Automotive radars, along with optical sensors such as cameras or lidars, offer a reliable way of obtaining the 3-D information about the environment. Of particular interest in autonomous driving (AD) is the reliable detection of particularly vulnerable road users (VRUs). Modern radar sensors are able to detect, distinguish, and track targets with high resolution. Relying on that, a backscattering model of complex traffic targets can be generated from the reflected signals of their scattering points (SPs). These models can be employed in the radar channel simulations for verification methods of advanced driver assistance systems. Therefore, in this work, different persons as the most vital VRUs are measured with high radial and high angular resolution. The necessary signal processing steps are explained in detail for the determination of the relevant SPs. Thus, the corresponding radar cross section (RCS) values can be assigned to certain body regions. In addition to real persons, further measurements are compared with a dummy of the corresponding size. Based on the measurement results, not only accurate models of road users can be derived, but also the measurement results can be employed for calculating wave propagation in traffic scenarios. From the measured SPs, the classification of the persons by size and stature is derived.
High-accuracy, short-range distance measurement is required in a variety of industrial applications e.g., positioning of robots in a fully automated production process, level measurement of liquids in small containers. An FMCW radar sensor is suitable for this purpose, since many of these applications involve harsh environments. Due to the progress in the field of semiconductor technology, FMCW radar sensors operating in different millimeter-wave frequency bands are available today. An important question in this context, which has not been investigated so far is how does a millimeter-wave frequency band influence the sensor accuracy, when thousands of distance measurements are performed with a sensor. This topic has been dealt with for the first time in this paper. The method used for analyzing the FMCW radar signal combines a frequency- and phase-estimation algorithm. The frequency-estimation algorithm based on the fast Fourier transform and the chirp-z transform provides a coarse estimate of the target distance. Subsequently, the phase-estimation algorithm based on a cross-correlation function provides a fine estimate of the target distance. The novel aspects of this paper are as follows. First, the estimation theory concept of Cramér-Rao lower bound (CRLB) has been used to compare the accuracy of two millimeter-wave FMCW radars operating at 60 GHz and 122 GHz. In this comparison, the measurement parameters (e.g., bandwidth, signal-to-noise ratio) as well as the signal-processing algorithm used for both the radars are the same, thus ensuring an unbiased comparison of the FMCW radars, solely based on the choice of millimeter-wave frequency band. Second, the improvement in distance measurement accuracy obtained after each step of the combined frequency- and phase-estimation algorithm has been experimentally demonstrated for both the radars. A total of 5100 short-range distance measurements are made using the 60 GHz and 122 GHz FMCW radar. The measurement results are analyzed at various stages of the frequency- and phase-estimation algorithm and the measurement error is calculated using a nanometer-precision linear motor. At every stage, the mean error values measured with the 60 GHz and 122 GHz FMCW radars are compared. The final accuracy achieved using both radars is of the order of a few micrometers. The measured standard deviation values of the 60 GHz and 122 GHz FMCW radar have been compared against the CRLB. As predicted by the CRLB, this paper experimentally validates for the first time that the 122 GHz FMCW radar provides a higher repeatability of micrometer-accuracy distance measurements than the 60 GHz FMCW radar.
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