LSTM-Kalman Filter-Based Multi-Sensor Signal Fusion for UAV Altitude Prediction in Non-Gaussian Environments
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Bibliographic record
Abstract
To address altitude estimation inaccuracies in Unmanned Aerial Vehicles (UAVs) under non-Gaussian noise and intermittent sensor failures, this paper proposes a Long Short-Term Memory (LSTM)-Kalman cooperative architecture that establishes symbiotic interaction between deep feature extraction and physical filtering.The core innovation lies in bidirectional cyclic learning: LSTM layers distill temporal noise patterns while Kalman modules inject state-space constraints through differentiable projection.A manifold interpolation mechanism resolves multi-rate signal mismatches, utilizing LSTM-derived coherence weights to guide Lie group synchronization for phase distortion suppression.The framework incorporates a fractal-aware decoupling network where LSTM cells generate adaptive masks, dynamically separating Gaussian/non-Gaussian components to reconstruct Kalman gain rules.Experimental validation demonstrates the architecture's superiority in balancing physical consistency and learning capability, providing a novel paradigm for robust navigation signal fusion under complex noise conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it