Multiply-constrained MVDR matched field processing with a-posteriori constraints for enhanced robustness to mismatch

Zoltowski, M.D.; Kautz, G.M.; Chou, S.I.

doi:10.1109/acssc.1991.186449

Cited by 4 publications

(2 citation statements)

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“…In practice, we normalize it and express it as 10 log10(κ2/M)≤0 dB [43]. The WNC process is a better choice for baseline comparison because it has been shown in many articles that the MVDR has a better resolution than Bartlett MFP, but it is sensitive to environmental/system mismatch and needs enough snapshots to make trueK^Y full rank [2,3,5].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…For example, conventional (Bartlett) MFP is relatively robust, however suffering from some fundamental limitations such as low resolution and a high side lobe level. On the other hand, methods such as Minimum Variance Distortionless Response (MVDR) offer a high resolution and low side lobe level [4,5], however often showing significant sensitivity to environmental/system mismatch; hence, when the assumed and true models differ [6,7], the methods frequently give biased estimates [8].…”

Section: Introductionmentioning

confidence: 99%

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Passive Source Localization Using Compressive Sensing

Zhao

Irshad

Shi

et al. 2019

Sensors

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This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem. The signal strength on a specified grid is evaluated using sparse reconstruction algorithms by exploiting the spatial sparsity of the source signals. Our strategy leads to a high ratio of measurements to sparsity (RMS), an increase in the peak sharpness with a low side lobe level, and minimization of the dimensionality of the problem due to the formulation of the system equation of the multiple snapshots based on the data correlation matrix. Furthermore, to reduce the computational burden, pre-locating with Bartlett is presented. Our proposed technique can perform close to Bartlet and white noise gain constraint processes in the single-source scenario, but it can give slightly better results while localizing multiple sources. It exhibits the respective characteristics of traditionally used Bartlett and white noise gain constraint methods, such as robustness to environmental/system mismatch and high resolution. Both the simulated and experimental data are processed to demonstrate the effectiveness of the method for underwater source localization.

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