Browsing by Subject "Sonar"

Now showing 1 - 20 of 23

Open Access
Comparative analysis of different approaches to target classification and localization with sonar
(IEEE, 2001-08) Ayrulu, Birsel; Barshan, Billur
The comparison of different classification and fusion techniques was done for target classification and localization with sonar. Target localization performance of artificial neural networks (ANN) was found to be better than the target differentiation algorithm (TDA) and fusion techniques. The target classification performance of non-parametric approaches was better than that of parameterized density estimator (PDE) using homoscedastic and heteroscedastic NM for statistical pattern recognition techniques.
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
A comparative study of map building techniques by processing sonar arc-maps
(2005) Kurt, Arda; Barshan, Billur
In this study, four signal processing schemes regarding sonar sensor based map-building applications were compared. The newly proposed method, Directional Maximum is found to be successful in terms of reducing the innate angular ambiguity of the sonar sensors. With respect to several works presented earlier in the same field and specifically map-building related studies, the new method is successful both in terms of mean absolute error and computational cost.
Open Access
A comparison of two methods for fusing information from a linear array of sonar sensors for obstacle localization
(IEEE, 1995) Arıkan, Orhan; Barshan, Billur
The performance of a commonly employed linear array of sonar sensors is assessed for point-obstacle localization intended for robotics applications. Two different methods of combining time-of-flight information from the sensors are described to estimate the range and azimuth of the obstacle: pairwise estimate method and the maximum likelihood estimator. The variances of the methods are compared to the Cramer-Rao Lower Bound, and their biases are investigated. 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. The results are useful for target localization in mobile robotics.
Open Access
Comparison of two methods of surface profile extraction from multiple ultrasonic range measurements
(Institute of Physics Publishing, 2000) Barshan, B.; Backent, D.
Two novel methods for surface profile extraction based on multiple ultrasonic range measurements are described and compared. One of the methods employs morphological processing techniques, whereas the other employs a spatial voting scheme followed by simple thresholding. Morphological processing exploits neighbouring relationships between the pixels of the generated arc map. On the other hand, spatial voting relies on the number of votes accumulated in each pixel and ignores neighbouring relationships. Both approaches are extremely flexible and robust, in addition to being simple and straightforward. They can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The methods have the intrinsic ability to suppress spurious readings, crosstalk and higher-order reflections, and process multiple reflections informatively. The performances of the two methods are compared on various examples involving both simulated and experimental data. The morphological processing method outperforms the spatial voting method in most cases with errors reduced by up to 80%. The effect of varying the measurement noise and surface roughness is also considered. Morphological processing is observed to be superior to spatial voting under these conditions as well.
Open Access
Estimation of object location and radius of curvature using ultrasonic sonar
(Elsevier, 2001-07) Sekmen, A. Ş.; Barshan, B.
Acoustic sensors are very popular in time-of-flight (TOF) ranging systems since they are inexpensive and convenient to use. One of the major limitations of these sensors is their low angular resolution which makes object localization difficult. In this paper, an adaptive multisensor configuration consisting of three transmitter/receiver ultrasonic transducers is introduced to compensate for the low angular resolution of sonar sensors and improve the localization accuracy. With this configuration, the radius of curvature and location of cylindrical objects are estimated. Two methods of TOF estimation are considered: thresholding and curve-fitting. The bias-variance combinations of these estimators are compared. Theory and simulations are verified by experimental data from a real sonar system. Extended Kalman filtering is used to smooth the data. It is shown that curve-fitting method, compared to thresholding method, provides about 30% improvement in the absence of noise and 50% improvement in the presence of noise. Moreover. the adaptive configuration improves the estimation accuracy by 35-40%. (C) 2001 Elsevier Science Ltd. All rights reserved.
Open Access
Evidential logical sensing using multiple sonars for the identification of target primitives in a mobile robot's environment
(IEEE, 1996) Ayrulu, Birsel; Barshan, Billur; Erkmen, İ.; Erkmen, A.
Physical models are used to model reflections from target primitives commonly encountered in mobile robot applications. These targets are differentiated by employing a multi-transducer pulse/echo system which relies on both amplitude and time-of-flight (TOF) data in the feature fusion process, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused for multiple logical sonars at different geographical sites. This evidential approach helps to overcome the vulnerability of echo amplitude to noise and enables the modeling of non-parametric uncertainty. Feature data from multiple logical sensors are fused with Dempster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Using three sensing nodes, improvement in differentiation is between 20-40% without false decision, at the cost of additional computation. Simulation results are verified by experiments with a real sonar system. This evidential approach helps to overcome the vulnerability of the echo amplitude to noise and enables the modeling of non-parametric uncertainty in real time.
Open Access
Fast processing techniques for accurate ultrasonic range measurements
(Institute of Physics Publishing, 2000) Barshan, B.
Four methods of range measurement for airborne ultrasonic systems - namely simple thresholding, curve-fitting, sliding-window, and correlation detection - are compared on the basis of bias error, standard deviation, total error, robustness to noise, and the difficulty/complexity of implementation. Whereas correlation detection is theoretically optimal, the other three methods can offer acceptable performance at much lower cost. Performances of all methods have been investigated as a function of target range, azimuth, and signal-to-noise ratio. Curve fitting, sliding window, and thresholding follow correlation detection in the order of decreasing complexity. Apart from correlation detection, minimum bias and total error is most consistently obtained with the curve-fitting method. On the other hand, the sliding-window method is always better than the thresholding and curve-fitting methods in terms of minimizing the standard deviation. The experimental results are in close agreement with the corresponding simulation results. Overall, the three simple and fast processing methods provide a variety of attractive compromises between measurement accuracy and system complexity. Although this paper concentrates on ultrasonic range measurement in air, the techniques described may also find application in underwater acoustics.
Open Access
Fractional fourier transform pre-processing for neural networks and its application to object recognition
(Elsevier, 2002-01) Barshan, Billur; Ayrulu, Birsel
This study investigates fractional Fourier transform pre-processing of input signals to neural networks. The fractional Fourier transform is a generalization of the ordinary Fourier transform with an order parameter a. Judicious choice of this parameter can lead to overall improvement of the neural network performance. As an illustrative example, we consider recognition and position estimation of different types of objects based on their sonar returns. Raw amplitude and time-of-flight patterns acquired from a real sonar system are processed, demonstrating reduced error in both recognition and position estimation of objects. (C) 2002 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
Identification of target primitives with multiple decision-making sonars using evidential reasoning
(Sage Publications Ltd., 1998-06) Ayrulu, B.; Barshan, B.
In this study, physical models are used to model reflections from target primitives commonly encountered in a mobile robot's environment. These targets are differentiated by employing a multitransducer pulse/echo system that relies on both time-of-flight data and amplitude in the feature-fusion process, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief, which are subsequently fused by employing multiple logical sonars at geographically distinct sites. Feature data from multiple logical sensors are fused with Dempster's rule of combination to improve the performance of classification by reducing perception uncertainty. Using three sensing nodes, improvement in differentiation is between 10% and 35% without false decision, at the cost of additional computation. The method is verified by experiments with a real sonar system. The evidential approach employed here helps to overcome the vulnerability of the echo amplitude to noise, and enables the modeling of nonparametric uncertainty in real time.
Open Access
Location and curvature estimation of "spherical" targets using a flexible sonar configuration
(IEEE, 1996) Barshan, Billur
A novel, flexible, three-dimensional (3-D) multi-sensor sonar system is employed to localize the center of a spherical target and estimate its radius of curvature. The interesting limiting cases for the problem under study are the point and planar targets, both of which are important for the characterization of a mobile robot's environment. A noise model is developed based on real sonar data. An extended Kalman filter (EKF) which incorporates the developed noise model is employed as an estimation tool for optimal processing of the sensor data. Simulations and experimental results are provided for specularly reflecting cylindrical targets.
Open Access
Location and curvature estimation of spherical targets using multiple sonar time-of-flight measurements
(Institute of Electrical and Electronics Engineers, 1999-12) Barshan, B.
A novel, flexible, three-dimensional multisensor sonar system is described to localize the center of a generalized spherical target and estimate its radius of curvature. Point, line, and planar targets are included as limiting cases which are important for the characterization of a mobile robot's environment. Sensitivity analysis of the curvature estimate with respect to measurement errors and some of the system parameters is provided. The analysis is verified experimentally for specularly reflecting cylindrical and planar targets. Typical accuracies in range and azimuth are 0.17 mm and 0.1°, respectively. Accuracy of the curvature estimate depends on the target type and system parameters such as transducer separation and operating range.
Open Access
Morphological surface profile extraction from multiple sonars
(IEEE, 1998) Başkent, Deniz; Barshan, Billur
This paper presents a novel method for surface profile determination using multiple sensors. Our approach is based on morphological processing techniques to fuse the range data from multiple sensor returns in a manner that directly reveals the target surface profile. The method has the intrinsic ability of suppressing spurious readings due to noise, crosstalk, and higher-order reflections, as well as processing multiple reflections informatively. The algorithm is verified both by simulations and experiments in the laboratory by processing real sonar data obtained from a mobile robot. The results are compared to those obtained from a more accurate structured-light system, which is however more complex and expensive.
Open Access
Performance comparison of four time-of-flight estimation methods for sonar signals
(1998-08-06) Barshan, B.; Ayrulu, B.
Performances of four methods of time-of-flight estimation for sonar signals are compared in terms of their bias, standard deviation and complexity: thresholding, curve fitting, m-out-of-N sliding-window, and correlation detection. Whereas correlation detection represents the theoretical optimum, simpler and faster suboptimal methods can offer acceptable performance at much lower cost. The experimental results are in close agreement with the simulations.
Open Access
Radius of curvature estimation and localization of targets using multiple sonar sensors
(A I P Publishing LLC, 1999-04) Barshan, B.; Sekmen, A. S.
Acoustic sensors have been widely used in time-of-flight ranging systems since they are inexpensive and convenient to use. One of the most important limitations of these sensors is their low angular resolution. To improve the angular resolution and the accuracy, a novel, flexible, and adaptive three- dimensional (3-D) multi-sensor sonar system is described for estimating the radius of curvature and location of cylindrical and spherical targets. Point, line, and planar targets are included as limiting cases which are important for the characterization of typical environments. Sensitivity analysis of the curvature estimate with respect to measurement errors and certain system parameters is provided. The analysis and the simulations are verified by experiments in 2-D with specularly reflecting cylindrical and planar targets, using a real sonar system. Typical accuracies in range and azimuth are 0.18 mm and 0.1°, respectively. Accuracy of the curvature estimation depends on the target type and system parameters such as transducer separation and operating range. The adaptive configuration brings an improvement varying between 35% and 45% in the accuracy of the curvature estimate. The presented results are useful for target differentiation and tracking applications.A flexible and adaptive three-dimensional multisensor sonar system capable of estimating the location and radius of curvature of spherical and cylindrical targets is presented. The performance radius of curvature estimation is analyzed to provide information for differentiating reflectors with different radii. Results showed that the adaptive configuration improved the accuracy of the curvature estimate between 35% and 45%.
Open Access
Recognition of vessel acoustic signatures using non-linear teager energy based features
(IEEE, 2016-10) Can, Gökmen; Akbaş, Cem Emre; Çetin, A. Enis
This paper proposes a vessel recognition and classification system based on vessel acoustic signatures. Teager Energy Operator (TEO) based Mel Frequency Cepstral Coefficients (MFCC) are used for the first time in Underwater Acoustic Signal Recognition (UASR) to identify platforms the acoustic noise they generate. TEO based MFCC (TEO-MFCC), being more robust in noisy conditions than conventional MFCC, provides a better estimation platform energy. Conventionally, acoustic noise is recognized by sonar oper-ators who listen to audio signals received by ship sonars. The aim of this work is to replace this conventional human-based recognition system with a TEO-MFCC features-based classification system. TEO is applied to short-time Fourier transform (STFT) of acoustic signal frames and Mel-scale filter bank is used to obtain Mel Teager-energy spectrum. The feature vector is constructed by discrete cosine transform (DCT) of logarithmic Mel Teager-energy spectrum. Obtained spectrum is transformed into cepstral coefficients that are labeled as TEO-MFCC. This analysis and implementation are carried out with datasets of 24 different noise recordings that belong to 10 separate classes of vessels. These datasets are partially provided by National Park Service (NPS). Artificial Neural Networks (ANN) are used as a classification method. Experimental results demonstrate that TEO-MFCC achieves 99.5% accuracy in classification of vessel noises. © 2016 IEEE.
Open Access
Reliability measure assignment to sonar for robust target differentiation
(Elsevier, 2002) Ayrulu, B.; Barshan, B.
This article addresses the use of evidential reasoning and majority voting in multi-sensor decision making for target differentiation using sonar sensors. Classification of target primitives which constitute the basic building blocks of typical surfaces in uncluttered robot environments has been considered. Multiple sonar sensors placed at geographically different sensing sites make decisions about the target type based on their measurement patterns. Their decisions are combined to reach a group decision through Dempster-Shafer evidential reasoning and majority voting. The sensing nodes view the targets at different ranges and angles so that they have different degrees of reliability. Proper accounting for these different reliabilities has the potential to improve decision making compared to simple uniform treatment of the sensors. Consistency problems arising in majority voting are addressed with a view to achieving high classification performance. This is done by introducing preference ordering among the possible target types and assigning reliability measures (which essentially serve as weights) to each decision-making node based on the target range and azimuth estimates it makes and the belief values it assigns to possible target types. The results bring substantial improvement over evidential reasoning and simple majority voting by reducing the target misclassification rate. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Open Access
Surface profile determination from multiple sonar data using morphological processing
(1998) Başkent, Deniz
In this thesis, a novel method for surface profile determination using multiple sensors is presented. Our approach is based on morphological processing techniques to fuse the range data from multiple sensor returns in a manner that directly reveals the target surface profile. The method has the intrinsic ability of suppressing spurious readings due to noise, crosstalk, and higher-order reflections, as well as processing multiple reflections informatively. The approach taken is extremely flexible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The profil of any continuous surface with varying curvature can be extracted as long as the curvature of the surface is not too high. The average processing time of the method is of the order of several seconds indicating that the method is viable for real-time applications. The algorithm is verified both by simulations and experiments in the laboratory by processing real sonar data obtained from the Nomad 200 mobile robot. The results are compared to those obtained from a more accurate structured-light system, which is however more complex and expensive.
Open Access
Surface profile determination from multiple sonar data using morphological processing
(Sage Publications Ltd., 1999-08) Başkent, D.; Barshan, B.
This paper presents a novel method for surface profile determination using multiple sensors. Our approach is based on morphological processing techniques to fuse the range data from multiple sensor returns in a manner that directly reveals the target surface profile. The method has the intrinsic ability of suppressing spurious readings due to noise, crosstalk, and higher-order reflections, as well as processing multiple reflections informatively. The approach taken is extremely flexible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The algorithm is verified both by simulating and experiments in the laboratory by processing real sonar data obtained from a mobile robot. The results are compared to those obtained from a more accurate structured-light system, which is, however, more complex and expensive.