Radar-Based Surveillance for Intruder Identification with Embedded AI

Traditional surveillance systems often underperform in low-visibility or obstructed environments, posing a risk in military zones and other sensitive areas. This research introduces a robust, real-time intruder detection system combining Frequency-Modulated Continuous Wave (FMCW) radar and face recognition technology. The designed system integrates radar technology to identify motion, while a camera module is employed to carry out facial recognition. Embedded systems handle preprocessing and classification using machine learning techniques. When an unknown individual is detected, the system triggers local alerts and sends notifications via IoT platforms. The system is designed to ensure operational reliability in diverse environments, offering an efficient and cost-effective alternative to conventional surveillance.