Design considerations for a shipboard MIMO radar for surface target detection
Bibliographic record
Abstract
An active phased array radar or a navigation radar is used in many navies around the world for surface target detection. Multiple-input multiple-output (MIMO) radar differs from current technology by using orthogonal waveforms on transmission which allows it to form a virtual array and conduct beamforming on reception. These differences introduce many advantages, but also some critical design considerations. Specifically, Doppler returns from a target have a greater effect on a MIMO radar due to its inherit requirement to integrate longer to maintain the same signal to noise ratio (SNR) as a phased array radar (PAR). In this paper, a Simulink-based MIMO radar model is developed to evaluate the performance of a naval MIMO radar against a PAR and provides a design restriction on the coherent processing interval (CPI) for detecting moving targets while highlighting the importance of selecting an operating frequency. Simulation results demonstrate that Doppler returns have a more profound effect on the probability of detection in a MIMO radar than they do in a PAR. Simulations also show that a MIMO radar shares the same two-way beam pattern as a PAR when using the same antenna structure and that a MIMO radar searches a large area and refreshes the radar picture faster than a PAR, but at a cost of additional computations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".