Evaluating the deformation monitoring capability of a ground based SAR system with MIMO antenna
Bibliographic record
Abstract
By increasing the applicability of ground-based SAR (GBSAR) systems in geoscience and remote sensing, the development and evaluation of new systems have gained attention. GBSAR systems can be utilized for monitoring areas that are hard to or cannot be seen by the airborne or spaceborne systems. Furthermore, they have better spatial and temporal resolutions and are cost-effective and easy to implement. This paper develops and evaluates a GBSAR system by combining a multiple-input multiple-output (MIMO) radar and mechanical linear rail as a synthetic aperture in azimuth direction (MIMO GBSAR) in a simulated environment. The considered radar sensor consists of two transmitters and four receiver antennas, operating at the W band frequency between 76-81 GHz. Azimuth compression consists of two main steps: MIMO beamforming and then compressing all gathered signals in the azimuth direction. According to the simulated results, the proposed MIMO GBSAR is able to improve the azimuth angular resolution to 4.9 mrad, compared to the 400 mrad angular resolution of the simple MIMO radar. A monostatic radar sensor requires 920 steps to complete a 0.9 m linear synthetic aperture, while the proposed MIMO GBSAR requires 115 steps, which implies a faster data acquisition rate. A simulated experiment was conducted in order to evaluate the interferometric capability of the considered sensor. The target's displacement rate was considered 0.1 millimeters per epoch. According to the results, the errors' amplitude was smaller than 1.5 micrometer, and the average displacement error was 0.32 micrometer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".