Performance Enhancement of the Measure-transformed Music Algorithm via Mse Based Optimization

Nir Halay, Koby Todros

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The measure-transformed (MT) MUltiple SIgnal Classification (MUSIC) algorithm is a robust MUSIC generalization that operates by applying a transform to the probability measure (distribution) of the data. In this paper, we first provide an asymptotic mean-squared-error (MSE) performance analysis of the MT-MUSIC algorithm. Under some mild assumptions, we show that the MT-MUSIC estimator is asymptotically normal and unbiased, and obtain an analytic expression for the asymptotic MSE matrix. We then proceed to develop a strongly consistent estimator for the asymptotic MSE matrix that is constructed from the same data samples being used for implementation of the MT-MUSIC. This paves the way for development of a data-driven procedure for optimal selection of the measure transformation parameters that minimizes an empirical estimate of the asymptotic average root MSE (RMSE). Simulation examples illustrate the performance advantage of the proposed MSE based optimization of the MT-MUSIC.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5187-5191
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - 1 May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • Array processing
  • DOA estimation
  • probability measure transform
  • robust statistics
  • signal subspace estimation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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