Comparison of Different Approaches for Identification of Radar Ghost Detections in Automotive Scenarios

Jin, Yi; Prophet, Robert; Deligiannis, Anastasios; Weber, Ingo; Fuentes-Michel, Juan-Carlos; Vossiek, Martin

doi:10.1109/radarconf2147009.2021.9454980

Cited by 14 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more general work is e.g. [15]. The authors compare a random forest, a convolutional neural network (CNN) and PointNet++ for the detection of arbitrary clutter.…”

Section: Related Workmentioning

confidence: 99%

Fast Rule-Based Clutter Detection in Automotive Radar Data

Kopp

Kellner

Piroli

et al. 2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles. One of the first steps during the processing of radar point clouds is often the detection of clutter, i.e. erroneous points that do not correspond to real objects. Another common objective is the semantic segmentation of moving road users. These two problems are handled strictly separate from each other in literature. The employed neural networks are always focused entirely on only one of the tasks. In contrast to this, we examine ways to solve both tasks at the same time with a single jointly used model. In addition to a new augmented multi-head architecture, we also devise a method to represent a network's predictions for the two tasks with only one output value. This novel approach allows us to solve the tasks simultaneously with the same inference time as a conventional task-specific model. In an extensive evaluation, we show that our setup is highly effective and outperforms every existing network for semantic segmentation on the RadarScenes dataset [1].

show abstract

“…A more general work is e.g. [15]. The authors compare a random forest, a convolutional neural network (CNN) and PointNet++ for the detection of arbitrary clutter.…”

Section: Related Workmentioning

confidence: 99%

Fast Rule-Based Clutter Detection in Automotive Radar Data

Kopp

Kellner

Piroli

et al. 2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

show abstract

“…Such artifacts within the radar image, which can also be caused by multipath or interference, are a relevant safety issue in autonomous driving. Therefore, on the one hand, the identification and reduction of these ghost targets is subject of current research [32], [33]. On the other hand, there is a need to detect occurring errors in the calibration, which can be caused by aging, temperature change, and deformation, to initiate re-calibration of the sensor [34].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Antenna Array Calibration Errors on MIMO and Multi-Channel Synthetic Aperture Radar Imaging

Geiß¹,

Sippel²,

Braunwarth³

et al. 2023

IEEE J. Microw.

Self Cite

View full text Add to dashboard Cite

In radar imaging, any lateral resolution is either attained using antenna arrays or by synthetic aperture radar (SAR) principles. Recently, the combination of both approaches to a multi-channel SAR (MC-SAR) has gained increased attention. However, antenna arrays are impaired by unknown calibration errors, particularly phase errors, amplification deviations, mutual coupling, and phase center position deviation of the antenna element. Since, first, established calibration procedures are often non-ideal and, second, the calibration parameters usually vary over time due to aging and changes in temperature, calibration errors distort the measurements during the radar operation. Therefore, current research focuses on finding new practical calibration procedures. However, hardly any research has been conducted to analyze the basic effects which erroneous calibration parameters have on the reconstructed image in MCSAR or MIMO radar. In this work, we analyze the effects of multiple calibration error types on MCSAR and MIMO radar imaging. Both measurement principles lead to periodic error patterns, resulting in deterministic distortions. The derived relations can be used to precisely predict the position and number of ghost targets caused by calibration errors for known measurement setups and targets. For this, no information about the actual calibration errors is required. Furthermore, worst-case distortion levels are provided for different bounded errors allowing rough estimates of the impact of different error types on the image's distortion level. The findings are validated via measurements using a 76 GHz FMCW MIMO radar.INDEX TERMS Antenna array, automotive radar, calibration errors, ghost targets, radar, SAR.

show abstract

“…Furthermore, ghost targets caused by multi-path or clutter are also a common source of false object detection in the automotive domain. Current machine learning approaches are normally based on annotated detection point clouds, as shown in [14], [15], and [16].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Image Enhancement for Automotive Radar Trained With an Advanced Virtual Sensor

Christian¹,

Hoffmann²,

Ullmann³

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper introduces a novel deep learning based concept for image enhancement and distortion suppression in automotive radar signal processing. The deep neural network (DNN) is trained solely on virtual data that is generated by an automotive MIMO radar ray tracing simulator. The simulator mimics the raw data that would be provided by a specific automotive MIMO radar for which the signal processing is envisaged. This virtual radar sensor, which has the same properties as the real radar, creates the DNN training input data. The unique feature of our approach is that an advanced virtual radar sensor is used to create the target values for the DNN training. The advanced virtual sensor is a simulated radar that works on the same basic principle as the real radar and its resembling virtual sensor, but is significantly upgraded in terms of performance. In our experiments the advanced virtual sensor, e.g., has a notably larger array, i.e. a much better lateral resolution than the real radar. In addition, our advanced virtual sensor is not affected by multi-path effects, clutter, noise, beamforming sidelobes and other typical automotive radar distortions. The paper shows that a DNN trained on this advanced virtual sensor training data can deliver outstanding automotive radar signal processing results not only on simulated data but also on field data. The presented real-world automotive radar measurements show that multi-path, clutter and noise are efficiently suppressed by this DNN and that it has a remarkable ability to sharpen images and suppress sidelobes. The results presented in the paper suggest that the novel advanced DNN training concept based on virtual sensors offers opportunities that also go far beyond the radar signal processing capabilities illustrated. Hence, the concept put forward here is an attractive option for placing DNN-based radar signal processing on a novel and easy-to-implement foundation.

show abstract

Comparison of Different Approaches for Identification of Radar Ghost Detections in Automotive Scenarios

Cited by 14 publications

References 8 publications

Fast Rule-Based Clutter Detection in Automotive Radar Data

Fast Rule-Based Clutter Detection in Automotive Radar Data

The Impact of Antenna Array Calibration Errors on MIMO and Multi-Channel Synthetic Aperture Radar Imaging

Deep Learning Based Image Enhancement for Automotive Radar Trained With an Advanced Virtual Sensor

Contact Info

Product

Resources

About