Robust maximum likelihood acoustic source localization in wireless sensor networks

2020-02-26 16:23

Target Location Estimation in Sensor Networks With Quantized Data Ruixin Niu, Member, IEEE, and Pramod K. Varshney, Fellow, IEEE AbstractA signal intensity based maximumlikelihood (ML) target location estimator that uses quantized data is proposed for wireless sensor networks (WSNs). The signal intensity receivedMaximum Likelihood MultipleSource Localization Using Acoustic Energy Measurements with Wireless Sensor Networks Xiaohong Sheng and YuHen Hu, Fellow, IEEE AbstractA maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy robust maximum likelihood acoustic source localization in wireless sensor networks

CiteSeerX Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): AbstractA maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources.

Furthermore, it is shown that WDC attains CRB for the case of a white source. ; Since the consideration of system design is inevitably important, in Chapter 5, design rules for sensor network deployment for acoustic source localization are determined. However, existing maximum likelihood (ML) acoustic energy based source localization algorithms are very sensitive to nonGaussian noise perturbations.robust maximum likelihood acoustic source localization in wireless sensor networks Leveraging the distributed, innetwork processing nature of a WSN, a novel decentralized robust acoustic source localization (DRASL) algorithm is proposed. With the DRASL, local sensor nodes receive sensor readings broadcast from neighboring sensors and independently compute local location estimates using a lightweight Iterative Nonlinear Reweighted Least Square (INRLS) algorithm.

Robust maximum likelihood acoustic source localization in wireless sensor networks free

Robust Maximum Likelihood Acoustic Energy Based Source Localization in Correlated Noisy Sensing Environments Abstract: Acoustic energy based localization with wireless sensor networks is an interesting solution to locate sources and targets. robust maximum likelihood acoustic source localization in wireless sensor networks Abstract: A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Apr 27, 2010 Acoustic Source Localization in Wireless Sensor Network. The weights of each criterion function take into account the decrease in the signaltonoise ratio (SNR) with distance from the source. In addition, RSS localization algorithm proposed in this paper provides improvement of the localization accuracy for low SNR. Sensor measurements in a wireless sensor network (WSN) may significantly deviate from a commonly used Gaussian noise model due to harsh operating conditions, unreliable wireless communication links, or sensor failures. In this work, a mixed Gaussian and impulse noise model is proposed to more accurately model these types of nonGaussian noise.

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