An Interacting Multiple-Model-Based Abrupt Change Detector for Ground-Penetrating Radar

2007 ◽  
Vol 4 (4) ◽  
pp. 634-638 ◽  
Author(s):  
Vijayaraghavan Venkatasubramanian ◽  
Henry Leung ◽  
Brian Moorman
2018 ◽  
Vol 66 ◽  
pp. 167-179
Author(s):  
Priscila E. Souza ◽  
Aart Kroon ◽  
Lars Nielsen

Detailed topographic data and high-resolution ground-penetrating radar (GPR) reflection data are presented from the present-day beach and across successive raised beach ridges at Itilleq, south-west Disko, West Greenland. In the western part of the study area, the present low-tide level is well defined by an abrupt change in sediment grain size between the sandy foreshore and the upper shoreface that is characterised by frequently occurring large clasts. The main parts of both fine and large clasts appear to be locally derived. Seaward-dipping reflections form downlap points, which are clearly identified in all beach-ridge GPR profiles. Most of them are located at the boundary between a unit with reflection characteristics representing palaeo-foreshore deposits and a deeper and more complex radar unit characterised by diffractions; the deeper unit is not penetrated to large depths by the GPR signals. Based on observations of the active shoreface regime, large clasts are interpreted to give rise to scattering observed near the top of the deeper radar unit. We regard the downlap points located at this radar boundary as markers of palaeo-low-tide levels. In some places, scattering hyperbolas are more pronounced and frequent than in others, suggesting differences in the occurrence of large boulders.


2019 ◽  
Vol 20 (12) ◽  
pp. 4308-4317 ◽  
Author(s):  
Sanghyun Hong ◽  
Jianbo Lu ◽  
Smruti R. Panigrahi ◽  
Jonathan Scott ◽  
Dimitar P. Filev

2013 ◽  
Vol 66 (6) ◽  
pp. 859-877 ◽  
Author(s):  
M. Malleswaran ◽  
V. Vaidehi ◽  
S. Irwin ◽  
B. Robin

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.


2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Xia Liu ◽  
Fei Long ◽  
Wenjie Zhang ◽  
Lu Guo

A new maneuvering target tracking algorithm is investigated, which is modeled as a class of Markov jump linear systems (MJLS). Drawing on the experience of combination idea of the extended Viterbi algorithm (EV) and the interacting multiple model algorithm (IMM), a modular interacting multiple model based on extended Viterbi (MIMMEV) is presented. The MIMMEV algorithm consists ofNindependent interacting multiple model-extended Viterbi (IMM-EV). Furthermore, these IMM-EV filters are independent and working in parallel in the MIMMEV algorithm. According to the derived probability, the estimated state of every moment is the weighted sum of each estimator at the corresponding time. Simulation results demonstrate that the proposed algorithm improves the tracking precision and reduces the computational burden compared with traditional IMM and IMM-EV.


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