Compressed Sensing Digital MIMO Radar Using a Non-Uniformly Spaced SIW Sparse Receiver Array
Bibliographic record
Abstract
A compressed sensing (CS) digital radar system based on a sparse array design is proposed for use in automotive collision-avoidance applications. The proof-of-concept radar system offers an enlarged antenna aperture, employing fewer elements and can distinguish targets at an angular separation of only 2 degrees for a bandwidth of 6.25%. This resolution is made possible using a multiple-input multiple-output (MIMO) configuration from the original sparse array which was implemented and tested using substrate integrated waveguide (SIW) technology. More specifically, the total aperture size (of the effective virtual receiver array) is$23.5\lambda $which is equivalent to a uniform-linear array (ULA) having 48 elements spaced at$0.5\lambda $apart. However, the total number of elements is 32. This defines a cost-effective setup offering a reduction of 16 elements which accounts for a 33% reduction in the number of required channels for the SIW array. Also, the radar exploits sparse-reconstruction techniques for target detection. Results of the simulations and measurements show that the performance of the proposed SIW antenna and experimentally verified radar system can offer competitive high-resolution detection when compared to other findings in the literature and to the best knowledge of the authors, no similar antenna and radar system implementation has been designed and experimentally verified.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".