Biblio

A new approach to micro-Doppler signal analysis is presented in this article. Novel chirp rate estimators in the time-frequency domain were used for this purpose, which provided the chirp rate of micro-Doppler signatures, allowing the classification of objects in the urban environment. As an example verifying the method, a signal from a high-resolution radar with a linear frequency modulated continuous wave (FMCW) recording an echo reflected from a pedestrian was used to validate the proposed algorithms for chirp rate estimation. The obtained results are plotted on saturated accelerograms, giving an additional parameter dedicated for target classification in security systems utilizing radar sensors for target detection.

The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.