Radar Sensing Missile Launching System (RSMLS)

Project Description: The Radar Sensing Missile Launching System (RSMLS) is an automated missile launching prototype designed to detect, track, and engage moving targets using radar and ultrasonic sensors. The system integrates AI-based trajectory prediction and intelligent threat classification to enhance precision and minimize human intervention. With IoT-enabled remote monitoring and control, the system provides a cost-effective and scalable solution for autonomous defense applications.

Overview: Traditional missile defense systems require significant human input and often rely on pre-programmed target data. RSMLS enhances automation by leveraging real-time sensor data and AI-driven analytics to predict target movements and determine optimal engagement strategies. The system is designed to be portable and modular, making it suitable for both research and field applications.

Hardware Components: Radar Sensors Ultrasonic Sensors ESP32 Microcontroller Raspberry Pi IoT Communication Modules

Software and Development Tools: Python (for AI and machine learning implementation) C++ (for ESP32 microcontroller programming) TensorFlow / PyTorch (for ML-based trajectory prediction) OpenCV (for object detection and tracking) MQTT / HTTP (for IoT-based remote monitoring)

Machine Learning Model: The system employs a deep learning model for:

1. Trajectory Prediction: Uses historical movement data to estimate the future path of a target. Implemented using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to improve accuracy.
2. Threat Classification: Uses a Convolutional Neural Network (CNN) to classify objects detected by the radar system, distinguishing between different target types (e.g., friendly vs. hostile).
3. Anomaly Detection: Incorporates an autoencoder-based anomaly detection model to identify unexpected movements or unidentified objects.

Innovative Aspects

1. Real-time AI-Powered Target Prediction, unlike traditional rule-based systems, RSMLS integrates deep learning for trajectory estimation, allowing it to predict and adjust engagement strategies dynamically. Hybrid Sensor Fusion Approach
2. The combination of radar and ultrasonic sensors enhances detection accuracy, compensating for environmental interferences that affect single-sensor systems. IoT-Enabled Remote Control and Monitoring
3. The system allows remote access via an IoT dashboard, enabling operators to monitor real-time target tracking and system status from any location. Portable and Cost-Effective Design 4, Uses off-the-shelf components such as ESP32 and Raspberry Pi, making the system affordable for research, testing, and small-scale defense applications. Adaptive Threat Classification
4. The AI model continuously learns and updates its classification criteria, improving accuracy over time without requiring manual reprogramming.