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Radar-based gesture recognition can play a vital role in autonomous vehicles’ interaction with vulnerable road users (VRUs). However, in automotive scenarios the same gesture produces strongly differing radar responses owing to the wide range of variations such as position, orientation, or ego-motion. Since including all kinds of modifications in a measured dataset is laborious, gesture simulations alleviate the measurement effort and increase the robustness against edge and corner cases. Hence, this article presents a flexible geometric human target model allowing the direct introduction of a wide range of modifications, while it facilitates the handling of shadowing effects and multiradar constellations. Using the proposed simulation model, a dataset recorded with a radar sensor network consisting of three chirp sequence (CS) radars is resimulated based on the motion data simultaneously captured with a stereo video system. Completely substituting the measured by the simulated data for training, a convolutional neural network (CNN) classifier still achieves 80.4% cross-validation accuracy on a challenging gesture set, compared with 89.4% for training on the measured data. Moreover, using the simulated data the classifier is shown to successfully generalize to new scenarios not observed in measurements. [ABSTRACT FROM AUTHOR]

In order to test and verify radar-based systems for advanced driver assistance systems, radar target simulators offer a test environment for reproducible dynamic automotive scenarios. This article presents a system capable of generating multiple targets with individual ranges and velocities for chirp-sequence frequency modulated continuous waveform radars. The novel approach allows to generate targets without prior knowledge about the radar sensor by estimating the radar’s waveform parameters. The proposed simulator offers high flexibility at low costs by performing the estimation on undersampled data. Finally, an automotive scenario of a spatially extended target is simulated for a radar sensor with the proposed system. [ABSTRACT FROM AUTHOR]

Multicarrier waveforms such as orthogonal frequency-division multiplexing (OFDM) found their way into radar applications in the last few years. However, currently, typically only a fraction of the large baseband bandwidth required to obtain high resolution is available in practice due to hardware limitations. In this paper, we propose a frequency-agile sparse OFDM radar processing that allows the transmission of consecutive bandwidth-reduced OFDM pulses on different carriers, and thereby covering a much larger measurement bandwidth in a measurement frame. Through joint processing of multiple narrowband pulses and compressed sensing methods, high resolution and unambiguity in the joint range-velocity profile are obtained comparable to an equivalent wideband OFDM. It shows that a baseband bandwidth of 20% of the full channel bandwidth is sufficient to reliably obtain the same result as for an equivalent wideband OFDM signal. The proposed processing scheme is validated using simulations and radar measurements at 77 GHz. [ABSTRACT FROM AUTHOR]

Recent publications show that the potential of using orthogonal frequency division multiplexing waveforms as radar signals. Since the range resolution is proportional to the RF bandwidth, the major obstacle that obstructs the practical use in automotive and other low-cost radars is the requirement to sample the received signal at sampling rates that span the whole RF signal bandwidth requiring ADCs with sampling rates in the order of GHz. This paper presents a method to achieve the high range resolution induced by a large RF bandwidth, but with a much lower baseband bandwidth, consequently requiring a much slower ADC while at the same time delivering a velocity profile for each subcarrier. In addition, the processing scheme induces a range migration compensation, independent of the number of targets. This is achieved with barely increased computational effort. The scheme is verified with simulations and measurements at 77 GHz. [ABSTRACT FROM PUBLISHER]

A novel hybrid approach to designing multilayer antennas for automotive radar using a thin single layer printed circuit board and multilayer thin films is presented in this paper. A new substrate integrated waveguide (SIW) slot fed stacked grid antenna element is designed using this approach. The flexibility of this approach in designing antenna arrays is shown by integrating the same antenna element with three different feed networks based on SIW, grounded coplanar waveguide (GCPW), and microstrip (MS) transmission lines. The antenna is designed to operate in the frequency band between 77 and 81 GHz for automotive radar. Measurement results on $1\times 4$ arrays show an impedance matching bandwidth and a realized gain of 7.8% (76.3–82.5 GHz) and 9.2 dBi, respectively, for SIW feed antenna, 11.3% (76.3–85.5 GHz) and 10.7 dBi, respectively, for GCPW feed antenna, and 11.3% (75.7–84.8 GHz) and 12.1 dBi, respectively, for MS feed antennas. The proposed antenna can be used for medium- and short-range automotive radar applications. [ABSTRACT FROM PUBLISHER]

As driver assistance systems and autonomous driving are on the rise, radar sensors become a common device for automobiles. The high sensor density leads to the occurrence of interference, which decreases the detection capabilities. Here, digital beamforming (DBF) is applied to mitigate such interference. A DBF system requires a calibration of the different receiving channels. It is shown how this calibration completely changes the DBF beam pattern required to cancel interferences, if the system has no IQ receiver. Afterward, the application of DBF on a multiple-input multiple-output (MIMO) radar is investigated. It is shown that only the real aperture and not the virtual one can be used for interference suppression, leading to wide notches in the pattern. However, for any target the large virtual aperture can be exploited, even if interferers are blinded out. Moreover, the wide notches for interference suppression of the real aperture appear narrow in the virtual aperture for target localization. The results are verified by measurements with time-multiplexing MIMO radar. [ABSTRACT FROM AUTHOR]

With new generations of high-resolution imaging radars, the orientation of vehicles can be estimated without temporal filtering. This enables time-critical systems to respond even faster. Based on a large data set, this paper compares three generic algorithms for the orientation estimation of a vehicle. An experimental MIMO imaging radar is used to highlight the requirements of a robust algorithm. The well-known orientated bounding box and the so-called L-fit are adapted for radar measurements and compared with a brute-force approach. A quality function selects the best fitted model and is a key factor to minimize alignment errors. Moreover, the reliability of the estimation is evaluated with respect to the aspect angle, the distance to the target, and the number of sensors. An approach to estimate the reliability of the current orientation estimation is introduced. It is shown that the root mean square error of the orientation estimation is 9.77° and 38% smaller compared with the common algorithm. In 50% of the evaluated measurements the orientation estimation error is smaller than 3.73°. [ABSTRACT FROM AUTHOR]

The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown. [ABSTRACT FROM AUTHOR]