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  • 1.
    Bechter, Jonathan
    et al.
    Ulm Univ, DEU.
    Rameez, Muhammad
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Waldschmidt, Christian
    Ulm Univ, DEU.
    Analytical and Experimental Investigations on Mitigation of Interference in a DBF MIMO Radar2017In: IEEE transactions on microwave theory and techniques, ISSN 0018-9480, E-ISSN 1557-9670, Vol. 65, no 5, p. 1727-1734Article in journal (Refereed)
    Abstract [en]

    As driver assistance systems and autonomous driving are on the rise, radar sensors become a common device for automobiles. The high sensor density leads to the occurrence of interference, which decreases the detection capabilities. Here, digital beamforming (DBF) is applied to mitigate such interference. A DBF system requires a calibration of the different receiving channels. It is shown how this calibration completely changes the DBF beam pattern required to cancel interferences, if the system has no IQ receiver. Afterward, the application of DBF on a multiple-input multiple-output (MIMO) radar is investigated. It is shown that only the real aperture and not the virtual one can be used for interference suppression, leading to wide notches in the pattern. However, for any target the large virtual aperture can be exploited, even if interferers are blinded out. Moreover, the wide notches for interference suppression of the real aperture appear narrow in the virtual aperture for target localization. The results are verified by measurements with time-multiplexing MIMO radar.

  • 2.
    Javadi, Mohammad Saleh
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Rameez, Muhammad
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Dahl, Mattias
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features2018In: Procedia Computer Science, Elsevier, 2018, Vol. 126, p. 7p. 1344-1350Conference paper (Refereed)
    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.

  • 3.
    Rameez, Muhammad
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Dahl, Mattias
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Adaptive digital beamforming for interference suppression in automotive FMCW radars2018In: 2018 IEEE Radar Conference, (RadarConf 2018), Institute of Electrical and Electronics Engineers Inc. , 2018, p. 252-256Conference 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.

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