Robust Range-Doppler Registration with HD Maps

Pishehvari, Ahmad; Stefer, Markus; Tibken, Betnd

doi:10.1109/iccre49379.2020.9096265

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…As for the invertibility of U , from (28) we see that this merely requires having at least two reflections in distinct angles. Using these derivatives, the expression in (26) for the velocity estimation error covariance can be more concretely expressed as…”

Section: B Velocity Estimation Error Covariancementioning

confidence: 97%

Performance Analysis of Automotive SAR With Radar Based Motion Estimation

Bialer¹,

Tirer²

2022

Preprint

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Automotive synthetic aperture radar (SAR) can achieve a significant angular resolution enhancement for detecting static objects, which is essential for automated driving. Obtaining high resolution SAR images requires precise ego vehicle velocity estimation. A small velocity estimation error can result in a focused SAR image with objects at offset angles. In this paper, we consider an automotive SAR system that produces SAR images of static objects based on ego vehicle velocity estimation from the radar return signal without the overhead in complexity and cost of using an auxiliary global navigation satellite system (GNSS) and inertial measurement unit (IMU). We derive a novel analytical approximation for the automotive SAR angle estimation error variance when the velocity is estimated by the radar. The developed analytical analysis closely predicts the true SAR angle estimation variance, and also provides insights on the effects of the radar parameters and the environment condition on the automotive SAR angle estimation error. We evaluate via the analytical analysis and simulation tests the radar settings and environment condition in which the automotive SAR attains a significant performance gain over the angular resolution of the short aperture physical antenna array. We show that, perhaps surprisingly, when the velocity is estimated by the radar the performance advantage of automotive SAR is realized only in limited conditions. Hence since its implementation comes with an increase in computation and system complexity as well as an increase in the detection delay it should be used carefully and selectively.

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Section: B Velocity Estimation Error Covariancementioning

confidence: 97%

Performance Analysis of Automotive SAR With Radar Based Motion Estimation

Bialer¹,

Tirer²

2022

Preprint

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“…As mentioned earlier, one benefit of FMCW MIMO radar is access to accurate estimate of target's radial Doppler velocity and azimuth angle within a frame [5]. This in turn enables radar odometry since the sensor's velocity can be estimated by analyzing the relationship between the radial Doppler velocities and azimuth angles of all static targets in the field of view [15], [16].…”

Section: Ego-motion Estimation With Radar Odometrymentioning

confidence: 99%

“…9) equal to the sensor's speed with opposite heading. Given that Doppler radar only measures the radial velocity component (green line) of target, we can reconstruct the sensor's velocity components along x-axis and y-axis (v x , v y ) by analyzing the velocity profile of at least two stationary targets [15], [16].…”

Section: Ego-motion Estimation With Radar Odometrymentioning

confidence: 99%

“…The traditional solution is to use on-board inertial measurement units (IMU) [14] that combines measurements from the wheel speed sensor, gyroscopes, and accelerometers. However, high-precision IMUs are cost-prohibitive for automotive applications, inspiring the need for self-contained alternatives such as radar odometry, to determine the velocity and direction of motion of the vehicular radar [15], [16], [17]. Our approach is based on analyzing the distribution of the radial velocities of the received reflections (targets) over their azimuth angles, which can be provided by the MIMO processing stage described above.…”

Section: Introductionmentioning

confidence: 99%

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MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving

Gao

Roy

Xing

2021

Preprint

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Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driverassistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for sidelooking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform (see Fig. 1).

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