A Security Radar System Based on the Ultrasonic Sensor, Servo Motor, and Raspberry Pi with Kalman Filtering
Bibliographic record
Abstract
This study presents the design and implementation of a remote-controlled radar system capable of detecting nearby objects and displaying real-time distance and position information through a Pygame-based graphical interface. Unlike previous works that relied on Arduino boards, this research integrates a Raspberry Pi 4 B with Python and Pygame, enabling faster processing and enhanced real-time visualization. The proposed system achieves a 270° scanning range, surpassing earlier ultrasonic radar systems typically limited to 180°. To improve measurement reliability, a Kalman filter was applied to reduce sensor noise and refine distance estimation. Experimental tests conducted at various ranges confirmed the system’s high performance, achieving an accuracy of 99.32%, which is significantly higher than comparable ultrasonic radar systems reported in the literature. The developed radar offers an effective, low-cost, and adaptable solution for object detection in multiple contexts, including semi-autonomous vehicles, security monitoring, navigation, and robotics applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".