PK12 - 3D Acoustic Localization & Tracking of Indoor Insect Flight

Area: Robotics and IoT | Supervisor: Kalchgruber

Problem Statement: Detecting mosquitoes in indoor environments is a critical challenge for health and comfort. While visual tracking is often ineffective due to the insect's size and speed, their unique "wing-beat" frequency (typically 300 Hz – 700 Hz) provides a distinct acoustic signature. However, identifying this signature and tracking its movement in 3D space requires precise microsecond-level signal processing which is difficult to achieve on low-cost hardware.

Project Goal: The goal of this "hardware/systems" project is to design, build, and calibrate a sensor-based system capable of detecting and localizing a sound source in a room. The student will focus on the challenge of TDOA (Time Difference of Arrival) geometry and signal processing. 
Note: Due to the physical difficulty of recording real insects, the primary development and grading will be performed using a "Simulated Source" (Smartphone generating specific frequencies).

Core Tasks:

  1. Hardware Integration: Construct a time-synchronized microphone array. This involves wiring at least 4 digital MEMS microphones (I2S) to a central processing unit (ESP32 or Raspberry Pi) in a non-planar (3D) physical layout.
  2. Acoustic Calibration: Develop a routine to calculate the exact X/Y/Z coordinates of the microphones relative to each other (e.g., using a reference "chirp" signal).
  3. Signal Identification (DSP): Implement Fast Fourier Transform (FFT) or Bandpass Filtering to isolate the target frequency (e.g., 450 Hz) from background noise.
  4. 3D Localization Algorithm: Implement a TDOA multilateration algorithm (e.g., GCC-PHAT) to calculate the source's coordinates in real-time based on the delay between microphones.
  5. Visualization: Create a simple real-time dashboard (Python/Web) plotting the (estimated 3D) flight path of the source.

Research Focus: 
The research focus lies on a Localization Accuracy Evaluation. The student must conduct a controlled experiment (e.g., placing the sound source at 10 known grid points) and calculate the Mean Absolute Error (MAE) of the system's estimated X/Y/Z coordinates compared to the actual physical position.

Prerequisites:

  • Strong interest in Physics/Math and basic soldering skills.

Technologies: Digital Signal Processing (DSP); Microcontrollers (ESP32/Raspberry Pi); Python (SciPy, NumPy); Cross-Correlation (GCC-PHAT); TDOA/Multilateration

Tags: IoT; Signal Processing; Hardware; Cyber-Physical Systems; Sensor Data; Kalchgruber

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