Electromagnetic matched-field processing: basic concepts and tropospheric simulations

Gingras, D.F.; Gerstoft, Peter; Gerr, Neil L.

doi:10.1109/8.633863

Cited by 44 publications

(41 citation statements)

References 28 publications

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“…Although the combination of ground-based GPS phase delay and propagation loss can infer refractivity parameters effectively, its performance is limited by the weak global search capability of NSGA-II. Therefore, in the present paper, we propose an improved algorithm based on NSGA-II in which the method of electromagnetic match-field processing is applied [36]. Furthermore, we also integrate the GPS phase delay and propagation loss from multiple antenna heights to infer the refractivity parameters.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

et al. 2018

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Abstract:The propagation of Global Positioning System (GPS) signals at low-elevation angles is significantly affected by a surface duct. In this paper, we present an improved algorithm known as NSSAGA, in which simulated annealing (SA) is combined with the non-dominated sorting genetic algorithm II (NSGA-II). Matched-field processing was used to remotely sense the refractivity structure by using the data of ground-based GPS phase delay and propagation loss from multiple antenna heights. The performance was checked by simulation data with and without noise. In comparison with NSGA-II, the new hybrid algorithm retrieved the refractivity structure more efficiently under various noise conditions. We then modified the objective function and found that matched-field processing is more effective than the conventional least-squares method for inferring the refractivity parameters. Comparing the inversion results and in situ sounding data suggests that the improved method presented herein can capture refractivity characteristics in realistic environments.

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Section: Introductionmentioning

confidence: 99%

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

et al. 2018

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“…Instead of using radar clutter returns, point-to-point microwave measurements have also been proposed as useful information for refractivity estimation. Gingras et al introduced basic concepts of electromagnetic matched-field processing (EM-MFP) techniques and performed simulations for simultaneously localizing an EM emitter and estimating surface-based duct parameters from synthetic complex-valued (amplitude and phase) field measurements [10]. Tabrikian et al proposed using point-to-point field measurements to estimate atmospheric duct parameters by the maximum a posteriori (MAP) method [11].…”

Section: Introductionmentioning

confidence: 99%

Evaporation Duct Height Estimation and Source Localization From Field Measurements at an Array of Radio Receivers

Zhao

2012

IEEE Trans. Antennas Propagat.

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Remote sensing of the atmospheric refractivity structure using signal strength measurements from a single emitter to an array of radio receivers has been proposed as a promising way for refractivity estimation. As a complement to the pioneers' published works, this paper focuses on addressing the problem of simultaneously estimating the evaporation duct height and localizing the source's position. The problem is organized as a multi-parameter optimization issue and genetic algorithm is adopted to search for the optimal solution from various trial parameters. The performance is determined via numerical simulations and mainly evaluated as a function of: 1) the geometry of the receiver array; 2) the transmitting frequency; and 3) the noise in the measurements.

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“…For known or well characterized media this problem has been addressed using frameworks such as matched field processing (MFP), maximum likelihood methods, or migration techniques in, e.g., acoustics [1,2,3], seismology [4,5,6], and electromagnetics [7,8]. The eigen-structure of the array covariance matrix or its inverse plays an important role in these approaches, in particular for data-adaptive implementations using, e.g., MVDR or MUSIC.…”

Section: Introductionmentioning

confidence: 99%

Graph clustering for localization within a sensor array

Riahi

Gerstoft

Mecklenbräuker

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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We develop a model-free technique to identify weak sources within dense sensor arrays using graph clustering. No knowledge about the propagation medium is needed except that signal strengths decay to insignificant levels within a scale that is shorter than the aperture. We then reinterpret the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with sensors as vertices. In a dense network, well-separated sources induce clusters in this graph. The support of the covariance matrix is estimated from limited-time data using a hypothesis test with a robust phase-only coherence test statistic combined with a physical distance criterion. The method is applied to a dense 5200 element geophone array that blanketed 7 km ⇥10 km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array.

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Electromagnetic matched-field processing: basic concepts and tropospheric simulations

Cited by 44 publications

References 28 publications

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

Evaporation Duct Height Estimation and Source Localization From Field Measurements at an Array of Radio Receivers

Graph clustering for localization within a sensor array

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