Robust beamforming for jammers suppression in MIMO radar
Bibliographic record
Abstract
Robust beamforming for multiple-input multiple-output (MIMO) radar in the background of powerful jamming signals is investigated in this paper. We design two minimum variance distortionless response (MVDR) type beamformers with adaptiveness/robustness against the powerful jammers for colo-cated MIMO radar. Specifically, the MVDR beamformer is firstly designed for known jammers in the sector-of-interest, which maintains distortionless response towards the direction of the target while imposing nulls towards the directions of jammers. Then the adaptive/robust MVDR beamformer is designed for the general case of unknown in-sector jammers and/or out-of-sector interfering sources. Convex optimization techniques are used in both of the designs. Moreover, we derive a closed-form solution to the simplified second design. Based on this solution, we derive efficient power estimates of the desired and/or interfering sources in the context of powerful jammers and non-ideal factors such as array calibration errors and target steering vector mismatches. We demonstrate that the capability of efficient jammers suppression using these designs is unique in MIMO radar.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".