Deterministic Approach for Spatial Diversity Analysis of Radar Systems Using Near-Field Radar Cross Section of a Metallic Plate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A deterministic analysis of spatial diversity is presented in connection with radar systems. A numerical technique based on physical optics is used for our analysis. Contrary to statistical models, the proposed technique takes into account accurate near-field radar cross section of the target, and radiation characteristics of transmitting and receiving antennas. The power scattered by the target and received by multiple antennas as a function of the target aspect angle and distance is analyzed. Two combining methods of received powers are tested and statistical analysis is performed showing that, using spatial diversity, the angular range can be increased significantly and the standard deviation of the target response can be reduced. In order to validate our analysis and proposed scheme, experimental measurements were carried out using a metallic plate and a car as targets. This work has potential applications in automotive collision warning/avoidance radar systems.
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.
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 | 0.000 | 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 | 0.000 | 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 it