A Low‐Cost FMCW Radar for Perimeter Surveillance With Suppression of Impact of Trees and Bushes as Clutter
Bibliographic record
Abstract
ABSTRACT Radar sensors are one of the most convenient and effective options for perimeter surveillance applications, where a narrow‐beam radar is needed due to the presence of various clutters, particularly trees and bushes, in urban and rural environments. In this paper, an efficient short‐range frequency modulated continuous wave radar is designed and implemented in the X‐band. The radar transceiver and antenna are built on microstrip substrates. A 2D fast Fourier transform algorithm is used to measure the target characteristics. In perimeter surveillance applications, it is challenging and costly to differentiate pedestrians from clutter due to the pedestrians' movement within the radar beam's cross‐section and their similar motion to nearby trees and bushes. In this work, a simple method is employed using two receiving antennas and one transmitting antenna, and a signal processing algorithm to artificially create a fixed beamwidth of less than 1.5 m over a range of 100 m, as opposed to the natural antenna beamwidth that would cover an increasingly larger area as distance from the antenna increases. The achieved fixed beamwidth prevents the detection of clutters such as moving trees in the surrounding area. The entire system is built and tested, confirming a 96%–100% error‐free motion detection.
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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 | 0.000 |
| 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 | 0.000 |
| 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".