Navigation-Aided Automotive SAR for High-Resolution Imaging of Driving Environments

Tagliaferri, Dario; Rizzi, Marco; Nicoli, Monica; Tebaldini, Stefano; Russo, Ivan; Guarnieri, Andrea Monti; Prati, C.; Spagnolini, Umberto

doi:10.1109/access.2021.3062084

Cited by 39 publications

(15 citation statements)

References 45 publications

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“…The inverse problem (32) is solved for the detected GCPs leading to the residual velocities in Table II, reported with the theoretical accuracy. First of all, the estimated residual velocities are within the confidence bound of the employed navigation sensors [15]. As expected, the error is higher in the direction of motion: in a forward looking geometry, all the GCPs are distributed in front of the car, thus higher accuracy is expected in this direction.…”

Section: Sar Image Formationsupporting

confidence: 70%

“…Moreover, car navigation systems must deal with unpredictable driver's maneuverings, leading to a velocity error that could be as high as 10 − 20 cm/s [29]. Although the fusion of inexpensive heterogeneous in-car sensors data was demonstrated to provide accurate imaging in moderate dynamics [15], a reliable SAR imaging for autonomous driving applications calls for the integration of navigation and radar data.…”

Section: Motion Error Analysismentioning

confidence: 99%

“…The on-board navigation equipment comprises: (i) two internal 3 Degrees of Freedom (DoF) IMUs, measuring lateral and longitudinal acceleration, along with heading rate; (ii) an occupant restraint controller, consisting in a 3 DoF IMU placed in the rear part of the car, measuring longitudinal and lateral acceleration as well as heading rate, the purpose is the airbag activation during a crash; (iii) four wheel encoders, measuring the odometric velocity of each wheel; (iv) a steering angle sensor at the frontal wheels; (v) an on-radar IMU+GNSS sensor [28]. The sensors' data are fused with an Unscented Kalman Filter (UKF) approach described in our previous work [15].…”

Section: Sar Image Formationmentioning

confidence: 99%

“…Early works on automotive SAR [12], [13] propose simple accelerometer-and/or gyroscope-based Motion Compensation (MoCo), other [14] consider the usage of odometric wheel speed, for approximately linear trajectories. Our previous work [15] demonstrated a good SAR imaging quality in urban scenarios, employing an ad-hoc fusion of multiple sensors such as Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), odometer and steering angle. However, these former works highlighted the need of a proper residual motion correction in arbitrary dynamic conditions, where automotive-grade navigation solutions are not accurate enough.…”

Section: Introductionmentioning

confidence: 99%

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Motion Estimation and Compensation in Automotive MIMO SAR

Manzoni¹,

Tagliaferri²,

Rizzi³

et al. 2022

Preprint

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With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.

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Section: Sar Image Formationsupporting

confidence: 70%

Section: Motion Error Analysismentioning

confidence: 99%

Section: Sar Image Formationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Motion Estimation and Compensation in Automotive MIMO SAR

Manzoni¹,

Tagliaferri²,

Rizzi³

et al. 2022

Preprint

Self Cite

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“…In this regard, automotive-legacy multiple-input multiple-output (MIMO) radars working in the 76-81 GHz band (W-band) [2] are widely employed to obtain measures of radial distance, velocity and angular position of remote targets at short, medium or long range (up to 250 meters), but are characterized by a poor trade-off between hardware cost, angular resolution (typically > 1 deg), bandwidth (typically around 600 MHz for medium-long range radars) and field of view (FoV) [3], [4]. Although radar-based simultaneous localization and mapping works are present in literature [5], synthetic aperture radar (SAR) techniques were increasingly used since the last 10 years to augment the accuracy of environmental perception [6]- [10]. The aim is to perform a radio imaging of both static and non-static targets (pedestrians, vehicles, buildings, etc.)…”

Section: Introductionmentioning

confidence: 99%

Multi-Beam Automotive SAR Imaging in Urban Scenarios

Rizzi¹,

Manzoni²,

Tebaldini³

et al. 2021

Preprint

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Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving. Compared to lidars and cameras, automotive-legacy radars can work in any weather condition and without an external source of illumination, but are limited in either range or angular resolution. SARs offer a relevant increase in angular resolution, provided that the ego-motion of the radar platform is known along the synthetic aperture. In this paper, we present the results of an experimental campaign aimed at assessing the potential of a multi-beam SAR imaging in an urban scenario, composed of various targets (buildings, cars, pedestrian, etc.), employing a 77 GHz multiple-input multiple-output (MIMO) radar platform based on a mass-market available automotivegrade technology. The results highlight a centimeter-level accuracy of the SAR images in realistic driving conditions, showing the possibility to use a multi-angle focusing approach to detect and discriminate between different targets based on their angular scattering response.

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