A Merging Strategy for Gaussian Process Extended Target Estimates in Multi-Sensor Applications

Author(s):  
Martin Michaelis ◽  
Philipp Berthold ◽  
Thorsten Luettel ◽  
Daniel Meissner ◽  
Hans-Joachim Wuensche
Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1704
Author(s):  
Haoyang Yu ◽  
Wei An ◽  
Ran Zhu

A problem of tracking surface shape-shifting extended target by using gray scale pixels on optical image is considered. The measurement with amplitude information (AI) is available to the proposed method. The target is regarded as a convex hemispheric object, and the amplitude distribution of the extended target is represented by a solid radial function. The Gaussian process (GP) is applied and the covariance function of GP is modified to fit the convex hemispheric shape. The points to be estimated on the target surface are selected reasonably in the hemispheric space at the azimuth and pitch directions. Analytical representation of the estimated target extent is provided and the recursive process is implemented by the extended Kalman filter (EKF). In order to demonstrate the algorithm’s ability of tracking complex shaped targets, a trailing target characterized by two feature parameters is simulated and the two feature parameters are extracted with the estimated points. The simulations verify the validity of the proposed method with compared to classical algorithms.


2018 ◽  
Vol 176 ◽  
pp. 01017
Author(s):  
Luo-jia Chi ◽  
Xin-xi Feng ◽  
Lu Miao

For the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed. The algorithm adopts the star convex to model the extended target, and realizes the online learning of the Gaussian process by constructing the state space model to complete the estimation of the extended target shape. At the same time, in the low SNR environment, the target motion state is tracked by the good tracking performance of the generalized label Bernoulli filter. Simulation results show that for any target with unknown shape, the proposed algorithm can well offer its extended shape and in the low SNR environment it can greatly improve the accuracy and stability of target tracking.


2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.


1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.


2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi

2000 ◽  
Vol 628 ◽  
Author(s):  
Mark A. Clarner ◽  
Michael J. Lochhead

ABSTRACTOrganically modified silica gels and dye-doped silica gels have been patterned into micrometer-scale structures on a substrate using micro molding in capillaries (MIMIC). This approach is from a class of elastomeric stamping and molding techniques collectively known as soft lithography. Soft lithography and sol-gel processing share attractive features in that they are relatively benign processes performed at ambient conditions, which makes both techniques compatible with a wide variety of organic molecules, molecular assemblies, and biomolecules. The combination of sol-gel and soft lithography, therefore, holds enormous promise as a tool for microfabrication of materials with optical, chemical, or biological functionality that are not readily patterned with conventional methods. This paper describes our investigation of micro-patterned organic-inorganic hybrid materials containing indicator dyes for microfluidic sensor applications. Reversible colorimetric pH sensing via entrapped reagents is demonstrated in a prototype microfluidic sensor element. Patterned structures range from one to tens of micrometers in cross-section and are up to centimeters in length. Fundamental chemical processing issues associated with mold filling, cracking and sensor stability are discussed.


Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra

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