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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.