Simulation & Control

Engineering project

DSP Filter Design Toolkit (Aerospace)

Complete MATLAB DSP toolkit for avionics sensor data: adaptive noise cancellation, Kalman filtering, spectral analysis.

DSPMATLABFIR/IIRAdaptive Filtering

Project brief

A comprehensive MATLAB/Simulink signal processing library for aerospace sensor data analysis, used for post-flight FDR/CVR data analysis and real-time hardware-in-the-loop testing. The toolkit implements adaptive noise cancellation, multi-rate filter banks, spectral analysis, and provides real-time Simulink block library implementations for embedded deployment.

The deliverable is described as a signal-processing toolkit with both analysis and model integration paths. The visual explanation is a software workflow, not a measured spectrum.

The engineering challenge

Make sensor filtering and spectral analysis reusable across offline data review and real-time models.

Engineering approach

  1. Organize adaptive, multirate, and spectral functions as reusable processing blocks.
  2. Connect recorded-data parsers and language wrappers to the same analysis library.
  3. Use stated simulation and fixed-point paths to examine deployment behavior.

Features & capabilities

  • Adaptive noise cancellation: LMS, NLMS, RLS — MATLAB and fixed-point C
  • Multi-rate FIR/IIR filter design using Filter Design Toolbox (FDA Tool)
  • Spectral analysis: FFT, STFT, continuous wavelet transform (CWT)
  • FDR/CVR data parser: ARINC 717, CSMU output format support
  • Simulink block library for HIL deployment on dSPACE and Speedgoat
  • Python wrapper (ctypes) for FDR analysis tool integration
  • Monte Carlo noise analysis: distribution fitting, statistical characterization
  • Real-time oscilloscope-style visualization GUI in MATLAB App Designer

Software & engineering tools

MATLAB R2024a, Signal Processing Toolbox, DSP System Toolbox, Simulink, Python 3.11 ctypes wrapper