This paper investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the deterministic and stochastic Cramer-Rao bound (CRB) and the deterministic maximum-likelihood (ML) DOA estimator under this model have been derived in M. Pesavento and A. Gershman, (July 2001), the stochastic ML DOA estimator under the same setting is still not available in the literature. In this paper, a new stochastic ML DOA estimator is derived. Its implementation is based on an iterative procedure which stepwise concentrates the log-likelihood function with respect to the signal and noise nuisance parameters. A modified inverse iteration algorithm is also presented for the estimation of the noise parameters. Simulation results have shown that the proposed algorithm is able to provide significant performance improvement over the conventional uniform ML estimator in nonuniform noise environments and require only a few iterations to converge to the nonuniform stochastic CRB.

This paper deals with the problem of estimating the directions-of-arrival (DOA) of multiple wideband sources using an array of sensors. While a variety of estimation techniques have been proposed in the literature, the maximum-likelihood (ML) DOA estimator has been shown to have superior performance under many challenging environments. In this paper, we propose a novel implementation for ML DOA estimator based on the cross-entropy (CE) method. Simulation results show the CE algorithm converges to the Cramer-Rao bound (CRB) in all scenarios within several iterations and the convergence speed is insensitive to the coherence of sources.

Modern communication, control, avionic, and radar systems require the use of computationally intensive algebraic operations for real-time high throughput filtering, estimation, tracking, direction-of-arrival, and localization purposes. In this overview paper, we first review some basic systolic array (SA) concept, then SA algorithms for digital filtering, recursive least-squares, QR decomposition, Kalman filtering, eigenvalue and singular value decompositions will be discussed.

We propose a novel algorithm employing particle filters for acoustic source tracking in a reverberant environment. By incorporating the likelihood function computed through approximate maximum-likelihood (AML) method, the proposed algorithm is applicable to wideband sources and can be implemented for multiple sources tracking. Both computer simulation and experimental results show the effectiveness of the proposed algorithm

We consider a digital signal processing sensor array system, based on randomly distributed sensor nodes, for surveillance and source localization applications. In most array processing the sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering.

Microphone array technology has been proposed for various audio, teleconference, hearing aid and voice recognition applications. By forming a focused beam toward the desired speech source, attenuating background noises and rejecting discrete spatial interferers, a microphone array can enhance the signal-to-noise-ratio (SNR) in a noisy environment with notable improvement in speech intelligibility. At the threshold of intelligibility, a one dB improvement in SNR can increase 10-15% speech intelligibility. It is also known that increasing SNR can result in significant improvement in the recognition rate of various automatic voice recognition systems. We propose a novel electronically steerable microphone array based on the maximum energy (ME) concentration criterion, which results in high SNR in rooms with reverberations and competing interferences. We present a prototype PC-based microphone array system designed for hearing aid applications but also applicable to other tasks.

Microphone array technology has been proposed for various audio, teleconference, and hearing aid applications. By forming a focused beam toward the desired speech source, attenuating background noises and rejecting discrete spatial interferers, a microphone array can enhance the SNR/SIR in a noisy environment with significant improvement in speech intelligibility. An array can also perform real time source-localization or direction-of-arrival (DOA) estimation in various applications. We present a high performance prototype PC-based microphone array system for hearing aid applications. Algorithms for maximum energy criterion array weight design needed in the speech processing mode as well as modified broadband near-field MUSIC schemes in the search mode are discussed. Then a PC-based microphone array system using a TMS320C40 DSP is described. Preliminary study of equalizing non-uniform response microphones is also discussed. Finally, some array performance results in free-space and reverberant room conditions are presented.

In this paper, we consider array processing techniques for broadband signals. The optimisation techniques and associated performance results correspond to steerable but fixed beam microphone arrays, to be used in a variety of applications, both in free-space and in reverberant conditions. The proposed arrays maximize the energy concentration around the desired look directions, minimize reverberant disturbance, cancel individual interferers, and control the amount of signal distortion. Optimization criteria for array subband processing are also given. The uniformly spaced subband and the non-uniformly spaced subband using quadrature mirror filter approaches are treated. Finally, various simulation results for free-space and reverberant conditions are presented to demonstrate the usefulness of this class of microphone array, as well as the feasibility of quadrature mirror filter-based subband processing.

In this paper we consider the algorithm for SVD updating based on Jacobi rotations. In order to overcome the tradeoff between accuracy and updating rate intrinsic in the original algorithm, we propose two schemes which improve the overall performance when the rate of change of the data is high. In the "variable rotational rate" scheme, the number of Jacobi rotations per update is dynamically determined. In the "variable forgetting factor" approach, the effective width of the observation adjusts to the data nonstationarity. The former scheme ensures closeness to convergence at all times, while the latter adapts the response to data variation. We consider applications of the SVD updating algorithm to speech processing of segmentation, adaptive parameter estimation, and glottal closure detection.

We consider an optimum broadband equally spaced linear array of R sensors each having L taps based on maximum energy concentration over some desired spatial "look" and frequency regions subject to user imposed spatial and frequency attenuation constraints. This problem is first expressed as a constrained maximization of a generalized Rayleigh ratio where the matrices in the ratio are specified by the spatial and frequency domains and the constraining subspace is specified by the array values, derivative values, and spatial locations. The optimum array weight vector is then obtained as a solution of a transformed computationally tractable unconstrained full-rank lower-dimensional generalized eigenvalue problem. Numerical examples are given to illustrate the usefulness of this approach.>

In many fields of signal and image processing control, and telecommunication there is much interest today in the numerical techniques offered by linear algebra. The singular value decomposition (SVD) is one of the techniques which have proven useful in many engineering applications, but unfortunately its computation is a costly procedure. The QR factorization (QRF) requires much less computational effort, but rank and null-space estimates are not necessarily reliable. This paper presents a version of rank revealing QR (RRQR) algorithm which is suited for implementation on a VLSI systolic array. The implementation of the RRQRF requires n(n+1)/2 processors and O(n) external buffers, for a problem of order n. The execution time for the algorithm is O(nr), where r is A's numerical rank.>

Design methodologies which incorporate partitioning in the projection of an algorithm onto a physical array of processors are of theoretical and practical interest in systolic/wavefront designs. A systematic partitioning technique utilizing the integral matrix approach for designing fixed-size arrays with desired user-imposed constraints is presented. These include I/O and interconnection constraints to obtain efficient communication link properties. Applications to the QR algorithm for eigenvalue decomposition and least square problems are given.>

The total least squares (TLS) technique is will known in numerical analysis and modern signal processing, while correspondence analysis (CA) has found applications in applied data and clustering analysis. Both techniques use the SVD method to determine their respective optimum solutions. Upon an appropriate preprocessing operation of centering the data matrix, it is shown by minimizing the energy of the perturbation imposed on the data matrix that the two approaches are equivalent. Some simple graphical interpretations and examples are given.>

The effects of imperfect carrier phase recovery on uncoded and trellis-encoded PSK (phase-shift keying) are compared. A recently proposed clock-aided carrier recovery scheme is analyzed and compared to the standard frequency multiplication circuit. Results obtained through a combination of analysis and computer simulation compare uncoded 4-PSK with trellis-encoded 8-PSK carrying two bits per channel symbol. The quality of the recovered carrier phase is evaluated using the ratio of tone power to background. The system error probability is evaluated and used to determine how accurate the time clock recovery must be in a clock-aided carrier recovery circuit in order to outperform classical recovery systems. It is shown that if the clock recovery system is accurate enough, clock-aided carrier recovery used with coded 8-PSK can compensate for the increased sensitivity of the latter to phase errors.>