Adaptive beamformer

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

Dereverberation for reverberation-robust microphone arrays

Abstract: This paper discusses using dereverberation technologies to improve the robustness of beamforming microphone arrays against reverberation. It is known that, while conventional adaptive beamformers are able to effectively cancel the interference from spatially separate noise sources in the absence of reverberation, their efficacy deteriorates in reverberant rooms. To provide a widely applicable solution to this problem, we propose an adaptive multiple point blind equalizer that can shorten all the room impulse responses between the sound sources and microphones. This algorithm reduces the impact of reverberation on the interference cancellation performance when it is used to pre-process the microphone signals prior to beamforming. Experimental results using real conversation data show that a system combining the proposed equalizer and the adaptive beamformer improves noise reduction performance without degrading the speech quality.

Signal processing algorithms for mimo radar

Abstract: The main contribution of this thesis is to study the signal processing issues in MIMO radar and propose novel algorithms for improving the MIMO radar system. In the first part of this thesis, we focus on the MIMO radar receiver algorithms. We first study the robustness of the beamformer used in MIMO radar receiver. It is known that the adaptive beamformer is very sensitive to the DOA (direction-of-arrival) mismatch. In MIMO radar, the aperture of the virtual array can be much larger than the physical receiving array in the SIMO radar. This makes the performance of the beamformer more sensitive to the DOA errors in the MIMO radar case. In this thesis, we propose an adaptive beamformer that is robust against the DOA mismatch. This method imposes constraints such that the magnitude responses of two angles exceed unity. Then a diagonal loading method is used to force the magnitude responses at the arrival angles between these two angles to exceed unity. Therefore the proposed method can always force the gains at a desired interval of angles to exceed a constant level while suppressing the interferences and noise. A closed form solution to the proposed minimization problem is introduced, and the diagonal loading factor can be computed systematically by a proposed algorithm. Numerical examples show that this method has an excellent SINR (signal to noise-plus-interference ratio) performance and a complexity comparable to the standard adaptive beamformer. We also study the space-time adaptive processing (STAP) for MIMO radar systems. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (phased array radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this thesis, we explore the clutter space and its rank in the MIMO radar. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). Using this representation, a new STAP algorithm is developed. It computes the clutter space using the PSWF and utilizes the block diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity. The second half of the thesis focuses on the transmitted waveform design for MIMO radar systems. We first study the ambiguity function of the MIMO radar and the corresponding waveform design methods. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. The idea of ambiguity function has recently been extended to the case of MIMO radar. In this thesis, we derive several mathematical properties of the MIMO radar ambiguity function. These properties provide some insights into the MIMO radar waveform design. We also propose a new algorithm for designing the orthogonal frequency-hopping waveforms. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions. Therefore the resolution of the MIMO radar system can be improved. In addition to designing the waveform for increasing the system resolution, we also consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. An extended target can be viewed as a collection of infinite number of point targets. The reflected waveform from a point target is just a delayed and scaled version of the transmitted waveform. However, the reflected waveform from an extended target is a convolved version of the transmitted waveform with a target spreading function. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. The numerical results show that the proposed iterative algorithms converge faster and also have significant better SINR performances than previously reported algorithms. (Abstract shortened by UMI.)

System and method for adaptive beamforming training using fixed time window for heterogeneous antenna systems

Abstract: A system and method for performing a beamforming training between heterogeneous wireless devices in a wireless network is disclosed. A number of time slots in a fixed-time period are assigned for transmit and/or receive sector training. The number of time slots assigned for transmit and/or receive sector training is based on an antenna configuration of a wireless station.

Adaptive beamforming using the constrained Kalman filter

Abstract: From the viewpoint of achieving rapid convergence, application of a Kalman filter to an adaptive array is considered. Compared with the Frost's (1972) constrained least-mean-square algorithm, the constrained Kalman algorithm for adaptive beamforming is proposed to overcome the problem of signal distortion along the look direction which occurs in the unconstrained Kalman beamformer of Baird (1974). A constraint on the array response along the look direction is added to the measurement equation of the Kalman filter. The weight vector of the constrained Kalman beamformer is derived and shown to converge to that of the minimum-variance distortionless-response beamformer. The convergence rate of the proposed algorithm is also analyzed. Compared to Baird's algorithm and the sidelobe canceller with one-step Kalman predictor, simulation results show the effectiveness of the proposed algorithm. >

Array gain/phase calibration techniques for adaptive beamforming and direction finding

Abstract: A calibration technique is proposed for handling adaptive beamforming and bearing estimation problems involving unknown perturbed sensor gain and phase. This calibration technique is applied on the MUSIC estimator, assuming that two or more signal sources (in which the direction of arrival of one or two of the signal sources are known temporarily) exist in the signal field, so as to estimate the true sensor gain and phase. The basic idea of the technique is to apply the first-order Taylor series expansion to approximate the true array steering vector from the nominal one. A set of linear equations is then formed, using the null characteristic of the MUSIC spectrum, from which the error steering vector (the difference between the actual steering vector and the nominal steering vector), which contains the gain/phase information of the array sensors, can be solved for. This technique exhibits relatively stable performance compared with existing techniques in the sense that it produces the required estimates consistently without the need for iterative computation and initialisation. This is illustrated with numerical results obtained from several Monte Carlo experiments.