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Publications (10 of 109) Show all publications
Vu, V. T., Gomes, N. R., Pettersson, M., Dämmert, P. & Hellsten, H. (2019). Bivariate Gamma Distribution for Wavelength-Resolution SAR Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 473-481
Open this publication in new window or tab >>Bivariate Gamma Distribution for Wavelength-Resolution SAR Change Detection
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2019 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 57, no 1, p. 473-481Article in journal (Refereed) Published
Abstract [en]

A gamma probability density function (pdf) is shown to be an alternative to model the distribution of the magnitudes of high-resolution, i.e., wavelength-resolution, synthetic aperture radar (SAR) images. As investigated in this paper, it is more appropriate and more realistic statistical in comparison with, e.g., Rayleigh. A bivariate gamma pdf is considered for developing a statistical hypothesis test for wavelength-resolution incoherent SAR change detection. The practical issues in implementation of statistical hypothesis test, such as assumptions on target magnitudes, estimations for scale and shape parameters, and implementation of modified Bessel function, are addressed. This paper also proposes a simple processing scheme for incoherent change detection to validate the proposed statistical hypothesis test. The proposal was experimented with 24 CARABAS data sets. With an average detection probability of 96%, the false alarm rate is only 0.47 per square kilometer. IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Bivariate gamma, CARABAS, change detection, synthetic aperture radar (SAR)., Probability density function, Probability distributions, Radar imaging, Statistical tests, Tracking radar, Bivariate, Bivariate gamma distribution, Probability density function (pdf), Scale and shape parameters, Statistical hypothesis test, Synthetic aperture radar (SAR) images, Synthetic aperture radar
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-16936 (URN)10.1109/TGRS.2018.2856926 (DOI)000455089000036 ()2-s2.0-85051396922 (Scopus ID)
Available from: 2018-08-24 Created: 2018-08-24 Last updated: 2019-01-28Bibliographically approved
Gomes, N. R., Dammert, P., Pettersson, M., Vu, V. T. & Hellsten, H. (2019). Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection. IEEE Geoscience and Remote Sensing Letters, 16, 756-760
Open this publication in new window or tab >>Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection
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2019 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 16, p. 756-760Article in journal (Refereed) Published
Abstract [en]

The aim of this letter is to compare two incoherent change-detection algorithms for target detection in low-frequency ultrawideband (UWB) synthetic aperture radar (SAR) images. The considered UWB SAR operates in the frequency range from 20 to 90 MHz. Both approaches employ a likelihood ratio test according to the Neyman–Pearson criterion. First, the bivariate Rayleigh probability distribution is used to implement the likelihood ratio test function. This distribution is well known and has been used for change-detection algorithms in low-frequency UWB SAR with good results. Aiming to minimize the false alarm rate and taking into consideration that low-frequency UWB SAR images have high resolution compared to the transmitted wavelength, the second approach implements the test by using a bivariate K-distribution. This distribution has scale and shape parameters that can be used to adjust it to the data. No filter is applied to the data set images, and the results show that with a good statistical model, it is not needed to rely on filtering the data to decrease the number of false alarms. Therefore, we can have a better tradeoff between resolution and detection performance.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Change detection, likelihood ratio test, synthetic aperture radar (SAR).
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-17863 (URN)10.1109/LGRS.2018.2881733 (DOI)000466228400019 ()
Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2019-06-14Bibliographically approved
Javadi, M. S., Dahl, M., Pettersson, M. & Kulesza, W. (2019). Design of a video-based vehicle speed measurement system: an uncertainty approach. In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 2018, pp. 44-49.: . Paper presented at Joint 7th International Conference on Informatics, Electronics and Vision and 2nd International Conference on Imaging, Vision and Pattern Recognition, ICIEV-IVPR 2018; Kitakyushu; Japan; 25-28 June 2018. IEEE, Article ID 8640964.
Open this publication in new window or tab >>Design of a video-based vehicle speed measurement system: an uncertainty approach
2019 (English)In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 2018, pp. 44-49., IEEE, 2019, article id 8640964Conference paper, Published paper (Refereed)
Abstract [en]

Speed measurement is one of the key components of intelligent transportation systems. It provides suitable information for traffic management and law enforcement. This paper presents a versatile and analytical model for a video-based speed measurement in form of the probability density function (PDF). In the proposed model, the main factors contributing to the uncertainties of the measurement are considered. Furthermore, a guideline is introduced in order to design a video-based speed measurement system based on the traffic and other requirements. As a proof of concept, the model has been simulated and tested for various speeds. An evaluation validates the strength of the model for accurate speed measurement under realistic circumstances.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Intelligent transportation systems, Machine vision, Motion analysis, Pattern recognition, Speed measurement
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-17163 (URN)10.1109/ICIEV.2018.8640964 (DOI)000462610300008 ()9781538651612 (ISBN)
Conference
Joint 7th International Conference on Informatics, Electronics and Vision and 2nd International Conference on Imaging, Vision and Pattern Recognition, ICIEV-IVPR 2018; Kitakyushu; Japan; 25-28 June 2018
Available from: 2018-10-23 Created: 2018-10-23 Last updated: 2019-06-28Bibliographically approved
Palm, B., Bayer, F., Cintra, R., Pettersson, M. & Machado, R. (2019). Rayleigh Regression Model for Ground Type Detection in SAR Imagery. IEEE Geoscience and Remote Sensing Letters
Open this publication in new window or tab >>Rayleigh Regression Model for Ground Type Detection in SAR Imagery
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2019 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571Article in journal (Refereed) Epub ahead of print
Abstract [en]

This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Detection, Rayleigh distribution, regression model, reparameterized Rayleigh distribution, synthetic aperture radar (SAR) images
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-17861 (URN)10.1109/LGRS.2018.2881733 (DOI)
Available from: 2019-04-29 Created: 2019-04-29 Last updated: 2019-05-03Bibliographically approved
Javadi, M. S., Dahl, M. & Pettersson, M. (2019). Vehicle speed measurement model for video-based systems. Computers & electrical engineering, 76, 238-248
Open this publication in new window or tab >>Vehicle speed measurement model for video-based systems
2019 (English)In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 76, p. 238-248Article in journal (Refereed) Published
Abstract [en]

Advanced analysis of road traffic data is an essential component of today's intelligent transportation systems. This paper presents a video-based vehicle speed measurement system based on a proposed mathematical model using a movement pattern vector as an input variable. The system uses the intrusion line technique to measure the movement pattern vector with low computational complexity. Further, the mathematical model introduced to generate the pdf (probability density function) of a vehicle's speed that improves the speed estimate. As a result, the presented model provides a reliable framework with which to optically measure the speeds of passing vehicles with high accuracy. As a proof of concept, the proposed method was tested on a busy highway under realistic circumstances. The results were validated by a GPS (Global Positioning System)-equipped car and the traffic regulations at the measurement site. The experimental results are promising, with an average error of 1.77 % in challenging scenarios.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Intelligent transportation systems; Machine vision; Motion analysis; Pattern recognition; Speed measurement system
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-17161 (URN)10.1016/j.compeleceng.2019.04.001 (DOI)000470954900019 ()
Note

open access

Available from: 2018-10-23 Created: 2018-10-23 Last updated: 2019-06-27Bibliographically approved
Vu, V. T., Pettersson, M., Sjögren, T. & Gustavsson, A. (2018). A hybrid GMTI method for reliable detec ion results in SAR images. In: Bao, VNQ Duy, TT (Ed.), PROCEEDINGS OF 2018 2ND INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN SIGNAL PROCESSING, TELECOMMUNICATIONS & COMPUTING (SIGTELCOM 2018): . Paper presented at Conference: 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing (SigTelCom), Ho Chi Minh, JAN 29-31 (pp. 73-78). IEEE
Open this publication in new window or tab >>A hybrid GMTI method for reliable detec ion results in SAR images
2018 (English)In: PROCEEDINGS OF 2018 2ND INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN SIGNAL PROCESSING, TELECOMMUNICATIONS & COMPUTING (SIGTELCOM 2018) / [ed] Bao, VNQ Duy, TT, IEEE , 2018, p. 73-78Conference paper, Published paper (Refereed)
Abstract [en]

Stand-alone synthetic aperture radar (SAR) ground moving target indication (GNITI) methods have both advantages and disadvantages. This paper introduces a hybrid SAR GMTI method that is based on two well-known methods: space time adaptive processing (STAP) and moving target detection by focusing (MTDF). The input of the proposed hybrid method is two time separated complex radar images. The output is detected ground moving targets, the target normalized relative speeds (NRS), and focused images of the detected targets. In the paper, we provide the mathematical background behind the hybrid SAR GMTI method in details. We also provide some experimental results for validating the proposed method. The data for the experiments was acquired in early 2015 by TanDEMX and TerraSAR-X operating in monostatic pursuit mode. The ground scene where the measurements were conducted is around Mantorp, west of Linkoping, Sweden.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Telecommunications Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-17697 (URN)000458556800014 ()978-1-5386-2976-5 (ISBN)
Conference
Conference: 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing (SigTelCom), Ho Chi Minh, JAN 29-31
Available from: 2019-03-07 Created: 2019-03-07 Last updated: 2019-03-08Bibliographically approved
Vu, V. T., Pettersson, M., Sjögren, T. & Gustavsson, A. (2018). A hybrid GMTI method for reliable detection results in SAR images. In: Proceedings - 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SIGTELCOM 2018: . Paper presented at 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SIGTELCOM, Ho Chi Minh City (pp. 73-78). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>A hybrid GMTI method for reliable detection results in SAR images
2018 (English)In: Proceedings - 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SIGTELCOM 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 73-78Conference paper, Published paper (Refereed)
Abstract [en]

Stand-alone synthetic aperture radar (SAR) ground moving target indication (GMTI) methods have both advantages and disadvantages. This paper introduces a hybrid SAR GMTI method that is based on two well-known methods: space time adaptive processing (STAP) and moving target detection by focusing (MTDF). The input of the proposed hybrid method is two time separated complex radar images. The output is detected ground moving targets, the target normalized relative speeds (NRS), and focused images of the detected targets. In the paper, we provide the mathematical background behind the hybrid SAR GMTI method in details. We also provide some experimental results for validating the proposed method. The data for the experiments was acquired in early 2015 by TanDEM-X and TerraSAR-X operating in monostatic pursuit mode. The ground scene where the measurements were conducted is around Mantorp, west of Linköping, Sweden. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Radar signal processing, Space time adaptive processing, Synthetic aperture radar, Ground moving target indication, Ground moving targets, Hybrid method, Monostatic, Moving target detection, Reliable detection, Stand -alone, TerraSAR-X, Radar imaging
National Category
Signal Processing Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-16538 (URN)10.1109/SIGTELCOM.2018.8325809 (DOI)2-s2.0-85047862168 (Scopus ID)9781538629765 (ISBN)
Conference
2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SIGTELCOM, Ho Chi Minh City
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18Bibliographically approved
Rameez, M., Dahl, M. & Pettersson, M. (2018). Adaptive digital beamforming for interference suppression in automotive FMCW radars. In: 2018 IEEE Radar Conference, (RadarConf 2018): . Paper presented at 2018 IEEE Radar Conference,Oklahoma City (pp. 252-256). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Adaptive digital beamforming for interference suppression in automotive FMCW radars
2018 (English)In: 2018 IEEE Radar Conference, (RadarConf 2018), Institute of Electrical and Electronics Engineers Inc. , 2018, p. 252-256Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of mutual interference between automotive radars. This problem is getting more attention with an increase in the number of radar systems used in traffic. An adaptive digital beamforming technique is presented here which suppresses the interference without the exact knowledge of the interfering signal's Direction of Arrival (DoA). The proposed technique is robust and does not rely on any calibration for the interference cancellation. The adaptive interference suppression method is evaluated using a simulated scenario. Up to about 20-23 dB improvement in the target Signal to Interference and Noise Ratio (SINR) is measured in the simulation and a better detection performance is achieved using the proposed interference suppression technique. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Series
IEEE Radar Conference
Keywords
Beamforming, Frequency modulation, Radar systems, Signal to noise ratio, Adaptive digital beamforming, Adaptive interference suppression, Automotive radar, Detection performance, Interference cancellation, Interfering signals, Mutual interference, Target signals, Radar interference
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-16910 (URN)10.1109/RADAR.2018.8378566 (DOI)000442172700046 ()2-s2.0-85049977586 (Scopus ID)978-1-5386-4167-5 (ISBN)
Conference
2018 IEEE Radar Conference,Oklahoma City
Available from: 2018-08-20 Created: 2018-08-20 Last updated: 2018-09-20Bibliographically approved
Sievert, T., Rasch, J., Carlström, A. & Pettersson, M. (2018). Analysis of reflections in GNSS radio occultation measurements using the phase matching amplitude. Atmospheric Measurement Techniques, 11(1), 569-580
Open this publication in new window or tab >>Analysis of reflections in GNSS radio occultation measurements using the phase matching amplitude
2018 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 11, no 1, p. 569-580Article in journal (Refereed) Published
Abstract [en]

It is well-known that in the presence of super-refractive layers in the lower-tropospheric inversion of GNSSradio occultation (RO) measurements using the Abel trans-form yields biased refractivity profiles. As such it is problem-atic to reconstruct the true refractivity from the RO signal.Additional information about this lower region of the atmo-sphere might be embedded in reflected parts of the signal. Toretrieve the bending angle, the phase matching operator canbe used. This operator produces a complex function of theimpact parameter, and from its phase we can calculate thebending angle. Instead of looking at the phase, in this paperwe focus on the function’s amplitude. The results in this pa-per show that the signatures of surface reflections in GNSSRO measurements can be significantly enhanced when usingthe phase matching method by processing only an appropri-ately selected segment of the received signal. This signatureenhancement is demonstrated by simulations and confirmedwith 10 hand-picked MetOp-A occultations with reflectedcomponents. To validate that these events show signs of re-flections, radio holographic images are generated. Our resultssuggest that the phase matching amplitude carries informa-tion that can improve the interpretation of radio occultationmeasurements in the lower troposphere.

Place, publisher, year, edition, pages
Nicolaus Copernicus University Press, 2018
National Category
Remote Sensing Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:bth-15843 (URN)10.5194/amt-11-569-2018 (DOI)000449173700001 ()
Funder
Knowledge Foundation, 20140192Swedish National Space Board, 241/15
Note

open access

Available from: 2018-01-31 Created: 2018-01-31 Last updated: 2019-01-10Bibliographically approved
Palm, B., Alves, D., Vu, V. T., Pettersson, M., Bayer, F., Cintra, R., . . . Hellsten, H. (2018). Autoregressive model for multi-pass SAR change detection based on image stacks. In: Bovolo F.,Bruzzone L. (Ed.), Proceedings of SPIE - The International Society for Optical Engineering: . Paper presented at Image and Signal Processing for Remote Sensing XXIV 2018, Berlin, 10 September 2018 through 12 September 2018. SPIE, 10789, Article ID 1078916.
Open this publication in new window or tab >>Autoregressive model for multi-pass SAR change detection based on image stacks
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2018 (English)In: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Bovolo F.,Bruzzone L., SPIE , 2018, Vol. 10789, article id 1078916Conference paper, Published paper (Refereed)
Abstract [en]

Change detection is an important synthetic aperture radar (SAR) application, usually used to detect changes on the ground scene measurements in different moments in time. Traditionally, change detection algorithm (CDA) is mainly designed for two synthetic aperture radar (SAR) images retrieved at different instants. However, more images can be used to improve the algorithms performance, witch emerges as a research topic on SAR change detection. Image stack information can be treated as a data series over time and can be modeled by autoregressive (AR) models. Thus, we present some initial findings on SAR change detection based on image stack considering AR models. Applying AR model for each pixel position in the image stack, we obtained an estimated image of the ground scene which can be used as a reference image for CDA. The experimental results reveal that ground scene estimates by the AR models is accurate and can be used for change detection applications. © 2018 SPIE.

Place, publisher, year, edition, pages
SPIE, 2018
Series
Proceedings of SPIE, ISSN 0277-786X
Keywords
AR models, Change detection, SAR, Time series, Image enhancement, Radar measurement, Remote sensing, Synthetic aperture radar, Auto regressive models, Change detection algorithms, Pixel position, Reference image, Research topics, Synthetic aperture radar (SAR) images, Radar imaging
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-17475 (URN)10.1117/12.2325661 (DOI)000455305000036 ()2-s2.0-85059005687 (Scopus ID)9781510621619 (ISBN)
Conference
Image and Signal Processing for Remote Sensing XXIV 2018, Berlin, 10 September 2018 through 12 September 2018
Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6643-312x

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