Genetic algorithm based maximum likelihood DOA estimation

M. Li; Y. Lu

doi:10.1109/RADAR.2002.1174766

Genetic algorithm based maximum likelihood DOA estimation

M. Li, Y. Lu

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper

31 Citations (Scopus)

Abstract

The maximum likelihood (ML) direction-of-arrival (DOA) estimation method was one of the first to be investigated. For a long time, the complexity and computational load of maximizing the multivariable, highly nonlinear likelihood function prevented it from popular. We present the genetic algorithm (GA) for computing exact solutions to the likelihood function with almost guarantee of global convergence. The performance of GA-based ML and multiple signal classification (MUSIC) algorithm have been compared for a variety of scenarios of SNR, DOA separation, number of snapshots, and computational cost. The relationship between the ML technique and MUSIC is also investigated.

Original language	English
Pages	502-506
Number of pages	5
DOIs	https://doi.org/10.1109/RADAR.2002.1174766
Publication status	Published - Oct 2002
Event	RADAR 2002 Conference - Edinburgh, United Kingdom Duration: 15 Oct 2002 → 17 Oct 2002

Conference

Conference	RADAR 2002 Conference
Country/Territory	United Kingdom
City	Edinburgh
Period	15/10/02 → 17/10/02

Keywords

DOA estimation
maximum likelihood
direction-of-arrival

Access to Document

10.1109/RADAR.2002.1174766

Cite this

@conference{3bc04eae8cfb4e0196c983d1da7b6d48,

title = "Genetic algorithm based maximum likelihood DOA estimation",

abstract = "The maximum likelihood (ML) direction-of-arrival (DOA) estimation method was one of the first to be investigated. For a long time, the complexity and computational load of maximizing the multivariable, highly nonlinear likelihood function prevented it from popular. We present the genetic algorithm (GA) for computing exact solutions to the likelihood function with almost guarantee of global convergence. The performance of GA-based ML and multiple signal classification (MUSIC) algorithm have been compared for a variety of scenarios of SNR, DOA separation, number of snapshots, and computational cost. The relationship between the ML technique and MUSIC is also investigated.",

keywords = "DOA estimation , maximum likelihood , direction-of-arrival",

author = "M. Li and Y. Lu",

year = "2002",

month = oct,

doi = "10.1109/RADAR.2002.1174766",

language = "English",

pages = "502--506",

note = "RADAR 2002 Conference ; Conference date: 15-10-2002 Through 17-10-2002",

}

TY - CONF

T1 - Genetic algorithm based maximum likelihood DOA estimation

AU - Li, M.

AU - Lu, Y.

PY - 2002/10

Y1 - 2002/10

N2 - The maximum likelihood (ML) direction-of-arrival (DOA) estimation method was one of the first to be investigated. For a long time, the complexity and computational load of maximizing the multivariable, highly nonlinear likelihood function prevented it from popular. We present the genetic algorithm (GA) for computing exact solutions to the likelihood function with almost guarantee of global convergence. The performance of GA-based ML and multiple signal classification (MUSIC) algorithm have been compared for a variety of scenarios of SNR, DOA separation, number of snapshots, and computational cost. The relationship between the ML technique and MUSIC is also investigated.

AB - The maximum likelihood (ML) direction-of-arrival (DOA) estimation method was one of the first to be investigated. For a long time, the complexity and computational load of maximizing the multivariable, highly nonlinear likelihood function prevented it from popular. We present the genetic algorithm (GA) for computing exact solutions to the likelihood function with almost guarantee of global convergence. The performance of GA-based ML and multiple signal classification (MUSIC) algorithm have been compared for a variety of scenarios of SNR, DOA separation, number of snapshots, and computational cost. The relationship between the ML technique and MUSIC is also investigated.

KW - DOA estimation

KW - maximum likelihood

KW - direction-of-arrival

U2 - 10.1109/RADAR.2002.1174766

DO - 10.1109/RADAR.2002.1174766

M3 - Paper

SP - 502

EP - 506

T2 - RADAR 2002 Conference

Y2 - 15 October 2002 through 17 October 2002

ER -

Genetic algorithm based maximum likelihood DOA estimation

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this