scholarly journals Enhanced DOA Estimation Using Linearly Predicted Array Expansion for Automotive Radar Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47714-47727 ◽  
Author(s):  
Heonkyo Sim ◽  
Seongwook Lee ◽  
Seokhyun Kang ◽  
Seong-Cheol Kim
Keyword(s):  
Doa Estimation ◽  
Automotive Radar ◽  
Radar Systems

scholarly journals Logarithmic-Domain Array Interpolation for Improved Direction of Arrival Estimation in Automotive Radars

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2410 ◽  
Author(s):  
Seongwook Lee ◽  
Seong-Cheol Kim
Keyword(s):  
Measurement Data ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Transformation Matrix ◽  
High Angular Resolution ◽  
Automotive Radar ◽  
Actual Measurement ◽  
Estimation Performance ◽  
Radar Systems ◽  
Original Array

In automotive radar systems, a limited number of antenna elements are used to estimate the angle of the target. Therefore, array interpolation techniques can be used for direction of arrival (DOA) estimation to achieve high angular resolution. In general, to generate interpolated array elements from original array elements, the method of linear least squares (LLS) is used. When the LLS method is used, the amplitudes of the interpolated array elements may not be equivalent to those of the original array elements. In addition, through the transformation matrix obtained from the LLS method, the phases of the interpolated array elements are not precisely generated. Therefore, we propose an array transformation matrix that generates accurate phases for interpolated array elements to improve DOA estimation performance, while maintaining constant amplitudes of the array elements. Moreover, to enhance the effect of our interpolation method, a power calibration method for interpolated received signals is also proposed. Through the simulation, we confirm that the array interpolation accuracy and DOA estimation performance of the proposed method are improved compared to those of the conventional method. Moreover, the performance and effectiveness of our proposed method are also verified using data obtained from the commercial radar system. Because the proposed method exhibits better performance when applied to actual measurement data, it can be utilized in commercial automotive radar systems.

scholarly journals Lens-based 77 GHZ MIMO radar for angular estimation in multitarget environments

2014 ◽  
Vol 6 (3-4) ◽  
pp. 397-404 ◽  
Author(s):  
Steffen Lutz ◽  
Thomas Walter ◽  
Robert Weigel
Keyword(s):  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Separation Performance ◽  
Patch Antennas ◽  
Waveform Generator ◽  
Automotive Radar ◽  
Sensor Performance ◽  
Radar Systems ◽  
77 Ghz

The demanding tasks for automotive radar systems in multitarget scenarios require an increased target separation performance and new sensor concepts. In this contribution, a highly integrated 77 GHz time domain multiplex (TDM) MIMO radar is presented. The sensor is feasible for advanced direction of arrival (DOA) estimation in azimuth and elevation. For efficient and high-quality measurements a fractional-n phased locked loop (PLL) with integrated waveform generator, enabling chirp and frequency modulated continous waveform (FMCW) modulations, is implemented. Spatial beamforming is done with series feed array patch antennas in combination with a dielectric cylindrical lens. For the improvement of the direction of arrival (DOA) estimation performance a new lens-based MIMO radar approach is introduced. Therefore the classical MIMO approach is combined with the advantages of an optical beamforming concept. Due to the usage of these techniques the sensor performance in accuracy, ambiguity suppression, and angular resolution can be significantly increased.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

Pedestrian Classification for 79 GHz Automotive Radar Systems

2018 ◽  
Author(s):  
Robert Prophet ◽  
Marcel Hoffmann ◽  
Alicja Ossowska ◽  
Waqas Malik ◽  
Christian Sturm ◽  
...  
Keyword(s):  
Automotive Radar ◽  
Radar Systems

scholarly journals From Antenna Design to High Fidelity, Full Physics Automotive Radar Sensor Corner Case Simulation

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ushemadzoro Chipengo ◽  
Peter M. Krenz ◽  
Shawn Carpenter
Keyword(s):  
Target Identification ◽  
Pedestrian Detection ◽  
High Fidelity ◽  
Driver Assistance ◽  
Automotive Radar ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Radar Systems ◽  
False Target ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) have recently been thrust into the spotlight in the automotive industry as carmakers and technology companies pursue effective active safety systems and fully autonomous vehicles. Various sensors such as lidar (light detection and ranging), radar (radio detection and ranging), ultrasonic, and optical cameras are employed to provide situational awareness to vehicles in a highly dynamic environment. Radar has emerged as a primary sensor technology for both active/passive safety and comfort-advanced driver-assistance systems. Physically building and testing radar systems to demonstrate reliability is an expensive and time-consuming process. Simulation emerges as the most practical solution to designing and testing radar systems. This paper provides a complete, full physics simulation workflow for automotive radar using finite element method and asymptotic ray tracing electromagnetic solvers. The design and optimization of both transmitter and receiver antennas is presented. Antenna interaction with vehicle bumper and fascia is also investigated. A full physics-based radar scene corner case is modelled to obtain high-fidelity range-Doppler maps. Finally, this paper investigates the effects of inclined roads on late pedestrian detection and the effects of construction metal plate radar returns on false target identification. Possible solutions are suggested and validated. Results from this study show how pedestrian radar returns can be increased by over 16 dB for early detection along with a 27 dB reduction in road construction plate radar returns to suppress false target identification. Both solutions to the above corner cases can potentially save pedestrian lives and prevent future accidents.

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