Kalman Filter-Enhanced Space Debris Detection and Tracking Using Space-Borne FMCW Radars
Bibliographic record
Abstract
Space debris poses a growing threat to the safety of man-made operational space-borne systems, demanding reliable detection and tracking solutions to mitigate collision risks. Due to the limited data rate resulting from non-sequential computations, practical radar systems typically exhibit low measurement accuracy, particularly over long distances in space. In this work, we propose a Kalman filter-based approach for accurate range and velocity estimation of space debris using space-based linear frequency modulated (LFM) radars. By enabling sequential processing, the proposed method enhances both range and velocity resolution while maintaining a low sampling rate requirement for analog-to-digital converters (ADCs), making it suitable for real-world deployment. The performance of the proposed method is evaluated through simulations and benchmarked against conventional approaches under low signal-to-noise ratio (SNR) conditions which demonstrate its effectiveness in improving tracking accuracy. Simulation results show that the Kalman filter significantly reduces velocity estimation error, especially under low SNR levels. This confirms its practical advantage in space-based debris monitoring applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".