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Photograph of 77GHz Automobile Millimeter Wave Radar installation The measured radar performance on the road is shown in Figure 10. The 77GHz automotive millimeter-wave radar based on the DBF system can more accurately distinguish vehicles or targets in different lanes ahead. In this figure, the red squares indicate that the target, which relative to the speed of the driving car is less than 0m/s, is mainly the surrounding poles and walls. The black squares indicate that the target, which relative to the speed of the driving car is greater than 0m/s, is mainly vehicles. Since the SNR decreases with distance, the accuracy of the angular measurement of a 77GHz automotive millimeter-wave radar based on the DBF system for a target with a distance of more than 120m is significantly reduced.
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The launch of unmanned models and the introduction of laws and regulations will promote the development of the autopilot industry. The greatest characteristic of millimeter-wave radar is its strong penetration, which can be used in harsh conditions such as rain, snow and heavy fog. It is an indispensable feature for future autopilot. According to t...
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Context 1
... automotive millimeter-wave radar is mainly used to detect forward targets to achieve ACC, AEB and other functions, 77GHz automotive millimeter-wave radar is generally installed in the front of the car as shown in Figure 9. When installing the radar, it is necessary to keep parallel with the ground plane as much as possible, so as to prevent the radar from facing toward the sky or toward the ground, resulting in the inability to work properly. ...
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Citations
Spectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximate this integral. However, POSP requires the phase of the signal to vary rapidly, which is not the case in circular synthetic aperture radar (SAR) and turntable inverse SAR (ISAR). To solve this problem, a new SA method based on time-frequency reversion (TFRSA) is proposed, which utilizes the relationship of the Fourier transform pairs and their corresponding signal phases. In addition, the connection between the imaging geometry and time-frequency relationship is also analyzed and utilized to help solve the time-frequency reversion. When the TFRSA is applied to the linear trajectory SAR, the obtained spectrum expression is the same as the result of POSP. When it is applied to ISAR, the spectrum expressions of near-field and far-field are derived and their difference is found to be position-independent. Based on this finding, an extended polar format algorithm (EPFA) for near-field ISAR imaging is proposed, which can solve the distortion and defocusing problems caused by traditional ISAR imaging algorithms. When it is applied to the circular SAR (CSAR), a new and efficient imaging method based on EPFA is proposed, which can solve the low efficiency problem of conventional BP-based CSAR imaging algorithms. The simulations and real data processing results are provided to validate the effectiveness of proposed method.
Digital beamforming (DBF) arrays with a large number of small antennas are extensively employed in millimeter-wave (mmWave) sensing systems. Sparse arrays have been posed as an attractive solution to mmWave systems due to their capability of striking the best compromise between performance and complexity. We propose two iterative antenna selection strategies, referred to as deterministic selection and adaptive selection, to design sparse DBF arrays in this paper. The first strategy assumes a common sparse array associated with different beamforming weights for multiple switched beams, while the other exploits switching networks to adaptively change the sparse array configuration with different beams. To counteract hardware-related issues that arise in practical realizations, we then propose guided adaptive selection and regularized adaptive selection to impose additional constraints for different switching networks. The optimality of sparse DBF arrays is defined in terms of both transmit and receive patterns. Taking that into account, an iterative re-weighted l1-norm is modified to promote boolean sparsity of the selection vector in this work. Simulation results validate the effectiveness of proposed antenna selection methods.
