Ground moving target detection for along-track interferometric SAR data
Bibliographic record
Abstract
This paper investigates different metrics for ground moving target indication (GMTI) with a multichannel synthetic aperture radar (SAR). These metrics are especially sensitive to targets that have an across-track component of velocity, v/sub y/, although they can also detect targets with an along track velocity component, v/sub x/. Not only are the metrics suitable for fully SAR compressed data, they are also meaningful when computed for range (pulse) compressed but Doppler uncompressed data (raw data). The use of both data domains allows for target detection over a larger v/sub y/ range than either used individually. For large v/sub y/, side-lobe suppression in fully SAR compressed data simultaneously suppresses the targets, giving an advantage to the raw data domain. For low v/sub y/, and especially with low RCS targets, the SAR compression gain facilitates detection, giving an advantage to the SAR compressed domain. By viewing the SAR compression as the application of a linear filter, the metrics are shown to be suitable for target detection after any linear filtering of the raw data, such as a linear time-frequency filter, or a simple transformation into the Doppler domain using an FFT filter. The metrics have in common that they are all based on the eigenvector decomposition of the sample covariance matrix. Their statistical properties are analytically compared and their detection capabilities are demonstrated on measured two-channel airborne SAR data in a variety of data domains. Some metrics are shown to allow target detection with low false alarm rates even in heterogeneous terrain, such as in urban areas, without compromising the probability of detection.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".