Filtering environmental false alarms in moving object detection with millimeter-wave radar

This study focuses on detecting moving objects using millimeter-wave radar technology, specifically addressing false alarms caused by environmental factors such as rain and wind-driven vegetation. Millimeter-wave radar systems operate in the 76–81 GHz frequency band and offer high-resolution detection capabilities. However, they are highly sensitive to environmental interferences, which can lead to false positive detections, reducing their reliability in practical applications. To overcome these challenges, various filtering methods based on mathematical and statistical analysis were developed and tested in this study. The proposed methods aim to improve radar detection accuracy by analyzing the scalar and vector displacement patterns of detected objects. Field tests were conducted under controlled conditions, simulating scenarios with both vegetation movements and actual moving objects such as humans and vehicles. During the tests, displacement data were collected and analyzed to distinguish between oscillatory movements caused by vegetation and linear movements associated with humans or vehicles. Scalar displacement, representing the total distance traveled, and vector displacement, indicating the shortest distance between the start and end positions, were calculated for each detected object. The ratio of vector to scalar displacement was used as a key parameter for classification. Test results revealed that vegetation movements typically exhibited a lower vector-to-scalar displacement ratio, often below 42%, while human or vehicle movements demonstrated significantly higher ratios, exceeding 74%. Based on these findings, a threshold range of 55–60% was proposed to classify objects as either vegetation or non-vegetation. The developed algorithms effectively filtered out false alarms caused by vegetation, achieving a notable reduction in false positive rates and enhancing radar performance. Furthermore, the algorithms were designed to operate efficiently on resource-constrained systems such as microcontrollers, making them suitable for real-time applications. Future research is recommended to integrate velocity and acceleration analyses and explore the use of artificial intelligence methods to further enhance detection accuracy. This study contributes to the development of reliable radar systems capable of distinguishing real targets from environmental noise, expanding the applicability of millimeter-wave radar technology in security, automotive, and environmental monitoring systems.