Topic
Adaptive beamformer
About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.
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19 Apr 2009
TL;DR: This new WL beamformer, that is derived from an original orthogonal decomposition, is shown to always improve the performance of both the well-known Capon's beamformer and a WL MVDR beamformer introduced recently in the literature.
Abstract: This paper introduces the optimal widely linear (WL) minimum variance distorsionless response (MVDR) beamformer for the reception of an unknown signal of interest (SOI) corrupted by potentially second order (SO) noncircular background noise and interference. The SOI, whose waveform is unknown, is assumed to be SO noncircular with arbitrary noncircular properties. In the steady state and for SO noncircular SOI and/or interference, this new WL beamformer, that is derived from an original orthogonal decomposition, is shown to always improve the performance of both the well-known Capon's beamformer and a WL MVDR beamformer introduced recently in the literature. This optimal WL MVDR beamformer is first introduced and some of its performance are analyzed. Then, several adaptive implementations of this optimal WL beamformer are presented.
83 citations
19 Apr 2009
TL;DR: Simulation results show the substantial improvements offered by the proposed technique as compared to previous techniques that validate its effectiveness.
Abstract: We propose a new technique for multiple-input multiple-output (MIMO) radar with colocated antennas. The essence of the proposed technique is to partition the transmitting array into a number of subarrays that are allowed to overlap. Each subarray is used to coherently transmit a waveform which is orthogonal to the waveforms transmitted by other subarrays. Coherent processing gain can be achieved by designing a weight vector for each subarray to form a beam towards a certain direction in space. Moreover, the subarrays are combined jointly to form a MIMO radar resulting in higher resolution capabilities. Simulation results show the substantial improvements offered by the proposed technique as compared to previous techniques that validate its effectiveness.
83 citations
TL;DR: A two-microphone noise reduction technique was tested with four cochlear implant patients and showed large improvements in speech intelligibility for all patients, when compared with a strategy in which the two microphone signals were simply added together.
Abstract: A two‐microphone noise reduction technique was tested with four cochlear implant patients. The noise reduction technique, known as adaptive beamforming (ABF), used signals from only two microphones—one behind each ear—to attenuate sounds not arriving from the direction directly in front of the patient. The algorithm was implemented in a portable digital signal processor, and was compared with a strategy in which the two microphone signals were simply added together (two‐microphone broadside strategy). Tests with the four patients were conducted in a soundproof booth with target speech arriving from in front of the patient and multitalker babble noise arriving at 90 deg to the left. Results at 0‐dB signal‐to‐noise level (S/N) showed large improvements in speech intelligibility for all patients, when compared to the two‐microphone broadside strategy. Precautions were taken to avoid cancellation of the target speech, and, accordingly, subjective tests showed no deterioration in performance for the adaptive beamformer in quiet. Physical measurement of the directional characteristics of the ABF was made with the microphones placed behind the ears of a KEMAR manikin and in the same acoustic environment as used with the patients. Results showed directional gain of approximately 10 dB when the angle of incidence for interfering noise was shifted more than 20 to 30 deg from directly in front of or behind the manikin. The effects of reverberation were explored by placing the manikin in different acoustical environments and observing the attenuation of the noise alone at various angles. A near anechoic environment allowed the noise to be attenuated by as much as 21 dB as the angle of incidence of the noise shifted away from directly in front of or behind the manikin. In a highly reverberant concrete stairwell, the adaptive beamformer was, however, unable to provide any directional gain beyond about 3 dB for any angle of incidence.
83 citations
TL;DR: A theoretical analysis of the signal-to-interference-plus-noise ratio (SINR) for the class of beamformers based on generalized loading of the covariance matrix in the presence of random steering vector errors is presented.
Abstract: Robust adaptive beamforming is a key issue in array applications where there exist uncertainties about the steering vector of interest. Diagonal loading is one of the most popular techniques to improve robustness. In this paper, we present a theoretical analysis of the signal-to-interference-plus-noise ratio (SINR) for the class of beamformers based on generalized (i.e., not necessarily diagonal) loading of the covariance matrix in the presence of random steering vector errors. A closed-form expression for the SINR is derived that is shown to accurately predict the SINR obtained in simulations. This theoretical formula is valid for any loading matrix. It provides insights into the influence of the loading matrix and can serve as a helpful guide to select it. Finally, the analysis enables us to predict the level of uncertainties up to which robust beamformers are effective and then depart from the optimal SINR.
83 citations
TL;DR: A novel adaptive beamforming design, of linear complexity with respect to the number of unknown parameters, to such a constrained nonlinear learning problem is derived by employing a very recently developed version of the adaptive projected subgradient method (APSM).
Abstract: This paper establishes a new paradigm for convexly constrained adaptive learning in reproducing kernel Hilbert spaces (RKHS). Although the technique is of a general nature, we present it in the context of the beamforming problem. A priori knowledge, like beampattern specifications and constraints concerning robustness against steering vector errors, takes the form of multiple closed convex sets in a high (possibly infinite) dimensional RKHS. Every robustness constraint is shown to be equivalent to a min-max optimization task formed by means of the robust statistics epsiv -insensitive loss function. Such a multiplicity of specifications turns out to obtain a simple expression by using the rich frame of fixed-point sets of certain mappings defined in a Hilbert space. Moreover, the cost function, that the final solution has to optimize, is expressed as an infinite sequence of convex, nondifferentiable loss functions, springing from the sequence of the incoming training data. A novel adaptive beamforming design, of linear complexity with respect to the number of unknown parameters, to such a constrained nonlinear learning problem is derived by employing a very recently developed version of the adaptive projected subgradient method (APSM). The method produces a sequence that, under mild conditions, exhibits properties like the strong convergence to a beamformer that satisfies all of the imposed constraints, and in the meantime asymptotically minimizes the sequence of the loss functions imposed by the training data. The numerical examples demonstrate that the proposed method displays increased resolution in cases where the classical linear beamforming solutions collapse. Moreover, it leads to solutions, which are in agreement with the imposed a priori knowledge, as opposed to unconstrained online kernel regression techniques.
83 citations