Adaptive beamformer

About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.

Subband Likelihood-Maximizing Beamforming for Speech Recognition in Reverberant Environments

Abstract: In this paper, we introduce subband likelihood-maximizing beamforming (S-LIMABEAM), a new microphone-array processing algorithm specifically designed for speech recognition applications. The proposed algorithm is an extension of the previously developed LIMABEAM array processing algorithm. Unlike most array processing algorithms which operate according to some waveform-level objective function, the goal of LIMABEAM is to find the set of array parameters that maximizes the likelihood of the correct recognition hypothesis. Optimizing the array parameters in this manner results in significant improvements in recognition accuracy over conventional array processing methods when speech is corrupted by additive noise and moderate levels of reverberation. Despite the success of the LIMABEAM algorithm in such environments, little improvement was achieved in highly reverberant environments. In such situations where the noise is highly correlated to the speech signal and the number of filter parameters to estimate is large, subband processing has been used to improve the performance of LMS-type adaptive filtering algorithms. We use subband processing principles to design a novel array processing architecture in which select groups of subbands are processed jointly to maximize the likelihood of the resulting speech recognition features, as measured by the recognizer itself. By creating a subband filtering architecture that explicitly accounts for the manner in which recognition features are computed, we can effectively apply the LIMABEAM framework to highly reverberant environments. By doing so, we are able to achieve improvements in word error rate of over 20% compared to conventional methods in highly reverberant environments

Robust Adaptive Beamforming for General-Rank Signal Model With Positive Semi-Definite Constraint via POTDC

Abstract: The robust adaptive beamforming (RAB) problem for general-rank signal model with an additional positive semi-definite constraint is considered. Using the principle of the worst-case performance optimization, such RAB problem leads to a difference-of-convex functions (DC) optimization problem. The existing approaches for solving the resulted non-convex DC problem are based on approximations and find only suboptimal solutions. Here, we aim at finding the globally optimal solution for the non-convex DC problem and clarify the conditions under which the solution is guaranteed to be globally optimal. Particularly, we rewrite the problem as the minimization of a one-dimensional optimal value function (OVF). Then, the OVF is replaced with another equivalent one, for which the corresponding optimization problem is convex. The new one-dimensional OVF is minimized iteratively via polynomial time DC (POTDC) algorithm. We show that the POTDC converges to a point that satisfies Karush-Kuhn-Tucker (KKT) optimality conditions, and such point is the global optimum under certain conditions. Towards this conclusion, we prove that the proposed algorithm finds the globally optimal solution if the presumed norm of the mismatch matrix that corresponds to the desired signal covariance matrix is sufficiently small. The new RAB method shows superior performance compared to the other state-of-the-art general-rank RAB methods.

Adaptive antenna selection and Tx/Rx beamforming for large-scale MIMO systems in 60 GHz channels

Abstract: We consider a large-scale MIMO system operating in the 60 GHz band employing beamforming for high-speed data transmission. We assume that the number of RF chains is smaller than the number of antennas, which motivates the use of antenna selection to exploit the beamforming gain afforded by the large-scale antenna array. However, the system constraint that at the receiver, only a linear combination of the receive antenna outputs is available, which together with the large dimension of the MIMO system makes it challenging to devise an efficient antenna selection algorithm. By exploiting the strong line-of-sight property of the 60 GHz channels, we propose an iterative antenna selection algorithm based on discrete stochastic approximation that can quickly lock onto a near-optimal antenna subset. Moreover, given a selected antenna subset, we propose an adaptive transmit and receive beamforming algorithm based on the stochastic gradient method that makes use of a low-rate feedback channel to inform the transmitter about the selected beams. Simulation results show that both the proposed antenna selection and the adaptive beamforming techniques exhibit fast convergence and near-optimal performance.

Large-aperture imaging using transducer array with adaptive element pitch control

Abstract: An adaptive transducer array in which the element pitch is controlled by the imaging system depending on the mode of operation. A multiplicity of transducer elements are connected to a multiplicity of beamformer channels by a multiplexing arrangement having multiple states. In one multiplexer state, successive transducer elements are respectively connected to successive beamformer channels to produce an aperture having a small element pitch equal to the distance separating the centerlines of two adjacent transducer elements. In another multiplexer state, selected transducer elements are respectively connected to successive beamformer channels to produce an aperture having an increased element pitch equal to the small pitch multiplied by a factor of two or more. The aperture is increased by connecting together pairs of adjacent elements to a respective beamformer channel or by connecting every other element to a respective beamformer channel to form a sparse array.

A robust algorithm for linearly constrained adaptive beamforming

Abstract: A new approach to robust adaptive beamforming for wideband array signals is proposed. General steering vector errors, such as direction-of-arrival mismatch and array positional error, are modeled by "time-delay errors" and compensated for by self-adjusted interpolation filtering. The proposed method effectively overcomes the target-signal cancellation problem without suffering from loss in the degree of freedom for interference rejection, as verified by simulations.