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Zur Erfassung von Personen und Objekten im Umfeld von Kraftfahrzeugen werden unter anderem Radarsensoren eingesetzt. Um die Sensoranforderungen für automatisierte und autonome Fahraufgaben zu erfüllen, werden an einzelnen Fahrzeugen mehrere Radare benötigt. Eine starke Verbreitung der Radare führt zur Entstehung von Interferenzen, die die Sensorleistung einschränken. In dieser Arbeit werden Gegenmaßnahmen beim Auftreten von Interferenzen beschrieben und bewertet, um eine Funktionalität der Sensoren zu gewährleisten.

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. Afterwards, the application of DBF on a MIMO (multiple-input multiple-output) 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 a time-multiplexing MIMO radar., acceptedVersion

In an automotive environment the presence of reflecting surfaces cannot be avoided. The electromagnetic wave returning from a target vehicle can get reflected on those surfaces causing a non existing so-called ghost target. For driver assistance systems ghost targets can lead to false decisions and, therefore, they should be detected and avoided. In this paper a model for describing those ghost targets and a procedure to distinguish them from real targets using the orientation and the motion state of a vehicle is presented., acceptedVersion

Radar sensors offer enormous advantages as sensing devices for automated and autonomous driving. However, when multiple of such sensors are operated in a large number of cars, there is a high risk of the occurrence of mutual interference. In the currently widespread linearly frequency modulated sensors interference reduces the detection performance, especially for targets with a low radar cross section. In this paper the interference effects are suppressed with an adaptive beamforming scheme based on a mean square error minimization. The paper evaluates the algorithm with the help of a simulated and measured scenario, and discusses the occurring interference effects and the benefits of the beamforming approach., acceptedVersion