Automotive Radar Multipath Propagation in Uncertain Environments

Kamann, Alexander; Held, Patrick; Perras, Florian; Zaumseil, Patrick; Brandmeier, Thomas; Schwarz, Ulrich

doi:10.1109/itsc.2018.8570016

Cited by 39 publications

(13 citation statements)

References 11 publications

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“…The described relations for measured distances and azimuth angles are experimentally validated in [11]. It is also shown that these three kinds of clutter can occur in any combination in practice.…”

Section: Multipath Propagation Via Specular Reflectionmentioning

confidence: 89%

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Fast Rule-Based Clutter Detection in Automotive Radar Data

Kopp¹,

Kellner²,

Piroli³

et al. 2021

Preprint

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Automotive radar sensors output a lot of unwanted clutter or ghost detections, whose position and velocity do not correspond to any real object in the sensor's field of view. This poses a substantial challenge for environment perception methods like object detection or tracking. Especially problematic are clutter detections that occur in groups or at similar locations in multiple consecutive measurements. In this paper, a new algorithm for identifying such erroneous detections is presented. It is mainly based on the modeling of specific commonly occurring wave propagation paths that lead to clutter. In particular, the three effects explicitly covered are reflections at the underbody of a car or truck, signals traveling back and forth between the vehicle on which the sensor is mounted and another object, and multipath propagation via specular reflection. The latter often occurs near guardrails, concrete walls or similar reflective surfaces. Each of these effects is described both theoretically and regarding a method for identifying the corresponding clutter detections. Identification is done by analyzing detections generated from a single sensor measurement only. The final algorithm is evaluated on recordings of real extra-urban traffic. For labeling, a semiautomatic process is employed. The results are promising, both in terms of performance and regarding the very low execution time. Typically, a large part of clutter is found, while only a small ratio of detections corresponding to real objects are falsely classified by the algorithm.

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Section: Multipath Propagation Via Specular Reflectionmentioning

confidence: 89%

“…Once candidates O and R have been chosen, the distance that would be measured for a clutter detection resulting from this propagation path is calculated via (7) or (11), depending on the investigated case. The determined value must be close to d dut .…”

Section: Identification Of Clutter Caused By Specular Reflectionmentioning

confidence: 99%

Fast Rule-Based Clutter Detection in Automotive Radar Data

Kopp¹,

Kellner²,

Piroli³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Each sensor has an estimated error on the datasheet [10]- [11]; however, there are few techniques to estimate how these uncertainties change due to intrinsic and extrinsic noise, and therefore to estimate the overall uncertainty on the measured value. • False positives and false negatives [12]. False positive is a wrong sensor output that indicates the presence of a target while it actually does not exist.…”

Section: Avs and Sensing Challengesmentioning

confidence: 99%

A Review and Perspective on Optical Phased Array for Automotive LiDAR

Hsu

Solano-Rivas

et al. 2021

IEEE J. Select. Topics Quantum Electron.

181

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“…In this way, anomalies resulting from measurement ambiguities are reduced. However, this does not affect the anomalies caused by multi-path propagation effects, which are, e.g., analyzed in [2], [8]. The authors of [2] additionally propose a model-based detection algorithm for the anomalies.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Radar Data Using PointNets

Griebel¹,

Authaler²,

Horn³

et al. 2021

Preprint

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For autonomous driving, radar is an important sensor type. On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment. On the other hand, in literature, radar sensors are known for their robustness against several kinds of adverse weather conditions. However, on the downside, radar is susceptible to ghost targets or clutter which can be caused by several different causes, e.g., reflective surfaces in the environment. Ghost targets, for instance, can result in erroneous object detections. To this end, it is desirable to identify anomalous targets as early as possible in radar data. In this work, we present an approach based on PointNets to detect anomalous radar targets. Modifying the PointNet-architecture driven by our task, we developed a novel grouping variant which contributes to a multi-form grouping module. Our method is evaluated on a real-world dataset in urban scenarios and shows promising results for the detection of anomalous radar targets.

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Automotive Radar Multipath Propagation in Uncertain Environments

Cited by 39 publications

References 11 publications

Fast Rule-Based Clutter Detection in Automotive Radar Data

Fast Rule-Based Clutter Detection in Automotive Radar Data

A Review and Perspective on Optical Phased Array for Automotive LiDAR

Anomaly Detection in Radar Data Using PointNets

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