scholarly journals Matched Field Processing Based on Bayesian Estimation

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1374
Author(s):  
Guolei Zhu ◽  
Yingmin Wang ◽  
Qi Wang
Keyword(s):  
Gaussian Noise ◽  
Posterior Probability ◽  
Side Lobe ◽  
Positioning Accuracy ◽  
Matched Field Processing ◽  
Moving Source ◽  
Bayesian Criterion ◽  
Posterior Probability Density ◽  
Ocean Environment ◽  
Localization And Tracking

In order to improve the robustness and positioning accuracy of the matched field processing (MFP) in underwater acoustic systems, we propose a conditional probability constraint matched field processing (MFP-CPC) algorithm in this paper, which protects the main-lobe and suppresses the side-lobe to the AMFP by the constraint parameters, such as the posterior probability density of source locations obtained by Bayesian criterion under the assumption of white Gaussian noise. Under such constraint, the proposed MFP-CPC algorithm not only has the same merit of a high resolution as AMFP but also improves the robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment is used. From the results, MFP-CPC is robust to the moored source, as well as the moving source. In addition, the localization and tracking performances of using the proposed algorithm are consistent with the trajectory of the moving source.

Geophysical signal processing using sequential Bayesian techniques

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. V87-V100 ◽  
Author(s):  
Caglar Yardim ◽  
Peter Gerstoft ◽  
Zoi-Heleni Michalopoulou
Keyword(s):  
Posterior Probability ◽  
Signal To Noise Ratio ◽  
Multiple Model ◽  
Nonstationary Processes ◽  
Ensemble Kalman Filters ◽  
Bayesian Techniques ◽  
Strongly Nonlinear ◽  
Posterior Probability Density ◽  
Geophysical Signal ◽  
Seismic Measurements

Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential observations. They provide a formulation in which the geophysical parameters that characterize dynamic, nonstationary processes are continuously estimated as new data become available. This is done by using prediction from previous estimates of geophysical parameters, updates stemming from physical and statistical models that relate seismic measurements to the unknown geophysical parameters. In addition, these techniques provide the evolving uncertainty in the estimates in the form of posterior probability density functions. In addition to the particle filters (PFs), extended, unscented, and ensemble Kalman filters (EnKFs) were evaluated. The filters were compared via reflector and nonvolcanic tremor tracking examples. Because there are numerous geophysical problems in which the environmental model itself is not known or evolves with time, the concept of model selection and its filtering implementation were introduced. A multiple model PF was then used to track an unknown number of reflectors from seismic interferometry data. We found that when the equations that define the geophysical problem are strongly nonlinear, a PF was needed. The PF outperformed all Kalman filter variants, especially in low signal-to-noise ratio tremor cases. However, PFs are computationally expensive. The EnKF is most appropriate when the number of parameters is large. Because each technique is ideal under different conditions, they complement each other and provide a useful set of techniques for solving sequential geophysical inversion problems.

A New Traceability Method for Sudden Water Pollution Accidents

2013 ◽  
Vol 807-809 ◽  
pp. 1570-1574 ◽  
Author(s):  
Hai Dong Yang ◽  
Dong Guo Shao ◽  
Bi Yu Liu
Keyword(s):  
Water Pollution ◽  
Probability Distribution ◽  
Numerical Experiments ◽  
Posterior Probability ◽  
Recognition Accuracy ◽  
Average Error ◽  
Bayesian Mcmc ◽  
Posterior Probability Distribution ◽  
Posterior Probability Density ◽  
Sudden Water Pollution

Pollution point source identification for the non-shore emission which is the main form of sudden water pollution incident is considered in this paper. Firstly, the source traceability of sudden water pollution accidents is taken as the Bayesian estimation problem; secondly, the posterior probability distribution of the source's parameters are deduced; thirdly, the marginal posterior probability density is obtained by using a new traceability method; finally, this proposed method is compared with Bayesian-MCMC by numerical experiments. The conclusions are as following: the new traceability method can reduce the iterations, improve the recognition accuracy, and reduce the overall average error obviously and it is more stable and robust than Bayesian-MCMC and can identify sudden water pollution accidents source effectively. Therefore, it provides a new idea and method to solve the difficulty of traceability problems in sudden water pollution accidents.

scholarly journals Performance Analysis of NLFM Signals with Doppler Effect and Background Noise

2020 ◽  
Vol 9 (3) ◽  
pp. 737-742
Keyword(s):  
Performance Analysis ◽  
Frequency Modulation ◽  
Gaussian Noise ◽  
Doppler Effect ◽  
Background Noise ◽  
Additive White Gaussian Noise ◽  
Side Lobe ◽  
Linear Functions ◽  
Side Lobe Level ◽  
Nonlinear Frequency

This paper focuses on the study of effect of background noise and Doppler Effect on various Nonlinear Frequency Modulation (NLFM) waveforms designed using two stage piece-wise and three stage piece-wise linear and non linear functions. The background noise investigated is Additive White Gaussian Noise (AWGN). Simulations are carried out for different target speeds ranging from 100 to 5000km/hour to study Doppler Effect. The simulations are carried out using Matlab software. Among the waveforms designed, the NLFM function designed using two piece-wise Linear Frequency Modulation (LFM) is observed to be Doppler tolerant and also not affected by noise, as the SNR changes from -20 to 20 dB the peak side-lobe level (PSL) of this signal is around -34.84 dB.

Matched field processing for a moving source

1989 ◽  
Vol 85 (S1) ◽  
pp. S16-S17
Author(s):  
John M. Ozard ◽  
Gary H. Brooke ◽  
Scott Tinis
Keyword(s):  
Matched Field Processing ◽  
Moving Source
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