Source localization based on matrix filter and sparse asymptotic minimum variance

2018 ◽  
Vol 144 (3) ◽  
pp. 1988-1988
Author(s):  
Yahao Zhang ◽  
Yixin Yang ◽  
Long Yang
Keyword(s):  
Source Localization ◽  
Minimum Variance

An Impact Source Localization on a Spherical Shell by Using Smoothed Wigner-Ville Distributions

2006 ◽  
Vol 321-323 ◽  
pp. 1274-1279 ◽  
Author(s):  
Jin Ho Park ◽  
Yang Hann Kim
Keyword(s):  
Spherical Shell ◽  
Group Velocity ◽  
Source Localization ◽  
Signal To Noise Ratio ◽  
Minimum Variance ◽  
High Signal ◽  
Time Of Arrival ◽  
Noisy Environment ◽  
Scanning Procedure ◽  
Group Velocities

A technique to localize an impact source for an elastic spherical shell is proposed. The conventional source localization techniques when the source is located on a dispersive medium, require both the time-of-arrival differences (TOADs) between the transducer signals and the group velocities. In practice, the material properties or the geometry of the medium are not fully informed, therefore the group velocity is not available. Furthermore, they are only applicable if we have a high signal-to-noise ratio (SNR). In this paper, we propose a method that can be applicable in practice, which does not need to know the group velocity, when we have a relatively small SNR. The scanning procedure over the structure to acquire a minimum variance point of the estimated group velocities is suggested. To reduce the noise effect, an exponential function is asymmetrically weighted in smoothed Wigner-Ville distributions (WVDs). Experiments have been conducted to confirm the validity of this method. As a result, the proposed technique is found to be effective for an impact source localization for a spherical shell without prior information on the group velocity, even in a noisy environment.

COMPUTATIONALLY EFFICIENT MONTE CARLO ESTIMATION ALGORITHMS FOR MATCHED FIELD PROCESSING IN UNCERTAIN OCEAN ENVIRONMENTS

1994 ◽  
Vol 02 (03) ◽  
pp. 285-314 ◽  
Author(s):  
J. A. SHOREY ◽  
L. W. NOLTE ◽  
J. L. KROLIK
Keyword(s):  
Monte Carlo ◽  
Source Localization ◽  
Upper Bound ◽  
Environmental Uncertainty ◽  
Minimum Variance ◽  
Monte Carlo Integration ◽  
Computationally Efficient ◽  
Localization Performance ◽  
Monte Carlo Estimation ◽  
Observation Times

In this paper, Monte Carlo estimation techniques are presented for computationally efficient implementation of two methods for matched field source localization in uncertain ocean channels. In the Optimal Uncertain Field Processor (OUFP), Monte Carlo integration is used to integrate out the environmental parameters and thus estimate the a posteriori distribution of the source location parameters. In the Minimum Variance Beamformer with Environmental Perturbation Constraints (MV-EPC), Monte Carlo estimation of the signal correlation matrix averaged over the ensemble of environmental realizations is used to estimate the beamformer constraints. Using the OUFP, detection performance bounds are evaluated which indicate that source position uncertainty affects performance much more than environmental uncertainty. An upper bound on source localization performance is also obtained indicating that for short observation times a threshold signal-to-noise ratio (SNR) exists, dependent upon environmental uncertainty, below which source localization performance rapidly degrades. Among robust minimum variance beamforming methods, the MV-EPC method demonstrated superior probability of correct localization (PCL), both in single source scenarios and in the presence of interference. The OUFP at high SNR and the MV-EPC at large observation times both achieved near perfect source localization performance, although for large environmental uncertainty the OUFP provides an upper bound on P CL .

A COMPARISON OF THE CONVENTIONAL, THE MINIMUM VARIANCE, AND THE MULTIPLE CONSTRAINT MATCHED FIELD PROCESSORS

1994 ◽  
Vol 02 (03) ◽  
pp. 217-229 ◽  
Author(s):  
ELLEN S. LIVINGSTON ◽  
HENRIK SCHMIDT
Keyword(s):  
Water Column ◽  
Source Localization ◽  
Minimum Variance ◽  
The Other ◽  
Test Cases ◽  
Sidelobe Suppression ◽  
Multiple Constraint ◽  
Simulated Test ◽  
Bottom Depth ◽  
Sidelobe Control

The multiple constraint processor (MCM) is known to be tolerant of water column mismatch while retaining sidelobe control. In this paper, the simulated test cases from the Spring 1993 MFP workshop, which additionally incorporate sediment parameter and bottom depth mismatch, are used to compare the conventional (Bartlett), the minimum variance (MV), and the MCM processors. Peak levels, range and depth estimates, and peak-to-high-sidelobe results are presented. The MCM is shown to maintain sidelobe suppression and correct source localization in all cases at 40 and 10 dB SNR, even when the other two fail. At −5 dB SNR, the MCM is still able to provide good localization in some cases when the Bartlett and the MV cannot.

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