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.
In this paper the capabilities of an incoherent radar sensor network for robust Doppler-based gesture recognition are investigated and a significant performance boost is demonstrated. A comprehensive dataset is recorded with an incoherent sensor network consisting of three time-synchronized 77 GHz FMCW radars. Based on this dataset, we show that differential Doppler features obtained from the varying viewing angles result in a significant multistatic gain for classification, particularly for high intra-class variations and low Doppler frequencies. For the most complex dataset crossuser validation accuracy of a CNN with optimized data fusion is improved by 7.4 % to an overall value of 87.1 %, which we regard high as gestures are not designed for distinguishability but reflect everyday control and communication signals.
The motion of a car has a vast impact on the relative measurements of all environmental sensors. Consequently, the precise knowledge of the ego-motion of a sensor equipped car is important for higher level signal processing like mapping or the estimation of the target parameters. This can be achieved by multiple simple radar sensors with a single measurement. The range and the velocity measurements conducted by multiple sensors that are observing a common field of view are jointly processed and filtered for stationary targets. This dataset is used to determine the velocity and the yaw rate of the vehicle in the x-and y-plane. An advantage of the proposed approach is the simplicity of the sensors which do not need the capability of angle estimation in the azimuth plane, while still providing comparable results. In addition, this approach works with a minimum number of two sensors.
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