Training-based adaptive transmit-receive beamforming for random phase radar signals
Bibliographic record
Abstract
The advent of increasingly sophisticated control over the transmitted signal has enabled the consideration of multiple input, multiple output (MIMO) radar systems wherein each transmitter transmits a different waveform. Exploiting this capability, MIMO radars can improve target detection by jointly designing the transmit signal and receive filter so as to optimize the resulting signal-to-interference-plus-noise ratio (SINR). However, the SINR depends on the clutter covariance matrix which, in turn, is a function of the transmitted signal. This paper considers the joint design of adaptive transmit and receive weights to maximize the SINR of a target at a chosen look angle-Doppler point. This is akin to extending receive-only space-time adaptive processing (STAP) to include transmit adaptivity. Previous work in joint design assumed that the required second-order statistics are known a priori. In this paper we develop a method to estimate the required statistics through a number of training sequences. The estimation is based on received data only, and does not assume any specific structure for, or a-priori knowledge of, the clutter covariance matrix. We do assume that the clutter statistics do not change during the training and detection intervals. Simulation results show that, as in receive-only STAP, the proposed method does not suffer from a large SINR loss with respect to the known-covariance case.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".