Modified IQML and a Statistically Efficient Method for Direction Estimation without Eigendecomposition

• This paper deals with direction estimation of signals impinging on a uniform linear sensor array. A well known algorithm for this problem is iterative quadratic maximum likelihood (IQML). Unfortunately, the IQML estimates are in general biased, especially in noisy scenarios. We propose a modification of IQML (MIQML) that gives consistent estimates at approximately the same computational cost. In addition, an algorithm with an estimation error covariance which is asymptotically identical to the asymptotic Cramer-Rao lower bound is presented. The optimal algorithm resembles weighted subspace fitting or MODE, but achieves optimal performance without having to compute an eigendecomposition of the sample covariance matrix.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Costs

Covariance matrix

Data models

Direction of arrival estimation

Iterative algorithms

Maximum likelihood estimation

Sensor arrays

Sensor systems

Time sharing computer systems

Vectors

Publication and Content Type

ref (subject category)

kon (subject category)