The Experts below are selected from a list of 5724 Experts worldwide ranked by ideXlab platform
Hermann Rohling - One of the best experts on this subject based on the ideXlab platform.
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Pedestrian recognition in Automotive Radar sensors
2013Co-Authors: Steffen Heuel, Hermann RohlingAbstract:Automotive Radar sensors in the 24 GHz frequency domain measure target range and Doppler frequency simultaneously even in multiple target situations. The all weather capability of Radar sensors is an important argument for their application in modern cars and in driver assistant systems. The measurement accuracy and target resolution depends on the applied waveform. Therefore the Multi Frequency Shift Keying (MFSK) and the Chirp Sequence waveform will be considered and compared in this paper. The classical Radar sensor measures target range, radial velocity and azimuth angle. Additionally the Radar sensor should be able to distinguish between different objects like pedestrians and cars in Automotive applications. In this case the target recognition quality depends mainly on the signal-to-noise ratio (SNR), the signal features and their measurement accuracy, respectively. In this paper the quality of the target recognition process will be quantitatively measured and compared for two different Radar waveforms.
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pedestrian classification in Automotive Radar systems
International Radar Symposium, 2012Co-Authors: Steffen Heuel, Hermann RohlingAbstract:Urban traffic is dangerous for all car drivers and especially for pedestrians. There are about 5000 fatalities on German streets every year, which is absolutely too much. Human beings have strong limitations in the ability to measure precisely the distance and the speed difference between cars, which is the reason for several accidents. Therefore, the European Union has called all car manufactureres to intensify their research activities in protecting vulnerable road users. An Automotive Radar sensor measures the target range and radial velocity very accurately and with high resolution even in multiple target situations and there is tremendous progress in 24 GHz Radar sensor development. The objective of this paper is to protect vulnerable road users by a target recognition scheme in any urban scenario. One main objective is the difference between lateral moving vehicles and pedestrians in terms of feature extraction and classification. Therefore, a pedestrian detection procedure is additionally integrated into the 24 GHz Automotive Radar sensor.
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Lateral velocity estimation for Automotive Radar applications
IET International Conference on Radar Systems 2007, 2007Co-Authors: Hermann Rohling, Florian Folster, Henning RitterAbstract:Automotive Radar sensors are applied to measure the target range, azimuth angle and radial velocity simultaneously even in multiple target situations. But it is also possible to calculate the lateral velocity by a single Radar measurement. Based on some analytical and experimental results it is shown in this paper that the lateral velocity of a Radar target can also be measured precisely even in a single observation situation. This additional information of lateral speed is of much interest in all target tracking procedures, especially for typical city traffic situations and for advanced driver assistance systems (ADAS). (4 pages)
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Lateral velocity estimation based on Automotive Radar sensors
2006 CIE International Conference on Radar, 2006Co-Authors: Florian Folster, Hermann RohlingAbstract:Automotive Radar sensors are applied to measure the target range, azimuth angle and radial velocity simultaneously even in multiple target situations. The single target measured data are necessary for target tracking in advanced driver assistance systems (ADAS) e.g. in highway scenarios. In typical city traffic situations the Radar measurement is also important but additionally even the lateral velocity component of each detected target such as a vehicle is of large interest in this case. It is shown in this paper that the lateral velocity of an extended target can be measured even in a mono observation situation. For an Automotive Radar sensor a high spectral resolution is required in this case which means the time on target should be sufficiently large.
Steffen Heuel - One of the best experts on this subject based on the ideXlab platform.
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A novel approach to measure the Automotive Radar sensor’s robustness against interferers in the lab with realistic scenarios
2018 19th International Radar Symposium (IRS), 2018Co-Authors: Alois Ascher, Rainer. Lenz, Steffen HeuelAbstract:Advanced Driver Assistance Systems (ADAS) based on Automotive Radar sensors are safety relevant systems, which drivers have to rely on. As the number of Radar sensors increases in daily traffic, the probability of a Radar sensor being interfered by the transmit signal of other Radar sensors also increases. Now, the received echo signals of the surrounding objects are interfered by all transmit Radar signals of other Radar sensors. In order to verify proper echo signal detection also in a highly populated electro-magnetic spectrum, testing Radar sensors in the lab against various interfering signals becomes a must. This paper introduces a novel approach to test the robustness of Automotive Radar sensors’ in the lab with realistic scenarios. The measurement setup as well as the testing procedure are explained. Exemplary measurements clarify the importance of testing the robustness of Automotive Radar sensors against interferers.
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Automotive Radar interference test
2017 18th International Radar Symposium (IRS), 2017Co-Authors: Steffen HeuelAbstract:Fully automated vehicles are currently under research and development and will become reality in the near future. Key enabling sensors in this area are Automotive Radars, which currently support driving comfort and crash prevention. These Radars operate in the 24 GHz and 76–81 GHz bands as of today and occupy the spectrum heavily. Hence Automotive Radar sensors require immunity to interference of some other Automotive Radar sensors. This paper presents methods to verify interference mitigation techniques using very wide band arbitrary RF signals in high frequency mmWave domain. It shows measurements that the noise floor increases in between 10 dB to 25 dB depending on the interference signal level and waveform. Without interference cancellation objects with low Radar cross sections, like pedestrians, would most likely go undetected.
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Pedestrian recognition in Automotive Radar sensors
2013Co-Authors: Steffen Heuel, Hermann RohlingAbstract:Automotive Radar sensors in the 24 GHz frequency domain measure target range and Doppler frequency simultaneously even in multiple target situations. The all weather capability of Radar sensors is an important argument for their application in modern cars and in driver assistant systems. The measurement accuracy and target resolution depends on the applied waveform. Therefore the Multi Frequency Shift Keying (MFSK) and the Chirp Sequence waveform will be considered and compared in this paper. The classical Radar sensor measures target range, radial velocity and azimuth angle. Additionally the Radar sensor should be able to distinguish between different objects like pedestrians and cars in Automotive applications. In this case the target recognition quality depends mainly on the signal-to-noise ratio (SNR), the signal features and their measurement accuracy, respectively. In this paper the quality of the target recognition process will be quantitatively measured and compared for two different Radar waveforms.
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pedestrian classification in Automotive Radar systems
International Radar Symposium, 2012Co-Authors: Steffen Heuel, Hermann RohlingAbstract:Urban traffic is dangerous for all car drivers and especially for pedestrians. There are about 5000 fatalities on German streets every year, which is absolutely too much. Human beings have strong limitations in the ability to measure precisely the distance and the speed difference between cars, which is the reason for several accidents. Therefore, the European Union has called all car manufactureres to intensify their research activities in protecting vulnerable road users. An Automotive Radar sensor measures the target range and radial velocity very accurately and with high resolution even in multiple target situations and there is tremendous progress in 24 GHz Radar sensor development. The objective of this paper is to protect vulnerable road users by a target recognition scheme in any urban scenario. One main objective is the difference between lateral moving vehicles and pedestrians in terms of feature extraction and classification. Therefore, a pedestrian detection procedure is additionally integrated into the 24 GHz Automotive Radar sensor.
Bin Yang - One of the best experts on this subject based on the ideXlab platform.
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High-Performance Automotive Radar: A review of signal processing algorithms and modulation schemes
IEEE Signal Processing Magazine, 2019Co-Authors: Gor Hakobyan, Bin YangAbstract:The ongoing automation of driving functions in cars results in the evolution of advanced driver assistance systems (ADAS) into ones capable of highly automated driving, which will in turn progress into fully autonomous, self-driving cars. To work properly, these functions first must be able to perceive the car's surroundings by such means as Radar, lidar, camera, and ultrasound sensors. As the complexity of such systems increases along with the level of automation, the demands on environment sensors, including Radar, grow as well. For Radar performance to meet the requirements of self-driving cars, straightforward scaling of the Radar parameters is not sufficient. To refine Radar capabilities to meet more stringent requirements, fundamentally different approaches may be required, including the use of more sophisticated signal processing algorithms as well as alternative Radar waveforms and modulation schemes. In addition, since Radar is an active sensor (i.e., it operates by transmitting signals and evaluating their reflections) interference becomes a crucial issue as the number of Automotive Radar sensors increases. This article gives an overview of the challenges that arise for Automotive Radar from its development as a sensor for ADAS to a core component of self-driving cars. It summarizes the relevant research and discusses the following topics related to highperformance Automotive Radar systems: 1) shortcomings of the classical signal processing algorithms due to underlying fundamental assumptions and a signal processing framework that overcomes these limitations, 2) use of digital modulations for Automotive Radar, and 3) interference-mitigation methods that enable multiple Radar sensors to coexist in conditions of increasing market penetration. The overview presented in this article shows that new paradigms arise as Automotive Radar transitions into a more powerful vehicular sensor, which provides a fertile research ground for further investigation.
Robert W Heath - One of the best experts on this subject based on the ideXlab platform.
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investigating the ieee 802 11ad standard for millimeter wave Automotive Radar
Vehicular Technology Conference, 2015Co-Authors: Preeti Kumari, Nuria Gonzalezprelcic, Robert W HeathAbstract:Millimeter wave (mmWave) technology is widely used for Automotive Radar applications, like adaptive cruise control and obstacle detection. Unlike conventional Radar waveforms which are usually propriety, this paper explores the use of a consumer wireless local area network (WLAN) waveform in the 60GHz unlicensed mmWave band for Automotive Radar applications. In particular, this paper develops a joint framework of long range Automotive Radar (LRR) and vehicle-to-vehicle communication (V2V) at 60 GHz by exploiting the special data-aided structure (repeated Golay complimentary sequences) of an IEEE 802.11ad single carrier physical layer (SCPHY) frame. This framework leverages the signal processing algorithms used in the typical WLAN receiver for time and frequency synchronization to perform Radar parameter estimation. The initial simulation results show that it is possible to achieve the desired range accuracy of 0.1 m with a very high probability of detection (above 99%) using the preamble of a SCPHY frame. Furthermore, the velocity estimation algorithm achieves the desired accuracy of 0.1 m/s at high SNR using the preamble and pilot words of only a single frame.
Gor Hakobyan - One of the best experts on this subject based on the ideXlab platform.
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Sweep-based Spectrum Sensing Method for Interference-Aware Cognitive Automotive Radar
2020 IEEE Radar Conference (RadarConf20), 2020Co-Authors: Gor Hakobyan, Martin Fink, Ayinhu Soyolyn, Nour Mansour, Dirk DahlhausAbstract:The ongoing automation of driving functions in cars leads to a massive growth in the number of Automotive Radar sensors, and thus to more Radar interference. An approach for actively mitigating interference between Automotive Radars is the interference-aware cognitive Radar (IACR). One major challenge for IACR is, however, sensing of a large spectral band (e.g. 77–81 GHz) potentially available for Radar operation. In this paper, we present a cost-efficient spectrum sensing method based on linear sweeping over a wide spectral band. The signal resulting from the sweep is lowpass filtered prior to sampling, which allows a significant bandwidth reduction down to few tens of MHz. In the digital domain, the captured signal is pulse-compressed with a bank of matched filters. The output image of the time-frequency space provides a basis for identification of interference-free regions and a subsequent adaptation for the next measurement cycle. The performance of the proposed approach is studied in simulation and demonstrated with a prototype. The results indicate the feasibility of such spectrum sensing module for Automotive Radar both in terms of performance and cost.
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High-Performance Automotive Radar: A review of signal processing algorithms and modulation schemes
IEEE Signal Processing Magazine, 2019Co-Authors: Gor Hakobyan, Bin YangAbstract:The ongoing automation of driving functions in cars results in the evolution of advanced driver assistance systems (ADAS) into ones capable of highly automated driving, which will in turn progress into fully autonomous, self-driving cars. To work properly, these functions first must be able to perceive the car's surroundings by such means as Radar, lidar, camera, and ultrasound sensors. As the complexity of such systems increases along with the level of automation, the demands on environment sensors, including Radar, grow as well. For Radar performance to meet the requirements of self-driving cars, straightforward scaling of the Radar parameters is not sufficient. To refine Radar capabilities to meet more stringent requirements, fundamentally different approaches may be required, including the use of more sophisticated signal processing algorithms as well as alternative Radar waveforms and modulation schemes. In addition, since Radar is an active sensor (i.e., it operates by transmitting signals and evaluating their reflections) interference becomes a crucial issue as the number of Automotive Radar sensors increases. This article gives an overview of the challenges that arise for Automotive Radar from its development as a sensor for ADAS to a core component of self-driving cars. It summarizes the relevant research and discusses the following topics related to highperformance Automotive Radar systems: 1) shortcomings of the classical signal processing algorithms due to underlying fundamental assumptions and a signal processing framework that overcomes these limitations, 2) use of digital modulations for Automotive Radar, and 3) interference-mitigation methods that enable multiple Radar sensors to coexist in conditions of increasing market penetration. The overview presented in this article shows that new paradigms arise as Automotive Radar transitions into a more powerful vehicular sensor, which provides a fertile research ground for further investigation.
