scholarly journals Fractal Dimension Based on Morphological Covering for Ground Target Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Kai Du ◽  
Xiang Fang ◽  
Wei-ping Zhang ◽  
Kai Ding
Keyword(s):  
Fractal Dimension ◽  
Seismic Waves ◽  
Vehicle Classification ◽  
Test Field ◽  
Target Classification ◽  
Operation Conditions ◽  
Wheeled Vehicle ◽  
Ground Target ◽  
Nonlinear Features ◽  
Mc Method

Seismic waves are widely used in ground target classification due to its inherent characteristics. However, they are often affected by extraneous factors and have been found to demonstrate a complicated nonlinear characteristic. The traditional signal analysis methods cannot effectively extract the nonlinear features. Motivated by this fact, this paper applies the fractal dimension (FD) based on morphological covering (MC) method to extract features of the seismic signals for ground targets classification. With the data measured from test field, three different schemes based on MC method are employed to classify tracked vehicle and wheeled vehicle in different operation conditions. Experiment results demonstrate that the three proposed methods achieve more than 90% accuracy for vehicle classification.

A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

2012 ◽  
Vol 19 (10) ◽  
pp. 639-642 ◽  
Author(s):  
Qianwei Zhou ◽  
Guanjun Tong ◽  
Dongfeng Xie ◽  
Baoqing Li ◽  
Xiaobing Yuan
Keyword(s):  
Feature Extraction ◽  
Target Classification ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Ground Target

scholarly journals Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5228
Author(s):  
Jin-Cheol Kim ◽  
Hwi-Gu Jeong ◽  
Seongwook Lee
Keyword(s):  
Millimeter Wave ◽  
Radar System ◽  
Sensor Data ◽  
Test Field ◽  
Target Classification ◽  
Radar Sensor ◽  
Millimeter Wave Radar ◽  
Direction Estimation ◽  
Wave Radar ◽  
Moving Direction

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

Automatic ground target classification using forward scattering radar

2006 ◽  
Vol 153 (5) ◽  
pp. 427 ◽  
Author(s):  
M. Cherniakov ◽  
R.S.A.R. Abdullah ◽  
P. Jančovič ◽  
M. Salous ◽  
V. Chapursky
Keyword(s):  
Forward Scattering ◽  
Target Classification ◽  
Ground Target

A New Air and Ground Target Classification Method Based on Doppler Information

2021 ◽  
Author(s):  
Wei Han ◽  
Yan Gao ◽  
Shengxiang Zhou ◽  
WeiJian Liu ◽  
Pei Zhu ◽  
...  
Keyword(s):  
Classification Method ◽  
Target Classification ◽  
Ground Target

scholarly journals An Agent-Ensemble for Thresholded Multi-Target Classification

2020 ◽  
Vol 10 (4) ◽  
pp. 1376
Author(s):  
Nathan H. Parrish ◽  
Ashley J. Llorens ◽  
Alex E. Driskell
Keyword(s):  
Binary Classification ◽  
Target Type ◽  
Vehicle Classification ◽  
Weighted Likelihood ◽  
Target Classification ◽  
Classification Problems ◽  
Target Class ◽  
Combination Strategy ◽  
Individual Agent ◽  
Ensemble Approach

We propose an ensemble approach for multi-target binary classification, where the target class breaks down into a disparate set of pre-defined target-types. The system goal is to maximize the probability of alerting on targets from any type while excluding background clutter. The agent-classifiers that make up the ensemble are binary classifiers trained to classify between one of the target-types vs. clutter. The agent ensemble approach offers several benefits for multi-target classification including straightforward in-situ tuning of the ensemble to drift in the target population and the ability to give an indication to a human operator of which target-type causes an alert. We propose a combination strategy that sums weighted likelihood ratios of the individual agent-classifiers, where the likelihood ratio is between the target-type for the agent vs. clutter. We show that this combination strategy is optimal under a conditionally non-discriminative assumption. We compare this combiner to the common strategy of selecting the maximum of the normalized agent-scores as the combiner score. We show experimentally that the proposed combiner gives excellent performance on the multi-target binary classification problems of pin-less verification of human faces and vehicle classification using acoustic signatures.

Toward a robust 3D-model-based ground target classification system for airborne platforms

2010 ◽  
Author(s):  
Wolfgang Ensinger ◽  
Christoph Stahl ◽  
Peter Knappe ◽  
Klaus Schertler ◽  
Jörg Liebelt
Keyword(s):  
Classification System ◽  
3D Model ◽  
Target Classification ◽  
Model Based ◽  
Ground Target

Non-Traditional Controllers: Composite ABS/TCS for Wheeled Vehicle

2013 ◽  
Vol 711 ◽  
pp. 491-494
Author(s):  
Ching Kuo Wang ◽  
Chang Hsin Chang
Keyword(s):  
Tracking Control ◽  
Fuzzy Controller ◽  
Viscous Friction ◽  
Lumped Mass ◽  
Braking System ◽  
Wheeled Vehicle ◽  
Nonlinear Features ◽  
Braking Distance ◽  
Rigid Body Motions ◽  
Wheeled Vehicles

Modern vehicle dynamics in its broadest sense encompasses all forms of vehicles. It aims to improve the riding comfort and the maneuverability for high-quality automobiles. This paper develops a sensor-based fuzzy controller (SFC) with a composite anti-lock braking system and tracking control system (ABS/TCS) to navigate escaping motions of wheeled vehicles under the assumption of Coulombs viscous friction and lumped-mass/rigid-body motions. The so-called escaping dynamics of wheeled vehicles occurs when the vehicle escapes from the constrained space during braking or cornering. Traditionally, such slippage phenomenon is usually ignored because of its high frequency and strong nonlinear features. The proposed SFC is designed to shorten braking distance under emergent circumstances and minimize cornering radius to improve maneuverability for wheeled vehicles. Finally, detailed simulations of wheeled vehicles with a composite ABS/TCS under the assumption of Coulombs viscous friction are used to justify the SFC algorithm.

scholarly journals Ground Target Classification Algorithm based on Multi-Sensor Images

2012 ◽  
Vol 15 (2) ◽  
pp. 195-203
Author(s):  
Eun-Young Lee ◽  
Eun-Hye Gu ◽  
Hee-Yul Lee ◽  
Woong-Ho Cho ◽  
Kil-Houm Park
Keyword(s):  
Classification Algorithm ◽  
Target Classification ◽  
Ground Target

Recognition of Working Condition in Cement Rotary Kiln Based on Generalized Dimensions

2015 ◽  
Vol 740 ◽  
pp. 676-679
Author(s):  
Hui Zhao ◽  
Jian Liu ◽  
Hong Jun Wang
Keyword(s):  
Fractal Dimension ◽  
Rotary Kiln ◽  
Working Condition ◽  
Characteristic Parameter ◽  
Current Signal ◽  
Maximum Correlation ◽  
Operation Conditions ◽  
Fractal Characteristics ◽  
Generalized Dimensions ◽  
Tight Correlation

There has been a tight correlation between the change of the cement rotary kiln carrent and operation conditions, such as the kiln thermal condition, the thickness of the crust, the kiln tyre collapse and so on. Considering the different fractal characteristics that cement rotary kiln current signal exhibit in different conditions, this paper has study on the generalized fractal dimension of kiln current signal under different working conditions as the characteristic parameter. The levels and trends of rotary kiln current and working condition will be identified by computing the maximum correlation coefficient according to the generalized fractal dimension of detected signal and of cement rotary kiln current under different working condition.

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