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

Kamel, Emna Bel; Peden, Alain; Pajusco, Patrice

doi:10.23919/eucap.2017.7928266

Cited by 34 publications

(14 citation statements)

References 13 publications

(12 reference statements)

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“…The first two objects represent two pedestrians, whereas the last target represents a vehicle. These values are based on the typical velocities of such targets, and the RCS values are obtained from previous studies (Kamel et al, 2017 ; Deep et al, 2020 ). The simulation also includes 20-mm sensor packaging and the 1-cm car bumper, with RCS σ P = −40 dBsm , and σ B = −23 dBsm , respectively.…”

Section: Experiments Resultsmentioning

confidence: 99%

Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

et al. 2021

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The development of advanced autonomous driving applications is hindered by the complex temporal structure of sensory data, as well as by the limited computational and energy resources of their on-board systems. Currently, neuromorphic engineering is a rapidly growing field that aims to design information processing systems similar to the human brain by leveraging novel algorithms based on spiking neural networks (SNNs). These systems are well-suited to recognize temporal patterns in data while maintaining a low energy consumption and offering highly parallel architectures for fast computation. However, the lack of effective algorithms for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem of radar signal processing by introducing a novel SNN that substitutes the discrete Fourier transform and constant false-alarm rate algorithm for raw radar data, where the weights and architecture of the SNN are derived from the original algorithms. We demonstrate that our proposed SNN can achieve competitive results compared to that of the original algorithms in simulated driving scenarios while retaining its spike-based nature.

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Section: Experiments Resultsmentioning

confidence: 99%

Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

et al. 2021

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“…Radar returns originating from pedestrians follow a Nakagami distribution, which is obtained by fitting a distribution over the pedestrian RCS data in [18] along the angle of observation. Due to the limited angular resolution of the ray casting, often only a single point is assigned to a pedestrian.…”

Section: Object Modelingmentioning

confidence: 99%

Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection

Stevens,

Tigrek,

Tammam

et al. 2021

Preprint

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Cognitive radars are systems that rely on learning through interactions of the radar with the surrounding environment. To realize this, radar transmit parameters can be adapted such that they facilitate some downstream task. This paper proposes the use of deep reinforcement learning (RL) to learn policies for gain control under the object detection task. The YOLOv3 single-shot object detector is used for the downstream task and will be concurrently used alongside the RL agent. Furthermore, a synthetic dataset is introduced which models the radar environment with use of the Grand Theft Auto V game engine. This approach allows for simulation of vast amounts of data with flexible assignment of the radar parameters to aid in the active learning process.

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“…the orientation of the object has no influence. Based on KAMEL's [24], MATSUNAMI's [25] and SCHIPPER's [26] 3) Detection Probability: Based on the calculated signalto-noise ratio values, the detection probability p d,i can be determined. For this purpose, approximations such as the Albersheim equation can be used, which additionally takes into account a false alarm rate, for example.…”

Section: Sn R Camera (R) =mentioning

confidence: 99%

Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models

Ponn

Müller

Diermeyer

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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The objective of this paper is to propose a systematic analysis of the sensor coverage of automated vehicles. Due to an unlimited number of possible traffic situations, a selection of scenarios to be tested must be applied in the safety assessment of automated vehicles. This paper describes how phenomenological sensor models can be used to identify system-specific relevant scenarios. In automated driving, the following sensors are predominantly used: camera, ultrasonic, Radar and Lidar. Based on the literature, phenomenological models have been developed for the four sensor types, which take into account phenomena such as environmental influences, sensor properties and the type of object to be detected. These phenomenological models have a significantly higher reliability than simple ideal sensor models and require lower computing costs than realistic physical sensor models, which represents an optimal compromise for systematic investigations of sensor coverage. The simulations showed significant differences between different system configurations and thus support the systemspecific selection of relevant scenarios for the safety assessment of automated vehicles.

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

Cited by 34 publications

References 13 publications

Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection

Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models

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