Adaptive Design for Estimation of Mixing Heights From Sodar Based Measurements

2014

Remote Sensing Letters

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Remote sensing techniques are applied to sodar data for providing information regarding the planetary boundary layer. The temperature inhomogeneity that results from convective mixing in lower atmosphere is responsible for the backscattering of sound wave transmitted by the sodar. The intensity of echo returns fluctuates with time in the mixing height zone due to convection. In this work, fractal dimension (FD) of the data is considered as a measure for classifying the mixing height zone. A method has been proposed to tackle the noisy sodar data without losing relevant meteorological information. A comparison of the computed FD for noise-prone sodar data sets of tropical urban settings with those of relatively noise-free Antarctic settings is reported in this context.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring fractal features in the mixing height zone of planetary boundary layer from observed sodar data

Roy¹,

2014

Remote Sensing Letters

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“…The time evolution of mixing height has been modeled and Kalman filter (KF) has been designed to estimate the PBL from extracted data [10]. Filter has been designed to adapt to relevant system model [11].…”

Section: Previous Workmentioning

confidence: 99%

Exploring the Dynamics of Capped Inversion From Sodar Data

Roy

2012

Fluct. Noise Lett.

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The time evolution of planetary boundary layer can be studied from sodar data. Normally most of the sound wave transmitted above gets backscattered from a single inversion layer. However, during turbulent atmospheric conditions, there are multiple layers from where the signal gets backscattered with varying echo strengths. The present work describes a methodology for computer analysis of multilayered structures. Two distinct backscatter heights are extracted from observed echograms. This noisy measurement is filtered to estimate the layer heights using an a priori system model. The process and measurement noise covariances used by the filter algorithm are tuned from physical considerations to get good filter performance. Data from sodar installed at Maitree station of Antarctica with capping over an inversion layer is used to prove the concept.

Utilizing Time Redundancy for Particle Filter-Based Transfer Alignment

Chattaraj

2016

Fluct. Noise Lett.

Signal detection in the presence of high noise is a challenge in natural sciences. From understanding signals emanating out of deep space probes to signals in protein interactions for systems biology, domain specific innovations are needed. The present work is in the domain of transfer alignment (TA), which deals with estimation of the misalignment of deliverable daughter munitions with respect to that of the delivering mother platform. In this domain, the design of noise filtering scheme has to consider a time varying and nonlinear system dynamics at play. The accuracy of conventional particle filter formulation suffers due to deviations from modeled system dynamics. An evolutionary particle filter can overcome this problem by evolving multiple system models through few support points per particle. However, this variant has even higher time complexity for real-time execution. As a result, measurement update gets deferred and the estimation accuracy is compromised. By running these filter algorithms on multiple processors, the execution time can be reduced, to allow frequent measurement updates. Such scheme ensures better system identification so that performance improves in case of simultaneous ejection of multiple daughters and also results in better convergence of TA algorithms for single daughter.