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Publications (10 of 109) Show all publications
Fredriksson, H., Dahl, M., Holmgren, J. & Lövström, B. (2024). Addressing Local and Regional Recharging Demand: Allocation of Charging Stations through Iterative Route Analysis. In: Elhadi Shakshuki (Ed.), Procedia Computer Science: . Paper presented at 15th International Conference on Ambient Systems, Networks and Technologies (ANT), Hasselt, Belgium, April 23-25, 2024 (pp. 65-72). Elsevier, 238
Open this publication in new window or tab >>Addressing Local and Regional Recharging Demand: Allocation of Charging Stations through Iterative Route Analysis
2024 (English)In: Procedia Computer Science / [ed] Elhadi Shakshuki, Elsevier, 2024, Vol. 238, p. 65-72Conference paper, Published paper (Refereed)
Abstract [en]

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an ``iterative route cover optimization method'' to suggest  charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each  route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. 

Place, publisher, year, edition, pages
Elsevier, 2024
Series
Procedia Computer Science, ISSN 1877-0509
Keywords
Allocation Strategy, Charging Station, Electric Vehicle, Recharging Demand
National Category
Transport Systems and Logistics
Research subject
Systems Engineering
Identifiers
urn:nbn:se:bth-26715 (URN)10.1016/j.procs.2024.05.197 (DOI)2-s2.0-85199527923 (Scopus ID)
Conference
15th International Conference on Ambient Systems, Networks and Technologies (ANT), Hasselt, Belgium, April 23-25, 2024
Available from: 2024-07-10 Created: 2024-07-10 Last updated: 2024-09-25Bibliographically approved
Hallösta, S., Pettersson, M. & Dahl, M. (2024). Effects of Foreground Augmentations in Synthetic Training Data on the Use of UAVs for Weed Detection. In: Lutchyn T., Rivera A.R., Ricaud B. (Ed.), Proceedings of Machine Learning Research: . Paper presented at 5th Northern Lights Deep Learning Conference, NLDL 2024, Tromso, 9 January through 11 January 2024. ML Research Press, 233
Open this publication in new window or tab >>Effects of Foreground Augmentations in Synthetic Training Data on the Use of UAVs for Weed Detection
2024 (English)In: Proceedings of Machine Learning Research / [ed] Lutchyn T., Rivera A.R., Ricaud B., ML Research Press , 2024, Vol. 233Conference paper, Published paper (Refereed)
Abstract [en]

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

Place, publisher, year, edition, pages
ML Research Press, 2024
Series
Proceedings of Machine Learning Research (PMLR), E-ISSN 2640-3498 ; 233
Keywords
Aircraft detection, Antennas, Large datasets, Unmanned aerial vehicles (UAV), Weed control, Aerial vehicle, Data augmentation, Herbicide resistant weeds, High resolution, Precision Agriculture, Synthetic data, Synthetic training data, Weed detection, Western Europe, Wheat yield
National Category
Computer graphics and computer vision Agricultural Science
Identifiers
urn:nbn:se:bth-26100 (URN)001221156400012 ()2-s2.0-85189301466 (Scopus ID)
Conference
5th Northern Lights Deep Learning Conference, NLDL 2024, Tromso, 9 January through 11 January 2024
Available from: 2024-04-12 Created: 2024-04-12 Last updated: 2025-02-01Bibliographically approved
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 graphics and computer vision
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: 2025-02-07Bibliographically approved
Hallösta, S., Javadi, S., Dahl, M. & Pettersson, M. (2024). Multispectral Image Registration and Sensor Calibration for Low-Altitude Agricultural Drones. In: International Geoscience and Remote Sensing Symposium (IGARSS): . Paper presented at IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, Athens, July 7-12, 2024 (pp. 6209-6213). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Multispectral Image Registration and Sensor Calibration for Low-Altitude Agricultural Drones
2024 (English)In: International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 6209-6213Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a crucial multispectral image registration and sensor calibration method for an agricultural application. The multispectral images are obtained using a special drone equipped with multiple cameras flying at low altitudes. However, the distance between lenses, the lens distortions and the low-altitude flights lead to a lack of alignment in the built-in normalized difference vegetation index (NDVI). This lack of alignment results in a very poor performance in further analysis, especially for image segmentation and target detection to distinguish crops from invasive plants. In this work, we point out the importance of reducing this misalignment. To do so, the near-infrared and red sensors are first calibrated to remove the lens distortions. Then, the corresponding keypoints are utilized to calculate the transformation matrix and to minimize the back-projection error. The registered near-infrared and red images are then used to compute NDVI. The experimental results show higher alignment and F1-score of 0.73 which is a significant improvement in the performance of a trained deep neural network using NDVI in the detection of invasive plants. This is particularly a challenging task as the invasive plants resemble the desired crops. © 2024 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
IEEE International Geoscience and Remote Sensing Symposium proceedings, ISSN 2153-6996, E-ISSN 2153-7003
Keywords
Multispectral image registration, near-infrared image, normalized difference vegetation index (NDVI), sensor calibration, unmanned aerial vehicles, Aircraft detection, Drones, Image enhancement, Image registration, Image segmentation, Aerial vehicle, Images registration, Invasive plants, Multispectral images, Near- infrared images, Normalized difference vegetation index, Unmanned aerial vehicle, Deep neural networks
National Category
Computer graphics and computer vision
Identifiers
urn:nbn:se:bth-27104 (URN)10.1109/IGARSS53475.2024.10642360 (DOI)2-s2.0-85208505152 (Scopus ID)
Conference
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, Athens, July 7-12, 2024
Available from: 2024-11-18 Created: 2024-11-18 Last updated: 2025-02-07Bibliographically approved
Joshani, M., Palm, B., Dahl, M. & Pettersson, M. (2024). Using a Two-Dimensional Autoregressive Model for Interference Mitigation in FMCW Radar. In: Proceedings International Radar Symposium: . Paper presented at 2024 International Radar Symposium, IRS 2024, Wroclaw, July 2-4 2024 (pp. 18-23). IEEE Computer Society
Open this publication in new window or tab >>Using a Two-Dimensional Autoregressive Model for Interference Mitigation in FMCW Radar
2024 (English)In: Proceedings International Radar Symposium, IEEE Computer Society, 2024, p. 18-23Conference paper, Published paper (Refereed)
Abstract [en]

This work confronts the complex issue of cross-interference in Frequency Modulated Continuous Wave (FMCW) radars, a critical concern that has become more pronounced with the proliferation of automotive radar systems. The study intro-duces a two-dimensional autoregressive (AR) modeling technique for signal reconstruction in the time domain, tailored specifically for the textured nature of FMCW radar frames composed of fast- time (Range bin) and slow-time (Doppler bin) signals. According to the simulations conducted in this study, the proposed 2-D AR model (of order 3) exhibits superior performance compared to its 1-D counterpart (of order 5). This is evidenced by a slightly lower Mean Absolute Percentage Error (MAPE) during model training and a higher Signal-to-Interference-plus-Noise Ratio (SINR) for the reconstructed signal, suggesting that the 2-D model requires less frequent temporal sampling. The study further investigates different sampling strategies and evaluates the influence of model order on signal reconstruction. Based on these assessments, a third-order 2-D AR is recommended as a suitable trade-off model for interference mitigation of FMCW radars for the evaluated scenarios. This paper is structured as follows: Section I defines the interference problem in FM CW radars and the latest solutions to this problem are discussed. Sections II and III include the working principles of FMCW radar and theoretical backgrounds about multi-dimension auto-regressive modeling, respectively. Eventually, the mitigation techniques and numerical evaluations of the proposed approach are presented in Sections IV and V. © 2024 Warsaw University of Technology.

Place, publisher, year, edition, pages
IEEE Computer Society, 2024
Series
Proceedings International Radar Symposium, ISSN 2155-5745, E-ISSN 2155-5753
Keywords
Autoregressive, FMCW radar, Interference mitigation, Two-dimensional, Amplitude shift keying, Automotive radar, Doppler effect, Frequency shift keying, Image coding, Image segmentation, Pulse amplitude modulation, Radar simulators, Signal to noise ratio, Auto-regressive, Automotive radar system, Autoregressive modeling techniques, Autoregressive modelling, Cross interference, Frequency-modulated-continuous-wave radars, Signals reconstruction, Time domain, Radar interference
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-26925 (URN)001307923500004 ()2-s2.0-85203701423 (Scopus ID)9788395602092 (ISBN)
Conference
2024 International Radar Symposium, IRS 2024, Wroclaw, July 2-4 2024
Available from: 2024-09-20 Created: 2024-09-20 Last updated: 2025-01-03Bibliographically approved
Fredriksson, H., Holmgren, J., Dahl, M. & Lövström, B. (2023). A Median-Based Misery Index for Travel Time Reliability. In: Elhadi Shakshuki (Ed.), Procedia Computer Science: . Paper presented at 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023, Leuven, 15 March through 17 March 2023 (pp. 162-169). Elsevier, 220
Open this publication in new window or tab >>A Median-Based Misery Index for Travel Time Reliability
2023 (English)In: Procedia Computer Science / [ed] Elhadi Shakshuki, Elsevier, 2023, Vol. 220, p. 162-169Conference paper, Published paper (Refereed)
Abstract [en]

Travel time reliability is vital for both road agencies and road users. Expected travel time reliability can be used by road agencies to assess the state of a transportation system, and by road users, to schedule their trips. Road network deficiencies, such as insufficient traffic flow capacity of a road segment or poor road design, have a negative impact on the reliability of travel times. Thus, to maintain robust and reliable travel times, the detection of road network deficiencies is vital. By continuously analyzing travel times and using appropriate travel time reliability measurements, it is possible to detect existing deficiencies or deficiencies that may eventually occur unless necessary actions are taken. In many cases, indices and measurements of travel time reliability are related to the distribution of the travel times, specifically the skewness and width of the distribution. The current paper introduces a median-based misery index for travel time reliability. The index is robust and handles travel times that follow a skewed distribution well. The index measures the relative difference between the slow travel speeds and the free-flow travel speed. The index is inspired by the median absolute deviation, and its primary application is to detect routes or road segments with potential road network deficiencies. To demonstrate the applicability of the index, we conducted an empirical case study using real travel speed data from the European route E4 in Sweden. The results from the empirical case study indicate that the index is capable of detecting road segments with slow travel speeds regardless of the travel speed distribution.

Place, publisher, year, edition, pages
Elsevier, 2023
Series
Procedia Computer Science, E-ISSN 1877-0509
Keywords
Travel time reliability, travel speed index, travel speed
National Category
Transport Systems and Logistics
Research subject
Mathematics and applications
Identifiers
urn:nbn:se:bth-24456 (URN)10.1016/j.procs.2023.03.023 (DOI)2-s2.0-85164538353 (Scopus ID)
Conference
14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023, Leuven, 15 March through 17 March 2023
Funder
Swedish Transport Administration
Available from: 2023-04-20 Created: 2023-04-20 Last updated: 2024-08-07Bibliographically approved
Fredriksson, H., Dahl, M., Holmgren, J., Lövström, B., Irvenå, J. & Mårtensson, M. (2022). Förstudie – Datadriven analys av restider.
Open this publication in new window or tab >>Förstudie – Datadriven analys av restider
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2022 (Swedish)Report (Other academic)
Abstract [sv]

Det uppkopplade samhället möjliggör kontinuerlig insamling av trafikdata genom de moderna fordonens navigationssystem, så väl som genom mobiltelefoner och andra GPS-enheter. Den ökade tillgången till trafikdata ger möjligheter analysera att trafiksystemet ur flera olika perspektiv. En typ av trafikdata som kan extraheras ur GPS-data är fordonshastigheter. Genom att analysera hur fordonshastigheter förändras över tid och identifiera avvikelser från ett normaltillstånd så skulle det vara möjligt att upptäcka och förutsäga potentiella brister i trafiksystemet. Många gånger upptäcks brister i infrastrukturen i ett relativt sent skede vilket i sin tur kan innebära både omfattande och kostsamma åtgärder för att komma till rätta med problemen.

Syftet med förstudien har varit att utveckla och utvärdera metoder och modeller för att detektera brister och identifiera hastigheter som relativt avviker från normaltillståndet, dvs ett fokus på fordonshastigheter som framför allt är ovanligt låga. Utgångspunkten har också varit att finna lämplig metod för att modellera trafiksituationen med hjälp av uppmätta fordonshastigheter. Det vill säga metoder som syftar till att ur ett övergripande perspektiv beskriva normaltillståndet längs med de studerade vägsträckorna. Analyser av normaltillståndets förändring över tid öppnar upp möjligheten att detektera om brister relaterat till vägsträckors kapacitet och framkomlighet uppstått eller avgöra om normaltillståndet på en vägsträcka är stabilt eller förändras över tid.

Det är framför allt de relativt låga fordonshastigheterna som uppstår som blir en indikator på att en vägsträcka har brister. Därför föreslås en metod för att systematiskt identifiera och gruppera uppmätta fordonshastigheter i låga, normala och höga hastigheter. En utgångspunkt har varit robusthet och att möjliggöra jämförelser av hastigheter för olika vägsträckor med olika attribut som antal körfält och skyltad hastighet med varandra. Vi presenterar även ett nytt mått som beskriver hur gruppen med relativt låga hastigheter förhåller sig till friflödeshastigheten som till exempel den skyltade hastigheten. Syftet med måttet är att kvantifiera framkomligheten på en vägsträcka eller vägsegment. Existerande mått och indikatorer baseras idag på fordonshastigheter som spänner från låga till höga hastigheter. Vi har i denna kontext tagit fram ett mått som endast tar hänsyn till vad som anses vara låga hastigheter och friflödeshastighet.

Inom förstudien så har även en metod baserad på klusteranalys använts för de studerade vägsträckorna. Klusteranalys har i olika sammanhang visat sig effektivt för att kategorisera och detektera återkommande mönster i hastighetsprofiler. Syftet med klusteranalysen är att undersöka om det finns någon koppling mellan hastighetsprofiler som har liknande beteende och till exempel veckodag och tidpunkt. Genom klusteranalys skulle det vara möjligt att inte bara detektera vilka vägsträckor där det uppstår problem, utan det skulle även vara möjligt att prognostisera vid vilka veckodagar och tidpunkter där det finns risk att köer och andra problem kan uppstå.

Förstudien är begränsad till användning av fordonhastigheter som datakälla och de framtagna metoderna och modellerna visar att det finns potential att frikoppla sig från andra datakällor som till exempel fordonsflöden för att detektera brister eller avvikelser som skulle kunna indikera brister i transportsystemet.

Abstract [en]

Modern vehicles are to a large extent connected today, either directly by built-in navigation systems in the vehicles or indirectly by other devices such as mobile phones and GPS units. This enables the possibility to continuously collect traffic data in a cost-effective way. The increased access to detailed data allows practitioners and researchers to analyze the transportation system from various perspectives. The travel speed is a common descriptor of the traffic state, and it can be extracted from GPS data. By analyzing how the travel speed vary over time and detect anomalies among the measured travel speeds, it is possible to detect potential deficiencies in the transportation system, e.g., insufficient road capacity which may cause bottlenecks. Often, a weakness in the infrastructure is detected in a very late stage which means that extensive investments may be required to resolve the deficiency.

The purpose of the pilot study is to develop methods and models to detect deficiency in the transportation system and to identity travel speeds that deviates from the normal state, i.e., travel speeds that are considered as very low or very high with respect to the normal behavior. Thus, the starting point of the pilot study is to find appropriate ways to model the traffic state along the studied road segments by using measured travel speeds from a general point of view. Analysis of the traffic state allows the study of how the normal state of the road segments change of time to detect deficiency related to road capacity and road access which may occur if no changes are made, or to detect road segments where the normal state is unchanged.

Typically, slower travel speeds may be an indicator of that a deficiency along a road segment exists. Thus, we present a method to systematically partition measured travel speeds in low, normal, and high travel speeds. The method is robust and enable the possibility to compare different road segment with different attributes, such as number of lanes and free-flow travel speed, with each other. Furthermore, we present a new measurement to describe how the low travel speeds relates to the free flow travel speed, e.g., the speed limit. Existing measurements and indicators used today utilize travel speeds which range from low to high. Our proposed measurement uses low travel speed and free flow travel speed exclusively and aims to quantify the accessibility and condition of a road segment.

The pilot study also includes an initial attempt to apply cluster analysis to detect recurrent patterns along the studied road segments. Cluster analysis is in several contexts an effective method to group time series to detect recurrent patterns among the speed profiles. The purpose of using cluster analysis is to evaluate if speed profiles with similar behavior is related to, for instance, weekday or time of the day. Thus, cluster analysis may be used to detect road segments with recurring low travel speeds, and potentially be used to forecast when congestion or queues may occur.

The pilot study is mainly limited to travel speed data. The proposed methods and models show that it is possibly to solely use travel speed data to detect deficiencies in the transportation system. In particular, the pilot study shows the potential to detect deficiencies in the transportation system without additional data sources such as link flow data.

National Category
Transport Systems and Logistics
Research subject
Mathematics and applications
Identifiers
urn:nbn:se:bth-25030 (URN)
Funder
Swedish Transport Administration
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2024-08-07Bibliographically approved
Rameez, M., Pettersson, M. & Dahl, M. (2022). Interference Compression and Mitigation for Automotive FMCW Radar Systems. IEEE Sensors Journal, 22(20), 19739-19749
Open this publication in new window or tab >>Interference Compression and Mitigation for Automotive FMCW Radar Systems
2022 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 22, no 20, p. 19739-19749Article in journal (Refereed) Published
Abstract [en]

Millimeter-wave (mm-wave) frequency-modu- lated continuous wave (FMCW) radars are increasingly being deployed for scenario perception in various applications. It is expected that the mutual interference between such radars will soon become a significant problem. Therefore, to maintain the reliability of the radar measurements, there must be procedures in place to mitigate this interference. This article proposes a novel interference mitigation technique that utilizes the pulse compression principle for interference compression and mitigation. The interference in the received time-domain signal is compressed using an estimated matched filter. Afterward, the compressed interference is discarded, and the signal is repaired in the pulse-compressed domain using an autoregressive (AR) model. Since the interference spans fewer samples after compression, the signal can be restored more accurately in the compressed domain. Real outdoor measurements show that the interference is effectively suppressed down to the noise floor using the proposed scheme. A signal to interference and noise ratio (SINR) gain of approximately 14 dB was achieved in the experimental data, supporting this study. Moreover, the results indicate that this method is also applicable to situations where multiple interference sources are present.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022
Keywords
Interference, Radar, Chirp, Sensors, Time-domain analysis, Time-frequency analysis, Radar detection, Automotive radar, interference cancellation, millimeter wave (mm-wave) radar, pulse compression, radar signal processing, signal reconstruction
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-23838 (URN)10.1109/JSEN.2022.3204505 (DOI)000870341900066 ()
Note

open access

This work was supported in part by the Netport Science Park and in part by the Municipality of Karlshamn.

Available from: 2022-11-04 Created: 2022-11-04 Last updated: 2023-02-08Bibliographically approved
Rameez, M., Dahl, M. & Pettersson, M. (2021). Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation. IEEE Sensors Journal (5), 6575-6586
Open this publication in new window or tab >>Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation
2021 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, no 5, p. 6575-6586Article in journal (Refereed) Published
Abstract [en]

Automotive radars have become an important part of sensing systems in vehicles and other traffic applications due to their accuracy, compact design, and robustness under severe light and weather conditions. The increased use of radars in various traffic applications has given rise to the problem of mutual interference, which needs to be mitigated. In this paper, we investigate interference mitigation in chirp sequence (CS) automotive radars via signal reconstruction based on autoregressive (AR) models in fast-and slow-time. The interference is mitigated by replacing the disturbed baseband signal samples with samples predicted using the estimated AR models. Measurements from 77 GHz frequency modulated continuous wave (FMCW) static and moving radars are used to evaluate the signal reconstruction performance in terms of the signal-to-interference-plus-noise ratio (SINR), peak side-lobe level (PSLL), and mean squared error (MSE). The results show that the interference is suppressed down to the general noise floor, leading to an improvement in the SINR. Additionally, enhanced side-lobe suppression is achieved via AR signal reconstruction, which is compared to a commonly used inverse-cosine method. Furthermore, the paper notes that the slow-time signal reconstruction can be more beneficial for interference suppression in certain scenarios. CCBY

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Automotive radar, Autoregressive (AR) modelling, Chirp Sequence (CS), Frequency Modulated Continuous Wave (FMCW), interference mitigation, signal reconstruction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Identifiers
urn:nbn:se:bth-19375 (URN)10.1109/JSEN.2020.3042061 (DOI)000616329300107 ()2-s2.0-85097401154 (Scopus ID)
Note

open access

Available from: 2020-04-07 Created: 2020-04-07 Last updated: 2023-02-08Bibliographically approved
Fredriksson, H., Dahl, M., Lövström, B., Holmgren, J. & Lennerstad, H. (2021). Modeling of road traffic flows in the neighboring regions. In: Shakshuki E., Yasar A. (Ed.), Procedia Computer Science: . Paper presented at The 12th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN), Leuven, Belgium, November 1-4, 2021 (pp. 43-50). Elsevier
Open this publication in new window or tab >>Modeling of road traffic flows in the neighboring regions
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2021 (English)In: Procedia Computer Science / [ed] Shakshuki E., Yasar A., Elsevier, 2021, p. 43-50Conference paper, Published paper (Refereed)
Abstract [en]

Traffic flows play a very important role in transportation engineering. In particular, link flows are a source of information about the traffic state, which is usually available from the authorities that manage road networks. Link flows are commonly used in both short-term and long-term planning models for operation and maintenance, and to forecast the future needs of transportation infrastructure. In this paper, we propose a model to study how traffic flow in one location can be expected to reflect the traffic flow in a nearby region. The statistical basis of the model is derived from link flows to find estimates of the distribution of traffic flows in junctions. The model is evaluated in a numerical study, which uses real link flow data from a transportation network in southern Sweden. The results indicate that the model may be useful for studying how large departing flows from a node reflect the link flows in a neighboring geographic region. 

Place, publisher, year, edition, pages
Elsevier, 2021
Series
Procedia Computer Science, E-ISSN 1877-0509 ; 198
Keywords
link flows, traffic volumes, flow distribution, flow estimation, transportation network
National Category
Transport Systems and Logistics
Research subject
Mathematics and applications
Identifiers
urn:nbn:se:bth-22071 (URN)10.1016/j.procs.2021.12.209 (DOI)2-s2.0-85124595881 (Scopus ID)
Conference
The 12th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN), Leuven, Belgium, November 1-4, 2021
Note

open access

Available from: 2021-09-01 Created: 2021-09-01 Last updated: 2024-09-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3707-2780

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