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In this paper, a coherent multistatic radar network with a novel system architecture is presented, which circumvents the general problems of clock distribution and phase noise-related signal-to-noise ratio (SNR) issues. The proposed network consists of a variable number of multiple-input multiple-output (MIMO) radar sensors and a variable number of repeater tags, all of which operate incoherently on the hardware level. In a minimum configuration, the network only consists of one MIMO radar sensor and a repeater tag. The theory behind such a multistatic network is mathematically derived, and simulations are presented to show key aspects of the network, i.e., multistatic range and Doppler measurements, as well as high-resolution angle estimation, exploiting a very large virtual aperture spanning the whole network. Measurements with one sensor and one repeater tag at 77 GHz are carried out to verify the simulations. The measurements show that the bistatic path between the sensor and the repeater tag retains coherency.

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The angular resolution of a radar system can be enhanced with an increasing antenna aperture. Instead of using more antenna elements, the distances in the aperture can be increased with a sparse array. To mitigate the high side lobes originating from the sparse array, the missing antenna elements can be reconstructed by means of compressed sensing. In this paper a sparse antenna array with a low side lobe level is determined with a genetic algorithm and a cost function. An investigation is performed what difference in the radar cross section of two targets in the same range-Doppler cell can be achieved. Additionally, instead of considering point targets only, a target vehicle is measured with a 77 GHz MIMO radar.

Future automotive radars will be able to achieve much higher range and angular resolution compared to currently used radar sensors. This enables functionalities like vehicle contour estimation to be used in advanced driver assistance systems, thus heavily increasing their performance. In this paper, the application of an adaptive algorithm on basis of k-nearestneighbours examination for clustering radar data as precursor to estimation of width, length, and position of vehicles is presented and compared to a more basic algorithm. The influence of the parameters of this KNN-DBSCAN algorithm and its performance dependency on the used MIMO radar system is discussed.

As high resolution automotive radars become more common, so does their usage for next-generation functionalities like advanced driver assistant systems and autonomous driving. This creates the need for robust clustering techniques to distinguish among multiple extended objects like vehicles in the same scenario. One especially challenging scenario is that of separating two extended targets close to each other, each following its own trajectory. This paper proposes a clustering algorithm based on the analysis of the velocity profile to divide target points of multiple vehicles into sub-clusters. The theoretical background is explained and shown on simulation data. The algorithm is verified using radar measurements of two extended vehicular targets.

Radar is an essential element of state of the art advanced driver assistance systems. In the foreseeable future, radar will be an indispensable sensor for the use in affordable, automated driven cars. Simulation tools are the key for an efficient development process and hence will lower the price of sophisticated driver assistance systems. Therefore, the development of adequate simulators is important for suppliers, car makers, and final consumers. This paper introduces the concept of such a simulator for multi-user automotive radar scenarios and presents selected simulation results for a use case of radar interference.

Future high-resolution radars enable new functionalities in advanced driver assistance systems, relying on fast and reliable extraction of properties of vehicles on the road. A critical property for the prediction of trajectories and the assessment of potentially dangerous situations is that of the actual motion-the velocity vector and yaw rate-of observed objects. In this paper, an approach to distinguish linear from non-linear motions as well as estimating the yaw rate using only a single radar sensor is presented and evaluated via measurements.