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Jansson, Magnus, ProfessorORCID iD iconorcid.org/0000-0002-6855-5868
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Publications (10 of 169) Show all publications
Karlsson, A., Jansson, M. & Hamalainen, M. (2023). Anomaly-Based Drone Classification Using a Model Trained Convolutional Neural Network Autoencoder on Radar Micro-Doppler. In: 2023 IEEE International Radar Conference, RADAR 2023: . Paper presented at 2023 IEEE International Radar Conference, RADAR 2023, Sydney, Australia, Nov 6 2023 - Nov 10 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Anomaly-Based Drone Classification Using a Model Trained Convolutional Neural Network Autoencoder on Radar Micro-Doppler
2023 (English)In: 2023 IEEE International Radar Conference, RADAR 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

We present an anomaly-based drone classification scheme. High dimensional spectrum data is encoded using a convolutional neural network autoencoder. This is trained on data generated from a generic mathematical drone model. Once encoded, we use quadratic discriminant analysis for non-drone classes and define anomalies in terms of the log likelihood and prior knowledge from the drone model. When integrating ten samples, we can discriminate drones from non-drone samples such as birds, with an average accuracy of 98% at 20 dB signal to noise ratio. This corresponds to an effective observation time of 90 ms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
bird, classification, deep learning, drone, high dimensional anomaly detection, QDA, radar, RCS modeling
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-342796 (URN)10.1109/RADAR54928.2023.10371163 (DOI)2-s2.0-85182724964 (Scopus ID)
Conference
2023 IEEE International Radar Conference, RADAR 2023, Sydney, Australia, Nov 6 2023 - Nov 10 2023
Note

Part of proceedings ISBN 9781665482783

QC 20240201

Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-02-01Bibliographically approved
Karlsson, A., Jansson, M. & Hamalainen, M. (2023). Data Driven Track Before Detect Using Artificial Neural Networks. In: 2023 IEEE International Radar Conference, RADAR 2023: . Paper presented at 2023 IEEE International Radar Conference, RADAR 2023, Sydney, Australia, Nov 6 2023 - Nov 10 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Data Driven Track Before Detect Using Artificial Neural Networks
2023 (English)In: 2023 IEEE International Radar Conference, RADAR 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

We present two neural network solutions for data driven track before detect applications. The detected tracks may be used to estimate good initial states for traditional trackers such as Kalman filters. We evaluate the method on different scenarios with multiple targets, non-linear trajectories, and different signal to noise ratio (SNR) values. Depending on scenario, the presented method achieves 99% detection probability on Swerling 3 and 4 targets at 5 - 13 dB SNR, with 0.04 - 0.001 false tracks per frame. The presented method is compared to a theoretically optimal detector.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
detection association, max channel, neural networks, radar, track before detect, weak target detection
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-342821 (URN)10.1109/RADAR54928.2023.10371120 (DOI)2-s2.0-85182726066 (Scopus ID)
Conference
2023 IEEE International Radar Conference, RADAR 2023, Sydney, Australia, Nov 6 2023 - Nov 10 2023
Note

Part of proceedings ISBN 9781665482783

QC 20240201

Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-02-01Bibliographically approved
Karlsson, A., Jansson, M. & Hämäläinen, M. (2023). Low-Angle Target Tracking in Sea Surface Multipath Using Convolutional Neural Networks. IEEE Transactions on Aerospace and Electronic Systems, 59(5), 6813-6831
Open this publication in new window or tab >>Low-Angle Target Tracking in Sea Surface Multipath Using Convolutional Neural Networks
2023 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, Vol. 59, no 5, p. 6813-6831Article in journal (Refereed) Published
Abstract [en]

Multipath interference while tracking sea-skimming targets can significantly distort the estimated height of the target. If accounted for however, this interference can be used to obtain more accurate estimates. In this study, we accomplish this with a convolutional neural network (CNN) used as a parameter estimator. The performance of this network is compared with maximum likelihood and least-squares methods. We found that the CNN performs well in comparison to these methods with only a fraction of the computations.

Place, publisher, year, edition, pages
New York, USA: Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
deep learning, frequency agile radar, low angle tracking, multipath, parameter estimation, phase monopulse
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-339921 (URN)10.1109/taes.2023.3282191 (DOI)2-s2.0-85161593958 (Scopus ID)
Funder
EU, European Research Council, 742648Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20231124

Available from: 2023-11-22 Created: 2023-11-22 Last updated: 2023-11-24Bibliographically approved
Borpatra Gohain, P. & Jansson, M. (2023). Model Selection in High-Dimensional Block-Sparse General Linear Regression. In: : . Paper presented at 31st European Signal Processing Conference, EUSIPCO 2023, Helsinki, Finland (pp. 1928-1932). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Model Selection in High-Dimensional Block-Sparse General Linear Regression
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Model selection is an indispensable part of data analysis dealing very frequently with fitting and prediction purposes. In this paper, we tackle the problem of model selection in a general linear regression where the parameter matrix possesses a block-sparse structure, i.e., the non-zero entries occur in clusters or blocks and the number of such non-zero blocks is very small compared to the parameter dimension. Furthermore, a high-dimensional setting is considered where the parameter dimension is quite large compared to the number of available measurements. To perform model selection in this setting, we present an information criterion that is a generalization of the Extended Bayesian Information Criterion-Robust (EBIC-R) and it takes into account both the block structure and the high-dimensionality scenario. We name it Generalized EBIC-R (GEBIC-R). The analytical steps for deriving the GEBIC-R are provided. Simulation results show that the proposed method performs considerably better than the existing state-of-the-art methods and achieves empirical consistency at large sample sizes and/or at high-SNR.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Model selection, block-sparsity, compressed sensing, information criterion, orthogonal matching pursuit
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-339924 (URN)10.23919/EUSIPCO58844.2023.10289738 (DOI)2-s2.0-85178377888 (Scopus ID)
Conference
31st European Signal Processing Conference, EUSIPCO 2023, Helsinki, Finland
Funder
EU, European Research Council, 742648
Note

Part of ISBN 978-9-4645-9360-0

QC 20231124

Available from: 2023-11-22 Created: 2023-11-22 Last updated: 2023-12-15Bibliographically approved
Borpatra Gohain, P. & Jansson, M. (2023). Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models. IEEE Transactions on Signal Processing, 71, 2251-2266
Open this publication in new window or tab >>Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models
2023 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 71, p. 2251-2266Article in journal (Refereed) Published
Abstract [en]

Model selection in linear regression models is a major challenge when dealing with high-dimensional data where the number of available measurements (sample size) is much smaller than the dimension of the parameter space. Traditional methods for model selection such as Akaike information criterion, Bayesian information criterion (BIC), and minimum description length are heavily prone to overfitting in the high-dimensional setting. In this regard, extended BIC (EBIC), which is an extended version of the original BIC, and extended Fisher information criterion (EFIC), which is a combination of EBIC and Fisher information criterion, are consistent estimators of the true model as the number of measurements grows very large. However, EBIC is not consistent in high signal-to-noise-ratio (SNR) scenarios where the sample size is fixed and EFIC is not invariant to data scaling resulting in unstable behaviour. In this article, we propose a new form of the EBIC criterion called EBIC-Robust, which is invariant to data scaling and consistent in both large sample sizes and high-SNR scenarios. Analytical proofs are presented to guarantee its consistency. Simulation results indicate that the performance of EBIC-Robust is quite superior to that of both EBIC and EFIC.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
High-dimension, linear regression, data scaling, statistical model selection, subset selection, sparse estimation, scale-invariant, variable selection
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-333537 (URN)10.1109/TSP.2023.3284365 (DOI)001018661000010 ()2-s2.0-85162629444 (Scopus ID)
Funder
EU, European Research Council, 742648
Note

QC 20230803

Available from: 2023-08-03 Created: 2023-08-03 Last updated: 2023-11-22Bibliographically approved
Champati, J. P., Skoglund, M., Jansson, M. & Gross, J. (2022). Detecting State Transitions of a Markov Source: Sampling Frequency and Age Trade-off. IEEE Transactions on Communications, 70(5), 3081-3095
Open this publication in new window or tab >>Detecting State Transitions of a Markov Source: Sampling Frequency and Age Trade-off
2022 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 70, no 5, p. 3081-3095Article in journal (Refereed) Published
Abstract [en]

We consider a finite-state Discrete-Time Markov Chain (DTMC) source that can be sampled for detecting the events when the DTMC transits to a new state. Our goal is to study the trade-off between sampling frequency and staleness in detecting the events. We argue that, for the problem at hand, using Age of Information (AoI) for quantifying the staleness of a sample is conservative and therefore, study another freshness metric age penalty, which is defined as the time elapsed since the first transition out of the most recently observed state. We study two optimization problems: minimize average age penalty subject to an average sampling frequency constraint, and minimize average sampling frequency subject to an average age penalty constraint; both are Constrained Markov Decision Problems. We solve them using the Lagrangian MDP approach, where we also provide structural results that reduce the search space. Our numerical results demonstrate that the computed Markov policies not only outperform optimal periodic sampling policies, but also achieve sampling frequencies close to or lower than that of an optimal clairvoyant (non-causal) sampling policy, if a small age penalty is allowed.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Measurement, Markov processes, Frequency shift keying, Web pages, Delays, Databases, Receivers, Age of information, age penalty, sampling, DTMC source, CMDP
National Category
Telecommunications Computer Systems
Identifiers
urn:nbn:se:kth:diva-313504 (URN)10.1109/TCOMM.2022.3160563 (DOI)000797439600018 ()2-s2.0-85126675858 (Scopus ID)
Funder
EU, European Research Council, 742648Swedish Research Council, 2016-04404
Note

QC 20220607

Available from: 2022-06-07 Created: 2022-06-07 Last updated: 2023-11-22Bibliographically approved
Karlsson, A., Jansson, M. & Hämäläinen, M. (2022). Model-Aided Drone Classification Using Convolutional Neural Networks. In: : . Paper presented at IEEE Radar Conference (RadarConf22), 21-25 March 2022, New York City, NY, USA (pp. 1-6). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Model-Aided Drone Classification Using Convolutional Neural Networks
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Classifiers using convolutional neural networks (CNNs) often yield high accuracies on samples that come from the same distribution as the training data. In this study we evaluate a CNN classifier's ability to discriminate drones from non-drone targets, such as birds, when they are not represented in the training data. We found that the mean accuracy on such out-of-distribution drones was 78%. By introducing a synthetic drone class, generated from a mathematical model, the out-of-distribution drone accuracy was improved to 86%. When trained on all drone types the mean accuracy over all classes was 90%. The data was collected with a 77 GHz mechanically scanning radar with only 9 ms dwell time.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
classification, FMCW radar, deep learning, drone, UAV, bird, RCS, millimeter wave
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-312176 (URN)10.1109/RadarConf2248738.2022.9764194 (DOI)000821555200055 ()2-s2.0-85146199498 (Scopus ID)
Conference
IEEE Radar Conference (RadarConf22), 21-25 March 2022, New York City, NY, USA
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)EU, European Research Council, grant No. 742648
Note

Part of proceedings: ISBN 978-1-7281-5368-1

QC 20220519

Available from: 2022-05-14 Created: 2022-05-14 Last updated: 2023-06-08Bibliographically approved
Borpatra Gohain, P. & Jansson, M. (2022). New Improved Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Virtual/Online 23-27 May 2022 (pp. 5692-5696). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>New Improved Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models
2022 (English)In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 5692-5696Conference paper, Published paper (Refereed)
Abstract [en]

Extended Bayesian information criterion (EBIC) and extended Fisher information criterion (EFIC) are two popular criteria for model selection in sparse high-dimensional linear regression models. However, EBIC is inconsistent in scenarios when the signal-to-noise-ratio (SNR) is high but the sample size is small, and EFIC is not invariant to data scaling, which affects its performance under different signal and noise statistics. In this paper, we present a refined criterion called EBIC R where the ‘R’ stands for robust. EBIC R is an improved version of EBIC and EFIC. It is scale-invariant and a consistent estimator of the true model as the sample size grows large and/or when the SNR tends to infinity. The performance of EBIC R is compared to existing methods such as EBIC, EFIC and multi-beta-test (MBT). Simulation results indicate that the performance of EBIC R in identifying the true model is either at par or superior to that of the other considered methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
High-dimensional inference, model selection, Lasso, OMP, sparse estimation, subset selection
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-312177 (URN)10.1109/ICASSP43922.2022.9746867 (DOI)000864187905197 ()2-s2.0-85131241340 (Scopus ID)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Virtual/Online 23-27 May 2022
Funder
EU, European Research Council, grant No. 742648
Note

QC 20220516

Available from: 2022-05-14 Created: 2022-05-14 Last updated: 2023-01-12Bibliographically approved
Borpatra Gohain, P. & Jansson, M. (2022). Scale-Invariant and consistent Bayesian information criterion for order selection in linear regression models. Signal Processing, 196, Article ID 108499.
Open this publication in new window or tab >>Scale-Invariant and consistent Bayesian information criterion for order selection in linear regression models
2022 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 196, article id 108499Article in journal (Refereed) Published
Abstract [en]

The Bayesian information criterion (BIC) is one of the most well-known criterion used for model order estimation in linear regression models. However, in its popular form, BIC is inconsistent as the noise variance tends to zero given that the sample size is small and fixed. Several modifications of the original BIC have been proposed that takes into account the high-SNR consistency, but it has been recently observed that the performance of the high-SNR forms of BIC highly depends on the scaling of the data. This scaling problem is a byproduct of the data dependent penalty design, which generates irregular penalties when the data is scaled and often leads to greater underfitting or overfitting losses in some scenarios when the noise variance is too small or large. In this paper, we present a new form of the BIC for order selection in linear regression models where the parameter vector dimension is small compared to the sample size. The proposed criterion eliminates the scaling problem and at the same time is consistent for both large sample sizes and high-SNR scenarios.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
BIC, Consistency, Linear regression, Model order selection, Scale-invariant
National Category
Probability Theory and Statistics Signal Processing
Identifiers
urn:nbn:se:kth:diva-311634 (URN)10.1016/j.sigpro.2022.108499 (DOI)000782990100004 ()2-s2.0-85126112427 (Scopus ID)
Funder
EU, European Research Council, 742648
Note

QC 20220530

Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2022-06-25Bibliographically approved
Karlsson, A., Jansson, M. & Holter, H. (2021). Stepped Frequency Pulse Compression with Non-Coherent Radar using Deep Learning. IEEE Transactions on Aerospace and Electronic Systems, 57(3), 1657-1671
Open this publication in new window or tab >>Stepped Frequency Pulse Compression with Non-Coherent Radar using Deep Learning
2021 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 57, no 3, p. 1657-1671Article in journal (Refereed) Published
Abstract [en]

A deep neural network (DNN) is used for achieving subpulse resolution in non-coherent stepped frequency waveform radar. The trade-off between high resolution and long range in radar systems is often addressed using pulse compression, allowing both long pulses and high resolution by increasing the pulse bandwidth. This typically requires a coherent radar. In this study we present a deep learning based solution for achieving subpulse resolution with a non-coherent radar. Our results for such a system are comparable to an equivalent coherent system for SNRs greater than 10 dB. All results are based on simulated data.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Keywords
Deep learning, frequency-agile radar, noncoherent radar, pulse compression, supervised learning, Bandwidth compression, Deep neural networks, Economic and social effects, Holography, Radar signal processing, Coherent radar, Coherent system, High resolution, Long pulse, Stepped frequency, Stepped frequency waveforms, Subpulse, Trade off
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-292895 (URN)10.1109/TAES.2020.3046336 (DOI)000659550200019 ()2-s2.0-85098746476 (Scopus ID)
Funder
EU, European Research Council, 742648
Note

QC 20250325

Available from: 2021-04-15 Created: 2021-04-15 Last updated: 2025-03-25Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-6855-5868

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