scholarly journals MVDR Algorithm Based on Estimated Diagonal Loading for Beamforming

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yuteng Xiao ◽  
Jihang Yin ◽  
Honggang Qi ◽  
Hongsheng Yin ◽  
Gang Hua
Keyword(s):  
Signal Processing ◽  
Covariance Matrix ◽  
Optimization Model ◽  
Matrix Theory ◽  
Minimum Variance ◽  
Signal Model ◽  
Diagonal Loading ◽  
Minimum Variance Distortionless Response ◽  
The Matrix ◽  
Beamforming Algorithm

Beamforming algorithm is widely used in many signal processing fields. At present, the typical beamforming algorithm is MVDR (Minimum Variance Distortionless Response). However, the performance of MVDR algorithm relies on the accurate covariance matrix. The MVDR algorithm declines dramatically with the inaccurate covariance matrix. To solve the problem, studying the beamforming array signal model and beamforming MVDR algorithm, we improve MVDR algorithm based on estimated diagonal loading for beamforming. MVDR optimization model based on diagonal loading compensation is established and the interval of the diagonal loading compensation value is deduced on the basis of the matrix theory. The optimal diagonal loading value in the interval is also determined through the experimental method. The experimental results show that the algorithm compared with existing algorithms is practical and effective.

Research on a New Array-Manifold of Single Vector Hydrophone

2014 ◽  
Vol 955-959 ◽  
pp. 899-910
Author(s):  
Bo Le Ma ◽  
Jing Fang Cheng ◽  
Chao Ran Zhang
Keyword(s):  
Signal Processing ◽  
Minimum Variance ◽  
Signal Classification ◽  
Beam Forming ◽  
Noise Power ◽  
Complex Data ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Minimum Variance Distortionless Response ◽  
Vector Hydrophone

For the purpose of improving the signal processing of single vector hydrophone, this paper combined two velocity signals as two complex data, so as to change array-manifold of single vector hydrophone. Taking two-dimension single vector hydrophone as an example, this paper compared the capability of signal processing of new array-manifold single vector hydrophone with old one from conventional beam-forming(CBF) ,minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC). As for CBF, the analysis indicates, the capability of spatial filtering of new array-manifold could improve 0.51db and the HPBW of new array-manifold will be smaller than old array-manifold. When the noise power is 0, the HPBW of new array-manifold will be narrower than old array-manifold 19.26°. As for MVDR, the capability of signal processing of new array-manifold is the same as old array-manifold. In MUSIC algorithm, the value measuring angle resolution shows the superiority of the new array-manifold- angle resolution. Simulation and measured data proved the better performance of the method presented by this paper.

Diagonal rejection-based minimum variance distortionless response for fiber underwater acoustic array

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740065
Author(s):  
Yang Chen ◽  
Ling Zou ◽  
Bin Zhou
Keyword(s):  
Covariance Matrix ◽  
Array Signal Processing ◽  
Spatial Filter ◽  
Minimum Variance ◽  
Observation Time ◽  
Small Aperture ◽  
Underwater Acoustic ◽  
Low Snr ◽  
Minimum Variance Distortionless Response ◽  
Acoustic Array

The high mounting precision of the fiber underwater acoustic array leads to an array manifold without perturbation. Besides, the targets are either static or slowly moving in azimuth in underwater acoustic array signal processing. Therefore, the covariance matrix can be estimated accurately by prolonging the observation time. However, this processing is limited to poor bearing resolution due to small aperture, low SNR and strong interferences. In this paper, diagonal rejection (DR) technology for Minimum Variance Distortionless Response (MVDR) was developed to enhance the resolution performance. The core idea of DR is rejecting the main diagonal elements of the covariance matrix to improve the output signal to interference and noise ratio (SINR). The definition of SINR here implicitly assumes independence between the spatial filter and the received observations at which the SINR is measured. The power of noise converges on the diagonal line in the covariance matrix and then it is integrated into the output beams. With the diagonal noise rejected by a factor smaller than 1, the array weights of MVDR will concentrate on interference suppression, leading to a better resolution capability. The algorithm was theoretically proved with optimal rejecting coefficient derived under both infinite and finite snapshots scenarios. Numerical simulations were conducted with an example of a linear array with eight elements half-wavelength spaced. Both resolution and Direction-of-Arrival (DOA) performances of MVDR and DR-based MVDR (DR–MVDR) were compared under different SNR and snapshot numbers. A conclusion can be drawn that with the covariance matrix accurately estimated, DR–MVDR can provide a lower sidelobe output level and a better bearing resolution capacity than MVDR without harming the DOA performance.

PARALLEL ALGORITHMS FOR ROBUST BROADBAND MVDR BEAMFORMING

2002 ◽  
Vol 10 (01) ◽  
pp. 69-96 ◽  
Author(s):  
PRIYABRATA SINHA ◽  
ALAN D. GEORGE ◽  
KEONWOOK KIM
Keyword(s):  
Parallel Algorithms ◽  
Execution Time ◽  
Minimum Variance ◽  
Autonomous Operation ◽  
Minimum Variance Distortionless Response ◽  
Distributed Array ◽  
The Matrix ◽  
Performance Results ◽  
Communication Demands ◽  
Sequential Regression

Rapid advancements in adaptive sonar beamforming algorithms have greatly increased the computation and communication demands on beamforming arrays, particularly for applications that require in-array autonomous operation. By coupling each transducer node in a distributed array with a microprocessor, and networking them together, embedded parallel processing for adaptive beamformers can significantly reduce execution time, power consumption and cost, and increase scalability and dependability. In this paper, the basic narrowband Minimum Variance Distortionless Response (MVDR) beamformer is enhanced by incorporating broadband processing, a technique to enhance the robustness of the algorithm, and speedup of the matrix inversion task using sequential regression. Using this Robust Broadband MVDR (RB-MVDR) algorithm as a sequential baseline, two novel parallel algorithms are developed and analyzed. Performance results are included, among them execution time, scaled speedup, parallel efficiency, result latency and memory utilization. The testbed used is a distributed system comprised of a cluster of personal computers connected by a conventional network.

scholarly journals Partial Component Consensus of Discrete-Time Multiagent Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Wenjun Hu ◽  
Gang Zhang ◽  
Zhongjun Ma ◽  
Binbin Wu
Keyword(s):  
Multiagent Systems ◽  
Discrete Time ◽  
Matrix Theory ◽  
Pinning Control ◽  
Directed Network ◽  
Strong Function ◽  
Partial Stability ◽  
Partial Component ◽  
The Matrix ◽  
Error System

The multiagent system has the advantages of simple structure, strong function, and cost saving, which has received wide attention from different fields. Consensus is the most basic problem in multiagent systems. In this paper, firstly, the problem of partial component consensus in the first-order linear discrete-time multiagent systems with the directed network topology is discussed. Via designing an appropriate pinning control protocol, the corresponding error system is analyzed by using the matrix theory and the partial stability theory. Secondly, a sufficient condition is given to realize partial component consensus in multiagent systems. Finally, the numerical simulations are given to illustrate the theoretical results.

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

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