Dual-polarized radar systems and signal processing algorithms
Bibliographic record
Abstract
Dual-polarized radar systems can be configured in different ways depending on the measurement goals and the choice of orthogonal polarization states. From a theoretical perspective, the 3 × 3 covariance matrix (see Section 3.11) forms a complete set, but only a few research meteorological radars exist at the present time that are configured for this measurement. The circularly polarized radars built at the National Research Council of Canada were essentially configured for coherency matrix measurements (see Section 3.9). In the early 1980s, a number of single-polarized research Doppler radars were upgraded for limited dual-polarization measurements in the linear h/v -basis (for measurement of differential reflectivity and differential propagation phase). Because only copolar signals were involved, the system requirements were much less stringent and significant practical results (e.g. rain rate estimation, hail detection) were obtained fairly quickly (Hall et al. 1980; Bringi et al. 1984; Sachidananda and Zrnić 1986). This chapter discusses a number of dual-polarized radar configurations from a systems perspective. Since antenna performance is critical for achieving high accuracy in the measurement of the “weak” cross-polar signal, both antenna performance characteristics and formulation of radar observables in the presence of system polarization errors are treated. Calibration issues relevant to polarization diversity systems are also discussed. A significant portion of this chapter is devoted to estimation of the elements of the covariance matrix from signal samples under three different pulsing schemes. The accuracy of these covariance matrix estimates is also treated in some detail.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".