Motion Estimation and Compensation in Automotive MIMO SAR

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

doi:10.1109/tits.2022.3219542

Cited by 29 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reasons for accurate SAR processing based on the estimated trajectory is the piecewise steadiness of the position error. A sharp SAR image of the environment can be generated for each of the six sub-apertures even though they have an error of 1.2 mm according to VI-C. Due to this ego-motion based error, the targets have small angular errors of less than one degree [18], [60]. Ultimately, to enable coherent addition of the six sub-apertures it must be ensured that a pixel can be found that compensates for the phase errors due to the positional inaccuracies caused by its erroneous pixel position in the grid.…”

Section: E Impact Of Localization Inaccuracies On the Sar Imagementioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) for Synthetic Aperture Radar (SAR) Processing in the Field of Autonomous Driving

Grebner,

Riekenbrauck,

Waldschmidt

2024

Trans. Rad. Sys.

View full text Add to dashboard Cite

Section: E Impact Of Localization Inaccuracies On the Sar Imagementioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) for Synthetic Aperture Radar (SAR) Processing in the Field of Autonomous Driving

Grebner,

Riekenbrauck,

Waldschmidt

2024

Trans. Rad. Sys.

View full text Add to dashboard Cite

“…where (i) P is the number of paths, (ii) ξ p ∼ CN 0, σ 2 p is the scattering amplitude of p-th path, such that P p=1 σ 2 p = 1, (iii) τ p is the propagation delay of the p-th path, (iv) a L (θ, ϕ) ∈ C L×1 is the steering vector of either Tx or Rx, function of Tx and Rx angles, respectively (φ t p , θ t p ) and (φ r p , θ r p ), and normalized such that a L (θ, ϕ) 2 F = L. As common for communication systems operating through back-scattering, the two-way channel to/from the STMM is characterized by a single dominant path, as for radar systems [29]. Multiple bounces can be considered, but their path loss is orders of magnitude higher, and thus are here neglected, considering P = 1 for both forward H i (t) and backward H o (t) channels.…”

Section: B Channel Modelmentioning

confidence: 99%

“…Theorem 1 implies that any phase-encoded modulation signal γ(t) leads to a strict inequality for (29), except for very low MU ← SU signal bandwidth, fulfilling (24), or the case in which the impinging wave is perfectly orthogonal to the STMM plane, i.e., θ = π/2. To evaluate the space-time phase coupling effect, let us consider a generic non-linear…”

Section: B Space-time Phase Couplingmentioning

confidence: 99%

See 1 more Smart Citation

Conformal Metasurfaces: A Novel Solution for Vehicular Communications

Mizmizi

Ayoubi

Tagliaferri

et al. 2023

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Space-time modulated metasurfaces (STMMs) are a newly investigated technology for next 6G generation wireless communication networks. An STMM augments the spatial phase function with a time-varying one across the elements, allowing for the conveyance of information that possibly modulates the impinging signal. Hence, STMM represents an evolution of reconfigurable intelligent surfaces (RIS), which only design the spatial phase pattern. STMMs convey signals without a relevant increase in the energy budget, which is convenient for applications where energy is a strong constraint. This paper proposes a mathematical model for STMM-based wireless communication, that creates the basics for two potential STMM architectures. One has excellent design flexibility, whereas the other is more costeffective. The model describes STMM's distinguishing features, such as space-time coupling, and their impact on system performance. The proposed STMM model addresses the design criteria of a full-duplex system architecture, in which the temporal signal originating at the STMM generates a modulation overlapped with the incident one. The presented numerical results demonstrate the efficacy of the proposed model and its potential to revolutionize wireless communication.

show abstract

“…However, a significant amount of research on imaging radar has been conducted over the past several years, including [ 16 , 17 , 18 , 19 ], which has resulted in coherent images with a centimeter-scale resolution. Detailed research on motion estimation and compensation methods for vehicle multiple-input–multiple-output synthetic aperture radar (MIMO SAR) systems was presented by Manzoni et al [ 16 ]. The authors discuss the difficulties caused by the natural motion of the vehicle, which might result in visual abnormalities and distortions.…”

Section: Introductionmentioning

confidence: 99%

Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

Ogunrinde

Bernadin

2023

Sensors

View full text Add to dashboard Cite

AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persists. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image onto the camera image. Using the attention mechanism, we emphasized and improved the important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our results show that the proposed method significantly enhances the detection of small and distant objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed, with an accuracy of 0.849 at 69 fps.

show abstract

Motion Estimation and Compensation in Automotive MIMO SAR

Cited by 29 publications

References 34 publications

Simultaneous Localization and Mapping (SLAM) for Synthetic Aperture Radar (SAR) Processing in the Field of Autonomous Driving

Simultaneous Localization and Mapping (SLAM) for Synthetic Aperture Radar (SAR) Processing in the Field of Autonomous Driving

Conformal Metasurfaces: A Novel Solution for Vehicular Communications

Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

Contact Info

Product

Resources

About