3:30, SAM-P6.1
DIRECTION FINDING FOR A WAVEFRONT WITH IMPERFECT SPATIAL COHERENCE
O. BESSON, P. STOICA, A. GERSHMAN
We consider the direction-of-arrival (DOA) problem for a wavefront whose amplitude and phase vary randomly along the array aperture. This phenomenon can for instance originate from propagation through an inhomogeneous medium. A simple and accurate DOA estimator is derived in the case of an uniform linear array of sensors. The estimator is based upon a reduced statistic obtained from the sub-diagonals of the covariance matrix of the array output. It only entails computing the Fourier transform of an $(m-1)$-length sequence where $m$ is the number of array sensors. A theoretical expression for the asymptotic variance of the estimator is derived. Numerical simulations validate the theoretical results and show that the estimator has an accuracy very close to the Cram\'{e}r-Rao bound.

3:30, SAM-P6.2
A ROOT-MUSIC ALGORITHM FOR NON CIRCULAR SOURCES
P. CHARGÉ, Y. WANG, J. SAILLARD
We present in this paper a new direction finding algorithm for non circular sources that is based on polynomial rooting. Due to the non circularity characteristics of the impinging sources, the proposed method is able to handle more sources than sensors. By using a polynomial rooting instead of a searching technique the method is limited to linear uniformly spaced arrays. However, polynomial rooting reduces significantly computation cost and enhances resolution power. Computer simulations are used to show the performance of the algorithm.

3:30, SAM-P6.3
COMPARATIVE CONVERGENCE ANALYSIS OF EM AND SAGE ALGORITHMS IN DOA ESTIMATION
P. CHUNG, J. BOEHME
Abstract In this work, the convergence rates of direction of arrival (DOA) estimates using Expectation-Maximization (EM) and Space Alternating Generalized EM (SAGE) algorithms are investigated. EM algorithm is a well known recursive method for locating modes of a likelihood function which is characterized by simple implementation and stability. Unfortunately the slow convergence associated with EM makes it less attractive. The recently proposed SAGE algorithm, based on the same idea of data augmentation, preserves the advantage of simple implementation and has the potential to speed up convergence. Theoretical analysis shows that SAGE has faster convergence rate than EM under certain conditions. This conclusion is also supported by numerical experiments carried out over a wide range of SNRs and different numbers of snapshots.

3:30, SAM-P6.4
MAXIMUM LIKELIHOOD METHODS FOR BEARINGS-ONLY TARGET LOCALIZATION
L. KAPLAN, Q. LE, P. MOLNAR
In this work, we develop four maximum likelihood (ML) methods to localize a moving target using a network of acoustical sensor arrays. Each array transmits a direction-of-arrival (DOA) estimate to a central processor, which employs one of the localization techniques. The four ML approaches use different target signal models where the time retardation factor for the target position and the degradation of the target signal through the air may or may not be included in the model. We compare these methods along with a linear least squares approach through a number of simulations at various signal to noise levels.

3:30, SAM-P6.5
DIRECTION OF ARRIVAL ESTIMATION IN PARTLY CALIBRATED TIME-VARYING SENSOR ARRAYS
M. PESAVENTO, A. GERSHMAN, K. WONG
Direction of arrival estimation in partly calibrated time-varying sensor arrays Marius Pesavento, Alex B. Gershman, and Kon Max Wong We consider the direction finding problem in time-varying arrays composed of identically oriented subarrays displaced by unknown vector translations. A new eigenstructure-based estimator is proposed for such a class of partly calibrated sensor arrays.

3:30, SAM-P6.6
COMPUTATIONALLY EFFICIENT DOA ESTIMATION BASED ON LINEAR PREDICTION WITH CAPON METHOD
M. HIRAKAWA, H. TSUJI, A. SANO
Of the several methods for estimating the direction of multiple signals with an array antenna, superresolution direction-of-arrival (DOA) estimation techniques, such as MUSIC and ESPRIT, have been in the spotlight. Although the performance of these techniques is reliable, their computational costs are considerable. We propose a new DOA estimation technique using the linear prediction (LP) method in conjunction with the Capon method. In our proposed technique, the LP method is used to estimate the true and spurious DOAs, and the true DOAs can be selected by evaluating the relative signal powers obtained by Capon method. To estimate the number of true DOAs, we apply the values of Capon's array output power to the decision criterion, such as minimum description length (MDL). Simulation results showed that the proposed technique gives a maximum of about eighty-percent in computational cost reduction compared with MUSIC and that the technique accurately estimated the DOAs.

3:30, SAM-P6.7
A MAXIMUM-LIKELIHOOD PARAMETRIC APPROACH TO SOURCE LOCALIZATIONS
J. CHEN, R. HUDSON, K. YAO
Source localization using passive sensor arrays has been an active research problem for many years. Most near-field source localization algorithms involve two separate estimations, namely, relative time-delay estimations and source location estimations. In this paper, a one-step maximum-likelihood parametric source localization algorithm is proposed based on the maximum correlation between phase shifted sensor data at the true source location. The performance of the algorithm is evaluated and shown to approach the Cramer-Rao bound asymtotically in simulations.

3:30, SAM-P6.8
THE STOCHASTIC CRB FOR ARRAY PROCESSING IN UNKNOWN NOISE FIELDS
A. GERSHMAN, M. PESAVENTO, P. STOICA, E. LARSSON
The stochastic CRB for array processing in unknown noise fields Alex B. Gershman, Marius Pesavento, Petre Stoica, and Erik G. Larsson The stochastic Cramer-Rao bound (CRB) plays an important role in array processing because several high-resolution direction-of-arrival (DOA) estimation methods are known to achieve this bound asymptotically. In this paper, we study the stochastic CRB on DOA estimation accuracy in the general case of arbitrary unknown noise field parametrized by a vector of unknowns. We derive explicit closed-form expressions for the CRB and examine its properties theoretically and by representative numerical examples.

3:30, SAM-P6.9
DECOUPLED ESTIMATION OF DOA AND COHERENCE LOSS FOR MULTIPLE SOURCES IN UNCERTAIN PROPAGATION ENVIRONMENTS
J. RINGELSTEIN, L. SCHMITT, J. BOEHME
In this paper, the problem of Direction of Arrival (DOA) estimation of multiple sources is addressed considering possible coherence loss along the impinging wavefronts. The loss results from wave propagation through a fluctuating medium and leads to a decreasing signal correlation from sensor to sensor. Two new algorithms are proposed that are significantly less computationally complex than the well-known Covariance Matching (CM) approach. Furthermore a polynomial approximation of the coherence loss parameters is introduced, which permits a decoupling of the DOA estimation from the estimation of all other parameters. The proposed algorithms and theoretical results are verified by numerical examples.