Minimization of Internally Reflected Power Via Waveform Design in Cognitive MIMO Radar
Bibliographic record
Abstract
State-of-the-art cognitive multiple-input multiple-output (MIMO) radars maximize the signal-to-interference-plus-noise ratio (SINR) for an extended target of interest by matching the transmitted waveforms to the target impulse response (TIR). Existing methods to match the transmitted waveforms do not consider the problem of internally reflected power due to the mutual coupling between the transmitting antenna array elements, which results in transmitter inefficiency and possible hardware damage. While the mutual coupling problem in MIMO radars has been handled using microwave techniques heretofore, we herein advocate a signal-processing approach to this problem in cognitive MIMO radars. Specifically, we propose an effective waveform design formalism allowing to jointly maximize the SINR and minimize the reflected power from the transmitting antennas under a TIR matching constraint, while achieving waveform orthogonality in the Doppler domain. Minimizing the reflected power is achieved through the incorporation of a regularization term, taking the form of an $\ell _{\infty }$-norm, in the objective function of a minimum variance distortionless response criterion. An efficient proximal gradient method is developed to solve the resulting nonsmooth optimization problem. Simulations with different TIR distributions and transmitting antenna array sizes show that the proposed waveform design algorithm results in lower active reflection coefficients for the antenna elements than selected benchmarks. Furthermore, our algorithm offers a competitive SINR performance compared to these benchmarks and can cope with the fast-varying TIR.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".