MUSIC Algorithm for DoA: Theory and FPGA Implementation

This course presents a structured introduction to the MUSIC algorithm for direction-of-arrival estimation. The complete workflow is covered, from theoretical principles and fixed-point modeling in Python to FPGA implementation and system integration using Vitis HLS.

What You'll Learn

Gain a clear understanding of the MUSIC algorithm and its underlying signal processing principles, including phased array operation, covariance matrix estimation, and subspace-based direction-of-arrival analysis.

Algorithm Understanding

Understand how the MUSIC algorithm is mapped to hardware using Vitis high-level synthesis and integrated as a reusable FPGA IP core.

FPGA implementation

Learn how to model and analyze fixed-point implementations, and how to evaluate precision–performance trade-offs resulting from quantization and finite word length effects.

Fixed-Point Design

Learn how the MUSIC accelerator is deployed, controlled, and validated on a Xilinx Zynq UltraScale+ platform, including software–hardware interaction and data transfer.

System integration

Course Structure

  • Introduction to phased antenna arrays, spatial sampling, steering vectors, and the basic principles of direction-of-arrival estimation.

  • Description Detailed walkthrough of the MUSIC algorithm, including covariance matrix estimation, subspace separation, and pseudospectrum computation.

  • Python-based fixed-point models of each processing stage, with analysis of quantization effects, scaling, and precision–performance trade-offs.

  • Mapping the MUSIC algorithm to hardware using Vitis HLS, including modular design, dataflow considerations, and synthesis constraints.

  • Integration of the MUSIC accelerator as an FPGA IP core, software–hardware interaction, and validation on a Xilinx Zynq UltraScale+ platform.

Get the Full FPGA Implementation

Enroll in the Extended Workshop