Change search
Link to record
Permanent link

Direct link
Alternative names
Publications (10 of 185) Show all publications
Hallösta, S., Pettersson, M. & Dahl, M. (2024). Impact of Neural Network Architecture for Fingerprint Recognition. In: Akram Bennour, Ahmed Bouridane, Lotfi Chaari (Ed.), Intelligent Systems and Pattern Recognition: Third International Conference, ISPR 2023, Hammamet, Tunisia, May 11–13, 2023, Revised Selected Papers, Part I. Paper presented at 3rd International Conference on Intelligent Systems & Pattern Recognition, ISPR2023, Hammamet, 11/5 - 13/5 2023 (pp. 3-14). Springer, 1940
Open this publication in new window or tab >>Impact of Neural Network Architecture for Fingerprint Recognition
2024 (English)In: Intelligent Systems and Pattern Recognition: Third International Conference, ISPR 2023, Hammamet, Tunisia, May 11–13, 2023, Revised Selected Papers, Part I / [ed] Akram Bennour, Ahmed Bouridane, Lotfi Chaari, Springer, 2024, Vol. 1940, p. 3-14Conference paper, Published paper (Refereed)
Abstract [en]

This work investigates the impact of the neural networks architecture when performing fingerprint recognition. Three networks are studied; a Triplet network and two Siamese networks. They are evaluated on datasets with specified amounts of relative translation between fingerprints. The results show that the Siamese model based on contrastive loss performed best in all evaluated metrics. Moreover, the results indicate that the network with a categorical scheme performed inferior to the other models, especially in recognizing images with high confidence. The Equal Error Rate (EER) of the best model ranged between 4%−11% which was on average 6.5 percentage points lower than the categorical schemed model. When increasing the translation between images, the networks were predominantly affected once the translation reached a fourth of the image. Our work concludes that architectures designed to cluster data have an advantage when designing an authentication system based on neural networks.

Place, publisher, year, edition, pages
Springer, 2024
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1940
Keywords
Fingerprint recognition, Neural network architecture, Siamese network
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Systems Engineering
Identifiers
urn:nbn:se:bth-25604 (URN)10.1007/978-3-031-46335-8_1 (DOI)2-s2.0-85177185075 (Scopus ID)978-3-031-46334-1 (ISBN)978-3-031-46335-8 (ISBN)
Conference
3rd International Conference on Intelligent Systems & Pattern Recognition, ISPR2023, Hammamet, 11/5 - 13/5 2023
Available from: 2023-11-14 Created: 2023-11-14 Last updated: 2023-12-04Bibliographically approved
Mittmann Voigt, G. H., Irion Alves, D., Müller, C., Machado, R., Ramos, L. P., Vu, V. T. & Pettersson, M. (2023). A Statistical Analysis for Intensity Wavelength-Resolution SAR Difference Images. Remote Sensing, 15(9), Article ID 2401.
Open this publication in new window or tab >>A Statistical Analysis for Intensity Wavelength-Resolution SAR Difference Images
Show others...
2023 (English)In: Remote Sensing, E-ISSN 2072-4292, Vol. 15, no 9, article id 2401Article in journal (Refereed) Published
Abstract [en]

This paper presents a statistical analysis of intensity wavelength-resolution synthetic aperture radar (SAR) difference images. In this analysis, Anderson Darling goodness-of-fit tests are performed, considering two different statistical distributions as candidates for modeling the clutter-plus-noise, i.e., the background statistics. The results show that the Gamma distribution is a good fit for the background of the tested SAR images, especially when compared with the Exponential distribution. Based on the results of this statistical analysis, a change detection application for the detection of concealed targets is presented. The adequate selection of the background distribution allows for the evaluated change detection method to achieve a better performance in terms of probability of detection and false alarm rate, even when compared with competitive performance change detection methods in the literature. For instance, in an experimental evaluation considering a data set obtained by the Coherent All Radio Band Sensing (CARABAS) II UWB SAR system, the evaluated change detection method reached a detection probability of 0.981 for a false alarm rate of 1/km2. © 2023 by the authors.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
background statistics, CARABAS-II, change detection method, SAR, UWB
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-24621 (URN)10.3390/rs15092401 (DOI)000988128500001 ()2-s2.0-85159359279 (Scopus ID)
Available from: 2023-05-26 Created: 2023-05-26 Last updated: 2023-08-28Bibliographically approved
Araujo, G. F., MacHado, R. & Pettersson, M. (2023). A Tailored cGAN SAR Synthetic Data Augmentation Method for ATR Application. In: Proceedings of the IEEE Radar Conference: . Paper presented at 2023 IEEE Radar Conference, RadarConf23, San Antonia, 1 May 2023 5 May 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023
Open this publication in new window or tab >>A Tailored cGAN SAR Synthetic Data Augmentation Method for ATR Application
2023 (English)In: Proceedings of the IEEE Radar Conference, Institute of Electrical and Electronics Engineers (IEEE), 2023, Vol. 2023Conference paper, Published paper (Refereed)
Abstract [en]

This article proposes a method to simulate Synthetic Aperture Radar (SAR) targets for specific incidence and azimuth angles. Images synthesized by Electromagnetic Computing (EMC) are used to train a Conditional Generative Adversarial Network (cGAN). Two synthetic image chips of the same class and incidence angle, separated by two degrees in azimuth, are used as input to the cGAN. The cGAN predicts the image of the same class and incidence angle whose azimuth angle corresponds to the bisector of the two input chips. An evaluation using the SAMPLE dataset was performed to verify the quality of the image prediction. Running through a total of 100 training epochs, the cGAN converges, reaching the best Mean Squared Error (MSE) after 77 epochs. The results demonstrate that the proposed method is promising for Automatic Target Recognition (ATR) applications. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Radar (RADAR), ISSN 1097-5764, E-ISSN 2640-7736
Keywords
Automatic Target Recognition, Conditional Generative Adversarial Network, Data Augmentation, Image Translation, Synthetic Aperture Radar, Generative adversarial networks, Mean square error, Radar imaging, Radar target recognition, Augmentation methods, Azimuth angles, Electromagnetics, Incidence angles, Radar target, Synthesised, Synthetic data
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-25216 (URN)10.1109/RadarConf2351548.2023.10149587 (DOI)001031599600049 ()2-s2.0-85163791888 (Scopus ID)9781665436694 (ISBN)
Conference
2023 IEEE Radar Conference, RadarConf23, San Antonia, 1 May 2023 5 May 2023
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-24Bibliographically approved
Campos, A. B., Molin, R. D., Ramos, L. P., MacHado, R., Vu, V. T. & Pettersson, M. (2023). Adaptive Target Enhancer: Bridging the Gap between Synthetic and Measured SAR Images for Automatic Target Recognition. In: Proceedings of the IEEE Radar Conference: . Paper presented at 2023 IEEE Radar Conference, RadarConf23, San Antonia, 1 May through 5 May 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023
Open this publication in new window or tab >>Adaptive Target Enhancer: Bridging the Gap between Synthetic and Measured SAR Images for Automatic Target Recognition
Show others...
2023 (English)In: Proceedings of the IEEE Radar Conference, Institute of Electrical and Electronics Engineers (IEEE), 2023, Vol. 2023Conference paper, Published paper (Refereed)
Abstract [en]

Automatic target recognition (ATR) algorithms have been successfully used for vehicle classification in synthetic aperture radar (SAR) images over the past few decades. For this application, however, the scarcity of labeled data is often a limiting factor for supervised approaches. While the advent of computer-simulated images may result in additional data for ATR, there is still a substantial gap between synthetic and measured images. In this paper, we propose the so-called adaptive target enhancer (ATE), a tool designed to automatically delimit and weight the region of an image that contains or is affected by the presence of a target. Results for the publicly released Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset show that, by defining regions of interest and suppressing the background, we can increase the classification accuracy from 68% to 84% while only using artificially generated images for training. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Radar (RADAR), ISSN 1097-5764, E-ISSN 2640-7736
Keywords
Adaptive filtering, automatic target recognition (ATR), MSTAR, SAMPLE, synthetic aperture radar (SAR), Adaptive filters, Automatic target recognition, Classification (of information), Image enhancement, Radar imaging, Radar measurement, Radar target recognition, Additional datum, Labeled data, Simulated images, Synthetic and measured paired and labeled experiment, Synthetic aperture radar, Synthetic aperture radar images, Target recognition algorithms, Vehicle classification
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-25225 (URN)10.1109/RadarConf2351548.2023.10149739 (DOI)001031599600197 ()2-s2.0-85163779747 (Scopus ID)9781665436694 (ISBN)
Conference
2023 IEEE Radar Conference, RadarConf23, San Antonia, 1 May through 5 May 2023
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-24Bibliographically approved
Batra, A., Ivanenko, Y., Vu, V. T., Wiemeler, M., Pettersson, M., Goehringer, D. & Kaiser, T. (2023). Analysis of Surface Roughness with 3D SAR Imaging at 1.5 THz. In: 2023 48TH INTERNATIONAL CONFERENCE ON INFRARED, MILLIMETER, AND TERAHERTZ WAVES, IRMMW-THZ: . Paper presented at 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), SEP 17-22, 2023, McGill Univ, Montreal, CANADA. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Analysis of Surface Roughness with 3D SAR Imaging at 1.5 THz
Show others...
2023 (English)In: 2023 48TH INTERNATIONAL CONFERENCE ON INFRARED, MILLIMETER, AND TERAHERTZ WAVES, IRMMW-THZ, Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

The expansion of the synthetic aperture radar (SAR) to the emerging THz spectrum has enabled a new era of applications in the areas of automobile, security, non-destructive testing, and material characterization. Thanks to the sub-mm wavelength, extraction of material surface properties is possible and of significant interest for the THz SAR applications. The properties define the surface scattering behavior, which is relational to the applied frequency. This study focuses on surface classification. We evaluate the scattering behavior of a rough surface and a smooth surface at 1.5 THz based on a SAR processing sequence that is introduced in this paper. First, we form the 3D SAR images of the metallic objects and then evaluate the surface properties based on the variation in the energy reflected by the object's surface.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
International Conference on Infrared Millimeter and Terahertz Waves, ISSN 2162-2027
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-25848 (URN)10.1109/IRMMW-THz57677.2023.10299181 (DOI)001098999800330 ()979-8-3503-3660-3 (ISBN)
Conference
48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), SEP 17-22, 2023, McGill Univ, Montreal, CANADA
Available from: 2024-01-02 Created: 2024-01-02 Last updated: 2024-01-03Bibliographically approved
Ivanenko, Y., Vu, V. T. & Pettersson, M. (2023). Autofocusing of THz SAR Images by Integrating Compressed Sensing into the Backprojection Process. In: Proceedings of the IEEE Radar Conference: . Paper presented at 2023 IEEE Radar Conference, RadarConf23, San Antonia, 1 May through 5 May 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023
Open this publication in new window or tab >>Autofocusing of THz SAR Images by Integrating Compressed Sensing into the Backprojection Process
2023 (English)In: Proceedings of the IEEE Radar Conference, Institute of Electrical and Electronics Engineers (IEEE), 2023, Vol. 2023Conference paper, Published paper (Refereed)
Abstract [en]

The THz frequency spectrum provides an opportunity to explore high-resolution synthetic-aperture-radar (SAR) short-range imaging that can be used for various applications. However, the performance of THz SAR imaging is sensitive to phase errors that can be caused by an insufficient amount of data samples for image formation and by path deviations that can be practically caused by SAR platform vibrations, changes in speed, changes in direction, and acceleration. To solve the former problem, an improved interpolation procedure for backprojection algorithms has been proposed. However, to make these algorithms efficient in handling the latter problem, an additional autofocusing is necessary. In this paper, we introduce an autofocusing procedure based on compressed sensing that is incorporated into the backprojection algorithm. The reconstruction is based on the following calculated parameters: windowed interpolation sinc kernel, and range distances between SAR platform and image pixels in a defined image plane. The proposed approach is tested on real data, which was acquired by the 2\pi FMCW SAR system through outdoor SAR imaging. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Radar (RADAR), ISSN 1097-5764, E-ISSN 2640-7736
Keywords
Autofocusing, Compressed Sensing, FMCW SAR, THz, Frequency modulation, Interpolation, Radar imaging, Synthetic aperture radar, Terahertz waves, Auto-focusing, Backprojection algorithms, Backprojections, Compressed-Sensing, FMCW synthetic-aperture-radar, Frequency spectra, Synthetic aperture radar images, Synthetic aperture radar imaging, THz frequencies
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-25226 (URN)10.1109/RadarConf2351548.2023.10149760 (DOI)001031599600217 ()2-s2.0-85163791710 (Scopus ID)9781665436694 (ISBN)
Conference
2023 IEEE Radar Conference, RadarConf23, San Antonia, 1 May through 5 May 2023
Projects
Multistatic High-resolution Sensing at THz, Project-ID A17
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-24Bibliographically approved
Alves, D. i., Palm, B., Hellsten, H., Machado, R., Vu, V. T., Pettersson, M. & Dammert, P. (2023). Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem: An Iterative Approach. IEEE Access, 11, 84734-84743
Open this publication in new window or tab >>Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem: An Iterative Approach
Show others...
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 84734-84743Article in journal (Refereed) Published
Abstract [en]

This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Bayes’ theorem, CARABAS II, Data models, Gaussian distribution, Histograms, iterative change detection, Iterative methods, Radar polarimetry, SAR, Stability analysis, Surveillance, wavelength-resolution SAR images, Change detection, Clutter (information theory), Errors, Image segmentation, Radar clutter, Radar imaging, Synthetic aperture radar, Ultra-wideband (UWB), Baye's theorem, CARABAS, Histogram, SAR Images, Stability analyze, Wavelength resolution, Wavelength-resolution SAR image
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-25326 (URN)10.1109/ACCESS.2023.3303107 (DOI)001049927400001 ()2-s2.0-85167776056 (Scopus ID)
Available from: 2023-08-25 Created: 2023-08-25 Last updated: 2023-09-04Bibliographically approved
Ludwig Barbosa, V., Rasch, J., Sievert, T., Carlström, A., Pettersson, M., Vu, V. T. & Christensen, J. (2023). Detection and localization of F-layer ionospheric irregularities with the back-propagation method along the radio occultation ray path. Atmospheric Measurement Techniques, 16(7), 1849-1864
Open this publication in new window or tab >>Detection and localization of F-layer ionospheric irregularities with the back-propagation method along the radio occultation ray path
Show others...
2023 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 16, no 7, p. 1849-1864Article in journal (Refereed) Published
Abstract [en]

The back propagation (BP) method consists of diffractive integrals computed over a trajectory path, projecting a signal to different planes. It unwinds the diffraction and multipath, resulting in minimum disturbance on the BP amplitude when the auxiliary plane coincides with the region causing the diffraction. The method has been previously applied in GNSS Radio Occultation (RO) measurements showing promising results in the location estimate of ionospheric irregularities but without complementary data to validate the estimation. In this study, we investigate with wave optics propagator (WOP) simulations of an equatorial C/NOFS occultation with scintillation signatures caused by an equatorial plasma bubble (EPB), which was parametrized with aid of collocated data. In addition, a few more test cases were designed to assess the BP method regarding size, intensity and placement of single and multiple irregularity regions. The results show a location estimate accuracy of 10 km (single bubble, reference case), where in multiple bubble scenarios only the strongest disturbance would be resolved properly. The minimum detectable disturbance level and the estimation accuracy depend on the receiver noise level, and in the case of several bubbles on the distance between them. The remarks of the evaluation supported the interpretation of results for two COSMIC occultations.

Place, publisher, year, edition, pages
Copernicus Publications, 2023
National Category
Meteorology and Atmospheric Sciences
Research subject
Systems Engineering
Identifiers
urn:nbn:se:bth-22798 (URN)10.5194/amt-2022-57 (DOI)000962705900001 ()2-s2.0-85152796342 (Scopus ID)
Projects
Swedish National Space Board, NRFP-4
Funder
Swedish National Space Board
Available from: 2022-03-28 Created: 2022-03-28 Last updated: 2023-05-01Bibliographically approved
Björklund, S. & Pettersson, M. (2023). Factors Affecting the Effective Clutter Rank for Planar and Conformal Antennas with Subarrays. In: Proceedings of the IEEE Radar Conference 2023: . Paper presented at IEEE International Radar Conference, RADAR 2023, Sydney, 6 November through 10 November 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Factors Affecting the Effective Clutter Rank for Planar and Conformal Antennas with Subarrays
2023 (English)In: Proceedings of the IEEE Radar Conference 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

The Effective Clutter Rank (ECR), the number of eigenvalues of the clutter covariance matrix larger than the white noise, has important consequences for the radar system when suppressing clutter with Space-Time Adaptive Processing (STAP), in terms of cost, complexity, usability and performance. In this paper some factors affecting the ECR are studied by simulations. The result is partly explained by theory from the literature. The main results are: 1) Factors affecting the ECR [subarray beam pointing direction, subarray design, antenna geometry, # radar pulses, PRF, radar velocity and target range]. 2) Differences between planar and conformal antennas. 3) A simulation-based rank calculation method for antennas with subarrays.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Radar (RADAR), ISSN 1097-5764, E-ISSN 2640-7736
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-25854 (URN)10.1109/radar54928.2023.10371089 (DOI)2-s2.0-85182748376 (Scopus ID)
Conference
IEEE International Radar Conference, RADAR 2023, Sydney, 6 November through 10 November 2023
Projects
R&D programme for Sensors and low observables
Funder
Swedish Armed Forces, FoT SoS, AT.9220423
Available from: 2024-01-03 Created: 2024-01-03 Last updated: 2024-02-02Bibliographically approved
Javadi, S., Palm, B., Vu, V. T., Pettersson, M. & Sjögren, T. (2023). Harbour Area Change Detection and Analysis Using SAR Images from a Recent Measurement Campaign. In: Proceedings of the IEEE Radar Conference 2023: . Paper presented at IEEE International Radar Conference, RADAR 2023, Sydney, 6 November through 10 November 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Harbour Area Change Detection and Analysis Using SAR Images from a Recent Measurement Campaign
Show others...
2023 (English)In: Proceedings of the IEEE Radar Conference 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

Synthetic aperture radar (SAR) data are widely used for remote sensing applications, such as change detection and environmental monitoring. This paper presents a recent measurement campaign for SAR images using the LORA system and investigates the applicability of the collected data for change detection. The region of interest in this study is a busy commercial harbour area in the south of Sweden. During the measurements, there were significant changes on the ground in the parking lot as trucks were disembarking a ship. The obtained SAR images were first filtered to have similar regions of interest in the Fourier domain to increase the coherence magnitude. Then, a constant false alarm rate (CFAR) algorithm was employed to detect changes with respect to trucks. In addition, optical aerial images were collected during this measurement campaign and were utilized to adjust the CFAR detection threshold. As a result, all the changed and unchanged regions corresponding to the selected targets were detected successfully. Moreover, a pattern of trucks’ utilization of the harbour’s parking lot during this peak time was obtained. The results demonstrate the applicability of the data from the ongoing measurement campaign and the possibility of further algorithm development for target detection and classification.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Radar (RADAR), ISSN 1097-5764, E-ISSN 2640-7736
Keywords
aerial images, CFAR detector, change detection, LORA system, SAR images
National Category
Remote Sensing
Identifiers
urn:nbn:se:bth-25853 (URN)10.1109/radar54928.2023.10371086 (DOI)2-s2.0-85182738528 (Scopus ID)
Conference
IEEE International Radar Conference, RADAR 2023, Sydney, 6 November through 10 November 2023
Available from: 2024-01-03 Created: 2024-01-03 Last updated: 2024-02-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6643-312X

Search in DiVA

Show all publications