The demanding tasks for automotive radar systems in multitarget scenarios require an increased target separation performance and new sensor concepts. In this contribution, a highly integrated 77 GHz time domain multiplex (TDM) MIMO radar is presented. The sensor is feasible for advanced direction of arrival (DOA) estimation in azimuth and elevation. For efficient and high-quality measurements a fractional-n phased locked loop (PLL) with integrated waveform generator, enabling chirp and frequency modulated continous waveform (FMCW) modulations, is implemented. Spatial beamforming is done with series feed array patch antennas in combination with a dielectric cylindrical lens. For the improvement of the direction of arrival (DOA) estimation performance a new lens-based MIMO radar approach is introduced. Therefore the classical MIMO approach is combined with the advantages of an optical beamforming concept. Due to the usage of these techniques the sensor performance in accuracy, ambiguity suppression, and angular resolution can be significantly increased.

This paper presents the technical realization of the multiple-input multiple-output (MIMO) radar MIRA-CLE Ka (MIMO radar-configurable in Ka-band). This system is a stationary imaging radar without any mechanical moving parts. It is highly portable, ready to use in only a couple of minutes, and it is able to process one radar image per second during continuous radar measurements in the current stage of development. In addition to the image processing, it is possible to detect changes in range of 0.1 mm in the illuminated scene. The MIMO system operates in Ka band and consists of 16 receive and 16 transmit elements, so that 256 virtual elements are generated during signal processing. The size of the antenna's frontend is about 80 cm in width. Owing to its compact size, flexibility, and realtime capability, the system offers a wide field of applications.

In this paper, an approach to differentiate between various dielectric threat objects in security applications is investigated. The scattering information in form of the Sinclair matrix of relevant scenarios is gained from a fully polarimetric, synthetic aperture radar. Both monostatic and multistatic array configurations are examined. A possible polarimetric calibration procedure is presented. The radar data are processed with the H-α decomposition algorithm. The H-α scattering characteristics of threat objects are analyzed in terms of a weighted averaging. It is shown that an object classification is possible even for threat objects conceiled under thick layers of clothing. Measurement results are presented to illustrate the topic.

A new approach is proposed to extract the slow-time feature of human motion in high-resolution radars. The approach is based on the self-similarity matrix (SSM) of the radar signals. The Mutual Information is used as a measure of similarity. The SSMs of different radar signals (high-resolution range profile, micro-Doppler, and range-Doppler video sequence) are compared, and the angel-invariant property of the SSMs is demonstrated. The SSM for different activities (i.e. walking and running) is extracted from range-Doppler video sequence and analyzed. Finally, simulation result is validated by experimental data.

The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars. We consider all steps of processing required to make a decision out of the raw radar data. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Then, classification features are extracted from the micro-Doppler signature in order to represent information about class at a lower dimension space. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with X-band radar. Planes, quadrocopter, helicopters, and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides the capability of correct classification with a probability of around 92%.