Showing papers on "Adaptive beamformer published in 1972"

An algorithm for linearly constrained adaptive array processing

Abstract: A constrained least mean-squares algorithm has been derived which is capable of adjusting an array of sensors in real time to respond to a signal coming from a desired direction while discriminating against noises coming from other directions. Analysis and computer simulations confirm that the algorithm is able to iteratively adapt variable weights on the taps of the sensor array to minimize noise power in the array output. A set of linear equality constraints on the weights maintains a chosen frequency characteristic for the array in the direction of interest. The array problem would be a classical constrained least-mean-squares problem except that the signal and noise statistics are assumed unknown a priori. A geometrical presentation shows that the algorithm is able to maintain the constraints and prevent the accumulation of quantization errors in a digital implementation.

Sensitivity Considerations in Adaptive Beamforming

Abstract: : The theory of optimum arrays became widely known in the underwater acoustics community through the work of Bryn and Mermoz. The relationship of their work to other detection and estimation problems has been discussed. Optimum array processing structures use detailed information about the signal and noise fields. Since this information is not known precisely in advance, one is led naturally to adaptive beamformers which continually adjust their parameters based upon on-line measurements of some kind. Since adaptive processors are continually adjusting, it is natural to question how sensitive performance is to small variations of the signal field, noise field and system parameters from their assumed or estimated values. The question of sensitivities has been examined tin the past in conjunction with 'super-directive' arrays. An attempt will be made to point out the relationship of the results of this paper to those earlier results. The emphasis in this paper is on receiving arrays. In Section II an introduction to the problem is provided using an intuitive approach. The performance measures of array gain and output power are used. Section III presents the principal sensitivity results. The approach is to take partial derivatives of the gain and output signal power with respect to the size of signal, noise and steering perturbations. Section IV discusses the problem of signal suppression which arises in passive adaptive processors when measurements of signal-plus-noise are used when noise only measurements are desired. Interference rejection is also discussed. A number of optimization problems are discussed in Section V.

Passive Sonar Processing for Noise with Unknown Covariance Structure

Abstract: Passive processing is interpretation of the noise and noise like signals received by an array of hydrophones. Our model assumes that there is an important noise component about which very little is known, that the data are stationary, that a long data sample is available, that signal wavefronts are planar, and that the response of the array to plane waves is known. We propose a processor and compare it with high‐resolution frequency‐wavenumber spectrum analysis. Let F denote an estimate of the spectral density matrix, and let H denote any spectral density matrix that corresponds to a noise field composed of uncorrelated plane waves. A reasonable estimate of the spectral matrix of the signals and ambient noise is a value of H for which F‐H is positive semidefinite and trace H is maximized. The rationale for this estimate is closely related to a rationale for the estimate obtained by high‐resolution frequency‐wavenumber spectrum analysis (which is also called adaptive beamforming). The difference is that the former extracts the contribution from all directions at once, whereas the latter extracts the contribution from each direction separately. The former estimate rejects more unwanted noise than the latter.

The First Twenty Years of Acoustic Signal Processing

Abstract: The decade of the 1950s marks the beginning of acoustic signal processing as a subfield of underwater acoustics The forerunner, signal recognition, gradually evolved into a unified class of spatial and temporal processing techniques and theory which came to stand on its own The doctrines that emerged in that decade, such as spectrum analysis, correlation, and multiple beamforming, have continued to grow in sophistication, until we now find methods such as the fast Fourier transform, sequential detection, and adaptive beamforming handed down to us from the 1960s The milestones of signal processing over the past 20 years present a striking record of progress in that field

Linear array beamforming: Sensitivity to anisotropic noise and correlated sources

Abstract: This paper examines the influence of correlated wavefronts and directional noise (anisotropic background) on the performance of conventional and adaptive beamforming schemes used with linear arrays in the ocean environment.