Browsing by Subject "Target localization"

Now showing 1 - 8 of 8

Open Access
A comparative analysis of different approaches to target differentiation and localization using infrared sensors
(2006) Aytaç, Tayfun
This study compares the performances of various techniques for the differentiation and localization of commonly encountered features in indoor environments, such as planes, corners, edges, and cylinders, possibly with different surface properties, using simple infrared sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and differentiation process. The techniques considered include rule-based, template-based, and neural network-based target differentiation, parametric surface differentiation, and statistical pattern recognition techniques such as parametric density estimation, various linear and quadratic classifiers, mixture of normals, kernel estimator, k-nearest neighbor, artificial neural network, and support vector machine classi- fiers. The geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. For a set of six surfaces, we get a correct differentiation rate of 100% in parametric differentiation based on reflection modeling. The results demonstrate that simple infrared sensors, when coupled with appropriate processing, can be used to extract substantially more information than such devices are commonly employed for. The demonstrated system would find application in intelligent autonomous systems such as mobile robots whose task involves surveying an unknown environment made of different geometry and surface types. Industrial applications where different materials/surfaces must be identified and separated may also benefit from this approach.
Open Access
Comparative analysis of different approaches to target differentiation and localization with sonar
(Elsevier, 2003) Barshan, B.; Ayrulu, B.
This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-2ight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained usingpre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Open Access
Fuzzy clustering and enumeration of target type based on sonar returns
(Elsevier, 2004) Barshan, B.; Ayrulu, B.
The fuzzy c-means (FCM) clustering algorithm is used in conjunction with a cluster validity criterion, to determine the number of different types of targets in a given environment, based on their sonar signatures. The class of each target and its location are also determined. The method is experimentally verified using real sonar returns from targets in indoor environments. A correct differentiation rate of 98% is achieved with average absolute valued localization errors of 0.5 cm and 0.8° in range and azimuth, respectively.
Open Access
Neural network-based target differentiation using sonar for robotics applications
(IEEE, 2000-08) Barshan, B.; Ayrulu, B.; Utete, S. W.
This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. The neural network can differentiate more targets with higher accuracy, improving on previously reported methods. It achieves this by exploiting the identifying features in the differential amplitude and time-of-flight (TOF) characteristics of these targets. Robustness tests indicate that the amplitude information is more crucial than TOF for reliable operation. The study suggests wider use of neural networks and amplitude information in sonar-based mobile robotics.
Open Access
Neural networks for improved target differentiation and localization with sonar
(Pergamon Press, 2001) Ayrulu, B.; Barshan, B.
This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-flight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61-90%) employing multiple sensor nodes. A sensor node is a pair of transducers with fixed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. Copyright © 2001 Elsevier Science Ltd.
Open Access
A performance analysis of two linear array processing algorithms for obstacle localization
(SPIE, 1995) Arıkan, Orhan; Barshan, Billur
The performance of a commonly employed linear array of sonar sensors is assessed for point- target localization. Two different methods of combining time-of-flight information from the sensors are described to estimate the range and azimuth of the target: pairwise estimate method and the maximum likelihood estimator. The biases and variances of the methods are investigated and their combined effect is compared to the Cramer-Rao Lower Bound. Simulation studies indicate that in estimating range, both methods perform comparably; in estimating azimuth, maximum likelihood estimate is superior at a cost of extra computation.
Open Access
Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors
(SPIE, 2004) Aytaç, T.; Barshan, B.
We investigate the use of low-cost infrared (IR) sensors for the simultaneous extraction of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We propose the use of angular intensity scans and present an algorithm to process them to determine the geometry and the surface type of the target and estimate its position. The method is verified experimentally with planes, 90-deg corners, and 90-deg edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1 deg, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99 and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to extract substantially more information than that for which such devices are commonly employed. © 2004 Society of Photo-Optical Instrumentation Engineers.
Open Access
Transform pre-processing for neural networks for object recognition and localization with sonar
(SPIE, 2003) Barshan, Billur; Ayrulu, Birsel
We investigate the pre-processing of sonar signals prior to using neural networks for robust differentiation of commonly encountered features in indoor environments. Amplitude and time-of-flight measurement patterns acquired from a real sonar system are pre-processed using various techniques including wavelet transforms, Fourier and fractional Fourier transforms, and Kohonen's self-organizing feature map. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. Neural networks can differentiate more targets, employing only a single sensor node, with a higher correct differentiation percentage than achieved with previously reported methods employing multiple sensor nodes. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate a considerable number of target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems.