Adaptive beamformer

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

Robust Adaptive Beamforming With a Novel Interference-Plus-Noise Covariance Matrix Reconstruction Method

Abstract: Recently, a new robust adaptive beamforming (RAB) technique was proposed to remove the signal of interest (SOI) component from the sample covariance matrix based on interference-plus-noise covariance matrix reconstruction, which utilizes the Capon spectrum estimator integrated over a region separated from the direction of the SOI. However, the extreme condition of the reconstruction-based technique, that the precise information about the array structure is known in advance, is almost impossible in practice. In this paper, a novel method to reconstruct the interference-plus-noise covariance matrix is proposed. Considering the imprecise prior information about the array structure, which means that the array may be uncalibrated, we use an annulus uncertainty set to constrain the steering vectors of the interferences. Then we integrate the Capon spectrum over the surface of the annulus, by which we can obtain the reconstructed interference matrix without containing the SOI. Since the integral interval is a high-dimensional domain, which is very difficult to solve, we use a discrete sum method to calculate the integral approximately. With the reconstructed interference-plus-noise matrix, the nominal steering vector can be corrected via maximizing the beamformer output power by solving a quadratically constrained quadratic programming (QCQP) problem. The previous reconstruction method can be seen as a special case of the proposed one. The main advantage is that the proposed algorithm is robust against unknown arbitrary-type mismatches. Theoretical analysis and simulation results demonstrate the effectiveness and robustness of the proposed algorithm.

Shared phased array beamformer

Abstract: A shared beamformer flexibly allocates beams among phased array antenna apertures of a satellite (or other type of platform). By sharing the beamformer among the apertures, if one aperture is not providing useful coverage (due to orientation, traffic volume, signal frequency, signal polarization, etc.), the beams may be reallocated to one or more other apertures that are providing useful coverage. To share the beamformer among the phased array antenna apertures, a selector network (e.g., one or more switches) is used to select which particular apertures are to send or receive signals.

A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters

Abstract: This paper proposes a new robust adaptive beamformer applicable to microphone arrays. The proposed beamformer is a generalized sidelobe canceller (GSC) with a variable blocking matrix using coefficient-constrained adaptive digital filters (CCADFs). The CCADFs minimize leakage of target signal into the interference path of the GSC. Each coefficient of the CCADFs is constrained to avoid mistracking. The input signal to all the CCADFs is the output of a fixed beamformer. In a multiple-input canceller, leaky ADFs are used to decrease undesirable target-signal cancellation. The proposed beamformer can allow large look-direction error with almost no degradation in interference-reduction performance and can be implemented with a small number of microphones. The maximum allowable look-direction error can be specified by the user. Simulation results show that the proposed beamformer designed to allow about 20 degrees of look-direction error can suppress interference by more than 17 dB.

Adaptive beamformer orthogonal rejection test

Abstract: Research in the area of signal detection in the presence of unknown interference has resulted in a number of adaptive detection algorithms. Examples of such algorithms include the adaptive matched filter (AMF), the generalized likelihood ratio test (GLRT), and the adaptive coherence estimator (ACE). Each of these algorithms results in a tradeoff between detection performance for matched signals and rejection performance for mismatch signals. This paper introduces a new detection algorithm we call the adaptive beamformer orthogonal rejection test (ABORT). Our test decides if an observation contains a multidimensional signal belonging to one subspace or if it contains a multidimensional signal belonging to an orthogonal subspace when unknown complex Gaussian noise is present. In our analysis, we use a statistical hypothesis testing framework to develop a generalized likelihood ratio decision rule. We evaluate the performance of this decision rule in both the matched and mismatched signal cases. Our results show that for constant power complex Gaussian noise, if the signal is matched to the steering vector, ABORT, GLRT, and AMF give approximately equivalent probability of detection, higher than that of ACE, which trades detection probability for an extra invariance to scale mismatch between training and test data. Of these four tests, ACE is most selective and, therefore, least tolerant of mismatch, whereas AMF is most tolerant of mismatch and, therefore, least selective, ABORT and GLRT offer compromises between these extremes, with ABORT more like ACE and with GLRT more like AMF.