Endre søk
Link to record
Permanent link

Direct link
BETA
Rameez, Muhammad
Publikasjoner (2 av 2) Visa alla publikasjoner
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.
Åpne denne publikasjonen i ny fane eller vindu >>Adaptive digital beamforming for interference suppression in automotive FMCW radars
2018 (engelsk)Inngår i: 2018 IEEE Radar Conference, (RadarConf 2018), Institute of Electrical and Electronics Engineers Inc. , 2018, s. 252-256Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc., 2018
Serie
IEEE Radar Conference
Emneord
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
HSV kategori
Identifikatorer
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)
Konferanse
2018 IEEE Radar Conference,Oklahoma City
Tilgjengelig fra: 2018-08-20 Laget: 2018-08-20 Sist oppdatert: 2018-09-20bibliografisk kontrollert
Javadi, M. S., Rameez, M., Dahl, M. & Pettersson, M. (2018). Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features. In: Procedia Computer Science: . Paper presented at 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2018), Belgrade (pp. 1344-1350). Elsevier, 126
Åpne denne publikasjonen i ny fane eller vindu >>Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features
2018 (engelsk)Inngår i: Procedia Computer Science, Elsevier, 2018, Vol. 126, s. 7s. 1344-1350Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Vehicle classification has a significant use in traffic surveillance and management. There are many methods proposed to accomplish this task using variety of sensorS. In this paper, a method based on fuzzy c-means (FCM) clustering is introduced that uses dimensions and speed features of each vehicle. This method exploits the distinction in dimensions features and traffic regulations for each class of vehicles by using multiple FCM clusterings and initializing the partition matrices of the respective classifierS. The experimental results demonstrate that the proposed approach is successful in clustering vehicles from different classes with similar appearanceS. In addition, it is fast and efficient for big data analysiS.

sted, utgiver, år, opplag, sider
Elsevier, 2018. s. 7
Serie
Procedia Computer Science, ISSN 1877-0509
Emneord
Vehicle classification, Fuzzy c-means clustering, Intelligent transportation systems, Pattern recognition
HSV kategori
Identifikatorer
urn:nbn:se:bth-17165 (URN)10.1016/j.procS.2018.08.085 (DOI)
Konferanse
22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2018), Belgrade
Merknad

open access

Tilgjengelig fra: 2018-10-23 Laget: 2018-10-23 Sist oppdatert: 2018-11-29bibliografisk kontrollert
Organisasjoner