Extraction of Scattering Centers Using a 77 GHz FMCW Radar

Abadpour, Sevda; Diewald, Axel; Pauli, Mario; Zwick, Thomas

doi:10.23919/gemic.2019.8698144

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…When the radar made a full circle around the car, it actually made a 360° observation of the target. The RCS fluctuation of the target was violent and irregular, as described by the authors of [ 49 , 50 ]. Therefore, it was wrong to use Gaussian kernel in method one when blurring the RCS of the target.…”

Section: Resultsmentioning

confidence: 99%

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Liu

Wang

et al. 2020

Sensors

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Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into “Multi-scan,” the problem of small data volume is solved. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a “Scan to Map” point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. Based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios.

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Section: Resultsmentioning

confidence: 99%

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Liu

Wang

et al. 2020

Sensors

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“…As the RCS profile is computationally expensive to calculate, simplified models have been derived. Based on observations and measurements, so-called "scattering center models" have been proposed [9], [10]. This theory assumes the total reflectivity of complex geometries such as vehicles is composed of individual scatterers.…”

Section: A Related Workmentioning

confidence: 99%

A Dataset for Radar Scattering Characteristics of Vehicles Under Real-World Driving Conditions: Major Findings for Sensor Simulation

2023

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In virtual validation of automated driving, trustworthy simulation models of perception sensors are required. Radar sensors are particularly hard to model, as their measurements are notoriously difficult to interpret. This is due to their complex measurement principle, involving multi-path propagation of mm-waves, varying backscattering characteristics of objects, and further factors such as limited measurement ranges and resolutions that introduce uncertainty to the measurements. This work presents a method for studying the backscatter characteristics of vehicles under real-world driving conditions. A slalom-like driving scenario, which is representative for road-driving where the vehicle is visible under different aspect angles, has been designed. It aims at a high level of reproducibility of the trajectories driven by the vehicles, hence reducing additional sources of uncertainty that were otherwise present in the measurements. In a large-scale measurement campaign, 13 vehicles have been studied. The vehicles-under-test are observed by multiple radars, mounted at different heights, and carry reference sensors for obtaining their positions. In this paper, we present the measurement campaign and show major findings from our measurement results. Our focus lies on drawing conclusions for trustworthy sensor simulation. Both sensor measurement data, and MATLAB code for data analysis are made publicly available alongside this paper.

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“…2). We focus in this work only on vehicles as objects and the corresponding radar cross-section (RCS) values are derived from the work of [26] and [27]. A generic antenna gain G is used for both transmitting and receiving with a maximum gain of 20 dB and a side lobe suppression of -13 dB.…”

Section: Synthetic Data Generationmentioning

confidence: 99%

A Sensitivity Analysis Approach for Evaluating a Radar Simulation for Virtual Testing of Autonomous Driving Functions

Ngo¹,

Bauer²,

Resch³

2020

Preprint

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Simulation-based testing is a promising approach to significantly reduce the validation effort of automated driving functions. Realistic models of environment perception sensors such as camera, radar and lidar play a key role in this testing strategy. A generally accepted method to validate these sensor models does not yet exist. Particularly radar has traditionally been one of the most difficult sensors to model. Although promising as an alternative to real test drives, virtual tests are time-consuming due to the fact that they simulate the entire radar system in detail, using computation-intensive simulation techniques to approximate the propagation of electromagnetic waves. In this paper, we introduce a sensitivity analysis approach for developing and evaluating a radar simulation, with the objective to identify the parameters with the greatest impact regarding the system under test. A modular radar system simulation is presented and parameterized to conduct a sensitivity analysis in order to evaluate a spatial clustering algorithm as the system under test, while comparing the output from the radar model to real driving measurements to ensure a realistic model behavior. The presented approach is evaluated and it is demonstrated that with this approach results from different situations can be traced back to the contribution of the individual sub-modules of the radar simulation.

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Extraction of Scattering Centers Using a 77 GHz FMCW Radar

Cited by 15 publications

References 5 publications

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

A Dataset for Radar Scattering Characteristics of Vehicles Under Real-World Driving Conditions: Major Findings for Sensor Simulation

A Sensitivity Analysis Approach for Evaluating a Radar Simulation for Virtual Testing of Autonomous Driving Functions

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