Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz

Geary, Kevin; Colburn, J.S.; Bekaryan, Arthur; Zeng, Shuqing; Litkouhi, Bakhtiar; Murad, Mohannad

doi:10.1109/radar.2013.6586128

Cited by 21 publications

(17 citation statements)

References 6 publications

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“…Moreover, the RCS maximum values vary between 8.7 -20.5 dBm 2 for the front of the cars, 14.4 -24.6 dBm 2 for the back and 19 -22 dBm 2 for the side. These values are in accordance with the results presented in [7]- [8]. We note that the simulated RCS maximum value for a typical vehicle, presented in Fig.…”

Section: B Measurements In Gymnasiumsupporting

confidence: 92%

“…The platform used to hold large objects like vehicles is available in very few research laboratories in the world. Only a limited set of reports on RCS simulation and measurements of vehicles and pedestrians in the W band have been reported to date [7]- [9]. The main focus of this work is to investigate car backscattering behavior in millimeter wave bands through simulation and measurements.…”

Section: Introductionmentioning

confidence: 99%

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RCS modeling and measurements for automotive radar applications in the W band

Kamel

Peden

Pajusco

2017

2017 11th European Conference on Antennas and Propagation (EUCAP)

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This paper describes a reliable methodology for radar cross section (RCS) measurement of complex small and large targets in the W band. The backscattering behavior of a small car model was measured in an anechoic chamber along with various automotive related targets in a wide gymnasium. Experimental performance in the anechoic chamber is compared to the simulation results. Our simulation model is based on deterministic scattering centers, determined by high frequency approaches, like the physical optics (PO) and the physical theory of diffraction (PTD). Nevertheless, simulations of realistic large objects are both time consuming and difficult to implement. The proposed measurement configuration enables the extraction of non-predetermined scattering points for large object modeling which will significantly decrease the simulation time for road scenarios in radar applications.

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Section: B Measurements In Gymnasiumsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

RCS modeling and measurements for automotive radar applications in the W band

Kamel

Peden

Pajusco

2017

2017 11th European Conference on Antennas and Propagation (EUCAP)

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“…An SVM is a popular and simple machine learning algorithm, and it is a bisection method that determines the best classifier, which divides the given data into two difference groups. Thus, SVM is broadly used for target classification in radar signal processing [24].…”

Section: Proposed Human Indication Schemementioning

confidence: 99%

Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Hyun

Jin

2020

Sensors

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In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.

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“…Undoubtedly, it is more difficult to detect drones due to their small dimensions relative to other moving objects, for example cars. Typical maximum radar cross section (RCS) values for consumer cars vary around average values of 18 dBsm at 23-27 GHz and 25 dBsm at 76-81 GHz [2], [3]. On the other hand, typical RCS value for drones are from −15 to −20 dBsm in the X-band [4] and smaller than −20 dBsm at 30-37 GHz [5].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we study quasi-3D RCS signatures of a diverse range of drone models over the frequency band of 26-40 GHz, which may provide essential material for the drone database. 3 With this database, researchers who lack the expertise, equipment or facilities to perform these RCS measurements themselves can develop new techniques to detect drones. For example, Machine Learning researchers can use this data to test the effectiveness of the many tools available in their field of 3 The measurement data is available as a supplementary material.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

et al. 2020

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In this work, we present quasi-monostatic Radar Cross Section measurements of different Unmanned Aerial Vehicles at 26-40 GHz. We study the Radar Cross Section signatures of nine different multi-rotor platforms as well as a single Lithium-ion Polymer battery. These results are useful in the design and testing of radar systems which employ millimeter-wave frequencies for superior drone detection. The data shows how radio waves are scattered by drones of various sizes and what impact the primary construction material has on the received Radar Cross Section signatures. Matching our intuition, the measurements confirm that larger drones made of carbon fiber are easier to detect, whereas drones made from plastic and styrofoam materials are less visible to the radar systems. The measurement results are published as an open database, creating an invaluable reference for engineers working on drone detection. INDEX TERMS Drone detection, millimeter-wave, radar cross section, unmanned aerial vehicle.

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Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz

Cited by 21 publications

References 6 publications

RCS modeling and measurements for automotive radar applications in the W band

RCS modeling and measurements for automotive radar applications in the W band

Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

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