Target Localization Performance of Binary-Optimized Antenna Placement Using Mutual Coherence Minimization
Bibliographic record
Abstract
Radar is becoming an attractive technology for automotive vehicles due to its robustness to weather conditions. However, the angular resolution of radar technology is dependent on the radar’s antenna size relative to the wavelength used, which, for achieving high angular resolution requires an impractically massive aperture and a large number of antennas. Recently, compressive sensing (CS) has allowed for the improvement of angular resolution in radar technology, which involves two principles: sparse signal recovery and sensing matrix design. Assuming a sparse target scene is present, CS radar performance hinges solely on the design of the sensing matrix. The design of the sensing matrix is a nuanced task, requiring it to possess certain properties, such as satisfying the restricted isometry property (RIP) and low coherence. The sensing matrix design depends on the location of the antennas, as well as the angular space discretization. In this work, we consider the antenna placement problem in CS radar. The problem is interpreted as a binary program to minimize the mutual coherence of the sensing matrix. The direct relationship between mutual coherence and peak-sidelobe level was exploited to place antenna elements with minimal peak-sidelobes. Due to the NP-hardness of the the antenna placement problem, we propose to solve it directly using a heuristic binary optimization algorithm, namely; using binary differential evolution (BDE) algorithm. Simulation results illustrate the superiority of approaching the problem directly using BDE rather than resorting to relaxation approaches in the literature in terms of achieving lower mutual coherence, higher probability of target detection, and low peak-sidelobe level. BDE showed consistent superior performance when compared with other methods in the literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".