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization,

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:46:23.957002Z

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Observation 533184e5-e360-4d37-9464-b61ad1a8f32c · outbound

This paper cites Kalman Filter Auto-Tuning With Consistent and Robust Bayesian Optimization,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Kalman Filter Auto-Tuning With Consistent and Robust Bayesian Optimization,

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:46:23.931391Z

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This paper cites Multi- Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Multi- Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter,

Reference 19

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This paper cites A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network,

Reference 20

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This paper cites A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

Reference 21

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Observation 7326f7f6-f260-42ca-bc34-2176a3bc33d5 · outbound

This paper cites Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

Reference 22

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This paper cites Deep Kalman Filters.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Deep Kalman Filters

Reference 23

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This paper cites KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Reference 24

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Observation 2a5f649e-7f69-4fa9-8817-e277886d0cfb · outbound

This paper cites Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter,

Reference 25

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Observation d184b666-e88a-4846-b8c1-2eeb940bb2d2 · outbound

This paper cites Cholesky-KalmanNet: Model-Based Deep Learning With Positive Definite Error Covariance Structure,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Cholesky-KalmanNet: Model-Based Deep Learning With Positive Definite Error Covariance Structure,

Reference 26

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This paper cites Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification

Reference 27

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This paper cites Properties and first application of an error-statistics tuning method in variational assimilation,.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Properties and first application of an error-statistics tuning method in variational assimilation,

Reference 28

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This paper cites A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation

Reference 29

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This paper cites doi: 10.1093/biomet/asx010.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification doi: 10.1093/biomet/asx010

Reference 30

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Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification A comparison of learning rate selection methods in generalized Bayesian inference

Reference 31

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Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Unresolved cited work

Reference 32

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Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Unconstrained parametrizations for variance-covariance matrices,

Reference 33

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This paper cites Optimization or Architecture: How to Hack Kalman Filtering.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Optimization or Architecture: How to Hack Kalman Filtering

Reference 34

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Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Adam: A Method for Stochastic Optimization

Reference 35

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This paper cites Goodfellow, Y.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Goodfellow, Y

Reference 36

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This paper cites Cluster Computing 6(3), 215–226 (Jul 2003), https://doi.org/10.1023/A: 1023588520138.

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification Cluster Computing 6(3), 215–226 (Jul 2003), https://doi.org/10.1023/A: 1023588520138

Reference 37

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