VARTA - Open Source Acoustic Drone Detector

Visual and Acoustic Real-Time Alert System

⚠️ ALPHA STATUS - READ BEFORE PROCEEDING

This project provides a complete framework for building an acoustic drone detector, but it is not a plug-and-play solution. Before this device will detect anything, you must:

Collect training data - Record drone audio samples in your environment

Train the ML model - Run the training pipeline on your collected data

Assemble hardware - Build the device from components (~$80-100)

Calibrate and test - Tune detection thresholds for your conditions

Current limitations:

No pre-trained model included (you must train your own)

4-microphone simultaneous capture needs firmware work (currently single-mic)

Detection performance is theoretical until field-validated

Hardware design is breadboard-ready, not a finished PCB

This project is ideal for makers, researchers, and defense engineers who can contribute to development. It is not ready for non-technical end users expecting a finished product.

We welcome contributions - especially training data, field test results, and firmware improvements.

Overview

VARTA is an open-source, portable acoustic drone detection system designed to detect fiber optic controlled FPV drones that evade traditional RF-based detectors. Built for under $100 in components, it provides early warning capability for personnel in the field.

Why VARTA exists: RF drone detectors (like the BlueBird Chuyka) cannot detect fiber optic controlled drones because these drones don't emit radio signals. VARTA fills this critical gap using acoustic detection.

Key Features

4-microphone beamforming array for direction estimation
ML-based detection using TensorFlow Lite for edge inference
4-8 second detection at 200-500m range (environment dependent)
Visual + haptic + audio alerts for high-noise environments
8+ hours battery life on 2S 18650 pack
IP54 rated enclosure (3D printable)
$80-100 total BOM cost

Specifications

| Parameter | Value | |-----------|-------| | Detection Range | 200-500m (line of sight, low ambient noise) | | Detection Time | 4-8 seconds | | Direction Accuracy | ±15° azimuth | | Power Supply | 7.4V (2S 18650) | | Battery Life | 8+ hours continuous | | Operating Temp | -20°C to +50°C | | Enclosure Rating | IP54 | | Weight | ~350g with battery | | Dimensions | 140 x 80 x 45 mm |

How It Works

Detection Principle

Drone motors produce distinctive acoustic signatures in the 100-1000Hz range with characteristic harmonic patterns. VARTA uses:

FFT Analysis - Continuous spectral analysis looking for drone motor fundamentals (typically 150-400Hz) and their harmonics
Beamforming - Time-difference-of-arrival (TDOA) across 4 microphones estimates threat direction
ML Classification - TensorFlow Lite model distinguishes drones from environmental noise (vehicles, wind, wildlife)

Limitations

Range decreases significantly in high ambient noise (combat, vehicles, wind)
Cannot detect drones with silenced/baffled motors at useful range
False positives possible from other small motors, insects, etc.
No altitude estimation - only azimuth direction
Requires line-of-sight acoustically - terrain/structures block sound

Hardware

Bill of Materials

| Component | Quantity | Unit Cost | Notes | |-----------|----------|-----------|-------| | ESP32-S3-WROOM-1 | 1 | $8 | N16R8 variant recommended | | INMP441 MEMS Microphone | 4 | $3 | I2S output | | 0.96" OLED Display (SSD1306) | 1 | $4 | I2C, 128x64 | | 2.4" TFT Display (ILI9341) | 1 | $8 | SPI, optional for waveform | | Piezo Buzzer | 1 | $1 | 3-5V active | | Vibration Motor | 1 | $2 | 3V coin type | | WS2812B LED Ring (8 LED) | 1 | $3 | Direction indicator | | 18650 Battery Holder (2S) | 1 | $3 | With BMS | | 18650 Cells | 2 | $8 | 2500mAh+ recommended | | TP4056 Charger Module | 1 | $1 | USB-C preferred | | MT3608 Boost Converter | 1 | $1 | 5V for ESP32 | | Power Switch | 1 | $1 | SPST | | SMA Connectors (optional) | 4 | $4 | For external mics | | PCB (custom) | 1 | $10 | JLCPCB/PCBWay | | 3D Printed Enclosure | 1 | $5 | PLA or PETG | | Misc (wires, screws, etc.) | - | $5 | - |

Total: ~$85

Microphone Array Geometry

The 4 microphones are arranged in a square pattern with 50mm spacing:

        Front
          ↑
    [M1]-----[M2]
      |       |
      |   ●   |    ● = center/mounting point
      |       |
    [M4]-----[M3]
    
    Spacing: 50mm between adjacent mics

This geometry allows:

Azimuth estimation via TDOA
Reasonable angular resolution (~15°)
Compact form factor

Schematic

See /hardware/kicad/ for full KiCad project files.

Key connections:

Microphones (I2S): All 4 INMP441 share SCK (GPIO14) and WS (GPIO15), individual SD pins (GPIO4, 5, 6, 7)
OLED Display (I2C): SDA (GPIO21), SCL (GPIO22)
TFT Display (SPI): MOSI (GPIO23), SCK (GPIO18), CS (GPIO5), DC (GPIO16), RST (GPIO17)
LED Ring: Data (GPIO48)
Buzzer: GPIO38
Vibration Motor: GPIO39

Firmware

Building

Requires PlatformIO:

cd firmware
pio run
pio run --target upload

Configuration

Edit include/config.h:

// Detection thresholds (adjust based on field testing)
#define DETECTION_THRESHOLD_DB    65.0f   // Minimum signal level
#define CONFIDENCE_THRESHOLD      0.75f   // ML model confidence
#define ALERT_HOLDOFF_MS          2000    // Debounce alerts

// Hardware config
#define MIC_SPACING_MM            50.0f   // Physical mic spacing
#define SAMPLE_RATE               44100   // Audio sample rate
#define FFT_SIZE                  2048    // FFT window size

Operating Modes

SCAN (default) - Continuous monitoring, LED ring shows ambient level
ALERT - Threat detected, LED shows direction, buzzer + vibration active
MONITOR - Display shows real-time spectrogram (debugging)
CALIBRATE - Ambient noise profiling for location

Button controls:

Short press: Cycle display modes
Long press (3s): Enter calibration mode
Double press: Mute audio alerts (haptic remains)

Machine Learning

Model Architecture

Lightweight CNN designed for ESP32-S3:

Input: 128 mel-frequency bands x 32 time frames (1 second)
├── Conv2D(16, 3x3) + ReLU + MaxPool
├── Conv2D(32, 3x3) + ReLU + MaxPool
├── Conv2D(64, 3x3) + ReLU + GlobalAvgPool
├── Dense(32) + ReLU + Dropout(0.3)
└── Dense(2) + Softmax → [no_drone, drone]

Parameters: ~45,000
Inference time: ~50ms on ESP32-S3

Training Your Own Model

The default model provides baseline detection. For best results, train on data from your operational environment:

cd ml/training

# 1. Collect audio samples
python collect_samples.py --device your_mic --duration 300

# 2. Label samples (creates labels.csv)
python labeler.py --input samples/

# 3. Train model
python train.py --data samples/ --labels labels.csv --epochs 100

# 4. Convert to TFLite
python convert_tflite.py --model checkpoints/best.h5 --output ../models/

# 5. Generate C header for firmware
xxd -i models/drone_detector.tflite > ../firmware/src/model_data.h

Training Data Requirements

For effective detection, collect:

Positive samples: Various drone types at different distances/angles
Negative samples: Environmental noise from deployment location
- Wind, foliage rustling
- Distant vehicles, aircraft
- Voices, footsteps
- Wildlife (birds, insects)
- Combat sounds (if applicable)

Recommended: 2+ hours of each class, diverse conditions.

Enclosure

3D Printing

Files in /enclosure/:

varta_body.stl - Main enclosure
varta_lid.stl - Top cover with mic ports
varta_clip.stl - Belt/MOLLE clip

Print settings:

Material: PETG recommended (better temperature resistance than PLA)
Layer height: 0.2mm
Infill: 20%
Supports: Yes (for mic port overhangs)

Weatherproofing

For IP54 rating:

Apply silicone sealant to lid seam
Install acoustic vent membrane over mic ports (e.g., Gore-Tex patch)
Seal cable glands with silicone

Assembly

See docs/ASSEMBLY.md for step-by-step instructions with photos.

Quick overview:

Solder microphones to breakout boards (or direct to PCB)
Assemble main PCB with ESP32, power management
Wire display, buzzer, vibration motor
Install battery holder and cells
Mount PCB in enclosure
Position microphones in lid at correct spacing
Final wiring and close-up
Flash firmware and calibrate

Field Use

Pre-deployment

Power on and wait for self-test (LED sequence)
Verify GPS fix if equipped (optional module)
Run calibration in deployment location (30 seconds)
Verify alert function with test tone

Operational Notes

Position: Wear on shoulder/chest, mics facing likely threat direction
Ambient noise: Detection range decreases significantly with background noise
Terrain: Hills, buildings, vegetation attenuate sound - adjust expectations
Weather: Wind is the enemy - seek shelter or accept reduced range
False alarms: Initial deployments will have false positives; re-train model with local data

Interpreting Alerts

LED ring indicates estimated threat direction:

Red LED position: Threat azimuth (north = front, clockwise)
LED brightness: Signal strength / confidence
Pulsing: Active threat
Solid: Recent detection, threat may have passed

Roadmap

Current Status (v0.1-alpha)

| Component | Status | N

Varta

Install / Use

README