🎧 AI-Based Acoustic Drone Detection Roadmap

Dashboard, Edge Deployment & Sensor Fusion Roadmap

Dashboard Features Overview

• Real-time waveform and prediction
• Spectrogram display (live)
• Classification history and timestamps
• Alert confidence and mic status Export to
• CSV / C2 integration

Edge Deployment Architecture

• Device: Raspberry Pi 4 + USB Mic
• Model: TensorFlow Lite (.tflite)
• Interface: Python + SoundDevice
• Outputs: Local log / Alert API / MQTT
• Status: Headless or lightweight dashboard

Multi-Sensor Fusion Strategy

• Inputs: Acoustic + RF + Radar + EO/IR
• Fusion Methods: Rule-based or Bayesian
• Output: Threat score and response level
• Integration: MQTT or REST API across sensors

Step-by-Step Implementation

1. Convert acoustic model to .tflite
2. Set up Pi + mic + TFLite runtime
3. Build Python listener with sounddevice
4. Route results to local dashboard or broker
5. Expand to Fusion node (connect RF/EO/IR)
6. Calibrate and log real-time detections