A Study of the LCMV and MVDR Noise Reduction Filters

Souden, Mehrez; Benesty, Jacob; Affes, Sofiène

doi:10.1109/tsp.2010.2051803

Cited by 134 publications

(68 citation statements)

References 17 publications

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“…The AOA of the TPMS signal can be determined when the TPMS is installed in a vehicle. The MVDR weight vector based on AOA is calculated by [17] min w R r w subject to a ℓ w = 1,…”

Section: Mvdr Beamformer For Tpmsmentioning

confidence: 99%

TPMS Interference Suppression Based on Beamforming Technology

Park

Kim

Hwang

2013

International Journal of Distributed Sensor Networks

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The Tire Pressure Monitoring System (TPMS) displays the state of a tire on a display to inform a driver of the relevant information, by means of a sensor installed on the tire of the vehicle. When the data measured from the tire are wirelessly transmitted to a receiving antenna located in the center of the vehicle, the exact transmission of data is affected by the interference from various external electronic and electrical devices. In this paper, we suggest a minimum variance distortionless response (MVDR) beamformer based on the angle-of-arrival (AOA) vector for suppressing external interference and receiving accurate data. Although the MVDR beamformer effectively suppresses the interference, it has high computational complexity because of the calculation of an autocorrelation matrix. In order to address this issue, we also suggest a generalized sidelobe canceler (GSC) beamformer which does not have the same performance of the interference suppression to MVDR but also has low computational complexity. Since the signal from each tire can cause interference to others, we consider utilizing a unique Gold Code to each tire to minimize intertire interference and reduce power consumption of a battery installed in each tire.

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“…The AOA of the TPMS signal can be determined when the TPMS is installed in a vehicle. The MVDR weight vector based on AOA is calculated by [17] min w R r w subject to a ℓ w = 1,…”

Section: Mvdr Beamformer For Tpmsmentioning

confidence: 99%

TPMS Interference Suppression Based on Beamforming Technology

Park

Kim

Hwang

2013

International Journal of Distributed Sensor Networks

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“…Using the minimum variance distortionless response (MVDR) beam forming method [28], the gain in the direction of desired signal is constrained to be 1, and the array output power is ensured to be minimum. Namely, the interference and noise produce the minimum output power.…”

Section: Theoretical Analysismentioning

confidence: 99%

On Adaptive Beamforming for Coherent Interference Suppression via Virtual Antenna Array

Li¹,

Li²,

Yu³

2012

PIER

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Abstract-In this paper, we propose Modified Interpolated Spatial Smoothing (MISS) algorithm that solves the problem when the inhibition gain generated by Interpolated Spatial Smoothing (ISS) algorithm is not sufficiently high in virtual antenna adaptive beam forming to suppress coherent interference.Using the subspace projection concept, this paper establishes an interference subspace spanned by the interference steering vectors of the virtual antenna array, and then the interference direction information can be imported into the transformation matrix by projecting the transformation matrix into the subspace, which will make the interference components in virtual smoothing covariance matrix enhanced as it is demonstrated by theoretical analysis. Employing the Minimum Variance Distortionless Response (MVDR) beam forming method, the interference inhibition gain and Signal to Interference and Noise Ratio (SINR) performance can be significantly improved.

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“…To overcome the limitations of single-channel systems, various multi-channel techniques have been developed, including the minimum variance distortionless response (MVDR) [7] and the multi-channel Wiener filter (MWF) with constraints [8][9][10][11][12]. The MVDR is a widely used spatial filter in multi-channel systems that minimizes output power under the constraint that the desired signal is not affected [7].…”

Section: Introductionmentioning

confidence: 99%

Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

Baek

Park

2017

J AUDIO SPEECH MUSIC PROC.

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In speech enhancement, noise power spectral density (PSD) estimation plays a key role in determining appropriate de-nosing gains. In this paper, we propose a robust noise PSD estimator for binaural speech enhancement in timevarying noise environments. First, it is shown that the noise PSD can be numerically obtained using an eigenvalue of the input covariance matrix. A simplified estimator is then derived through an approximation process, so that the noise PSD is expressed as a combination of the second eigenvalue of the input covariance matrix, the noise coherence, and the interaural phase difference (IPD) of the input signal. Later, to enhance the accuracy of the noise PSD estimate in time-varying noise environments, an eigenvalue compensation scheme is presented, in which two eigenvalues obtained in noise-dominant regions are combined using a weighting parameter based on the speech presence probability (SPP). Compared with the previous prediction filter-based approach, the proposed method requires neither causality delays nor explicit estimation of the prediction errors. Finally, the proposed noise PSD estimator is applied to a binaural speech enhancement system, and its performance is evaluated through computer simulations. The simulation results show that the proposed noise PSD estimator yields accurate noise PSD regardless of the direction of the target speech signal. Therefore, slightly better performance in quality and intelligibility can be obtained than that with conventional algorithms.

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A Study of the LCMV and MVDR Noise Reduction Filters

Cited by 134 publications

References 17 publications

TPMS Interference Suppression Based on Beamforming Technology

TPMS Interference Suppression Based on Beamforming Technology

On Adaptive Beamforming for Coherent Interference Suppression via Virtual Antenna Array

Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

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