Localization and Tracking of Objects Using Cross-Correlation of Shadow Fading Noise

2014 ◽  
Vol 13 (10) ◽  
pp. 2293-2305 ◽  
Author(s):  
M. R. Basheer ◽  
S. Jagannathan
Keyword(s):  
Cross Correlation ◽  
Shadow Fading ◽  
Localization And Tracking

scholarly journals Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5437
Author(s):  
Zhengwang Tian ◽  
Weifeng Liu ◽  
Xinfeng Ru
Keyword(s):  
Cross Correlation ◽  
Measurement Data ◽  
Angle Of Arrival ◽  
Simulation Experiments ◽  
Acoustic Localization ◽  
Cluttered Environment ◽  
Detection And Tracking ◽  
Correlation Algorithm ◽  
Acoustic Array ◽  
Localization And Tracking

This paper deals with mobile multi-target detection and tracking. In the traditional method, there are uncertainties such as misdetection and false alarm in the measurement data, and it will be inevitable having to deal with the data association. To solve the target trajectory and state estimation problem under a cluttered environment, this paper proposes a non-concurrent multi-target acoustic localization tracking method based on the Gibbs-generalized labelled multi-Bernoulli (Gibbs-GLMB) filter and considers an acoustic array of a fixed arrangement for the tracking of targets by joint time difference of arrival (TDOA) and angle of arrival (AOA) measurements. Firstly, the TDOAs are calculated by using the generalized cross-correlation algorithm (GCC) and the AOAs are derived from the received signal directions. Secondly, we assume the independence of the targets and fuse the measurements which are used to track the multiple targets via the Gibbs-GLMB filter. Finally, the effectiveness of the method is verified by Monte Carlo simulation experiments.

Artefact in 20Å-Resolution Electron Images of Negatively Stained Twodimensional Membrane Protein Crystals

1982 ◽  
Vol 40 ◽  
pp. 92-93
Author(s):  
Douglas L. Dorset ◽  
Barbara Moss
Keyword(s):  
Electron Scattering ◽  
Cross Correlation ◽  
Transmission Function ◽  
Group Symmetry ◽  
Asymmetric Structure ◽  
Object Model ◽  
Phase Object ◽  
Computing Systems ◽  
Resolution Electron ◽  
Plane Group

A number of computing systems devoted to the averaging of electron images of two-dimensional macromolecular crystalline arrays have facilitated the visualization of negatively-stained biological structures. Either by simulation of optical filtering techniques or, in more refined treatments, by cross-correlation averaging, an idealized representation of the repeating asymmetric structure unit is constructed, eliminating image distortions due to radiation damage, stain irregularities and, in the latter approach, imperfections and distortions in the unit cell repeat. In these analyses it is generally assumed that the electron scattering from the thin negativelystained object is well-approximated by a phase object model. Even when absorption effects are considered (i.e. “amplitude contrast“), the expansion of the transmission function, q(x,y)=exp (iσɸ (x,y)), does not exceed the first (kinematical) term. Furthermore, in reconstruction of electron images, kinematical phases are applied to diffraction amplitudes and obey the constraints of the plane group symmetry.

Application of cross correlation to HREM images of surfaces

1987 ◽  
Vol 45 ◽  
pp. 754-755
Author(s):  
D. E. Luzzi ◽  
L. D. Marks ◽  
M. I. Buckett
Keyword(s):  
Cross Correlation ◽  
A Priori ◽  
Matrix Material ◽  
Correlation Method ◽  
Accurate Knowledge ◽  
Complex Image ◽  
Fourier Filtering ◽  
The Matrix ◽  
Low Pass ◽  
Data Reduction Technique

As the HREM becomes increasingly used for the study of dynamic localized phenomena, the development of techniques to recover the desired information from a real image is important. Often, the important features are not strongly scattering in comparison to the matrix material in addition to being masked by statistical and amorphous noise. The desired information will usually involve the accurate knowledge of the position and intensity of the contrast. In order to decipher the desired information from a complex image, cross-correlation (xcf) techniques can be utilized. Unlike other image processing methods which rely on data massaging (e.g. high/low pass filtering or Fourier filtering), the cross-correlation method is a rigorous data reduction technique with no a priori assumptions.We have examined basic cross-correlation procedures using images of discrete gaussian peaks and have developed an iterative procedure to greatly enhance the capabilities of these techniques when the contrast from the peaks overlap.

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