60 GHz synthetic aperture radar for short-range imaging: Theory and experiments

Mamandipoor, Babak; Malysa, Greg; Arbabian, Amin; Madhow, Upamanyu; Noujeim, Karam

doi:10.1109/acssc.2014.7094506

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…There have been active research in recent years on applying Frequency Modulated Continuous Wave (FMCW) Millimeter Wave (mmWave) radar sensing in many application scenarios, for example, person/gesture identification [5], [6], car detection/imaging [1] and environment sensing [2], [3] in low visibility environment. To achieve high resolution radar imaging, usually mmWave radar systems need to work at close distance to objects or they rely on SAR process [7]- [10].…”

Section: Related Workmentioning

confidence: 99%

3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning

Sun¹,

Huang²,

Zhang³

et al. 2021

Preprint

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mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, high noise, and multi-path induced shadow reflections and artifacts. In this paper we propose 3D Reconstruction and Imaging via mmWave Radar (3DRIMR), a deep learning based architecture that reconstructs 3D shape of an object in dense detailed point cloud format, based on sparse raw mmWave radar intensity data. The architecture consists of two back-toback conditional GAN deep neural networks: the first generator network generates 2D depth images based on raw radar intensity data, and the second generator network outputs 3D point clouds based on the results of the first generator. The architecture exploits both convolutional neural network's convolutional operation (that extracts local structure neighborhood information) and the efficiency and detailed geometry capture capability of point clouds (other than costly voxelization of 3D space or distance fields). Our experiments have demonstrated 3DRIMR's effectiveness in reconstructing 3D objects, and its performance improvement over standard techniques.

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Section: Related Workmentioning

confidence: 99%

3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning

Sun¹,

Huang²,

Zhang³

et al. 2021

Preprint

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show abstract

“…Frequency Modulated Continuous Wave (FMCW) Millimeter Wave (mmWave) radar sensing has been an active research area in recent years, especially in applications such as person/gesture identification [5], [6], car detection/imaging [1], and environment sensing [2], [3]. Usually Synthetic Aperture Radar (SAR) is used in data collection for high resolution, e.g., [7]- [10].…”

Section: Related Workmentioning

confidence: 99%

DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar

Sun,

Zhang,

Huang

et al. 2021

Preprint

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Recent research has shown that mmWave radar sensing is effective for object detection in low visibility environments, which makes it an ideal technique in autonomous navigation systems such as autonomous vehicles. However, due to the characteristics of radar signals such as sparsity, low resolution, specularity, and high noise, it is still quite challenging to reconstruct 3D object shapes via mmWave radar sensing. Built on our recent proposed 3DRIMR (3D Reconstruction and Imaging via mmWave Radar), we introduce in this paper DeepPoint, a deep learning model that generates 3D objects in point cloud format that significantly outperforms the original 3DRIMR design. The model adopts a conditional Generative Adversarial Network (GAN) based deep neural network architecture. It takes as input the 2D depth images of an object generated by 3DRIMR's Stage 1, and outputs smooth and dense 3D point clouds of the object. The model consists of a novel generator network that utilizes a sequence of DeepPoint blocks or layers to extract essential features of the union of multiple rough and sparse input point clouds of an object when observed from various viewpoints, given that those input point clouds may contain many incorrect points due to the imperfect generation process of 3DRIMR's Stage 1. The design of DeepPoint adopts a deep structure to capture the global features of input point clouds, and it relies on an optimally chosen number of DeepPoint blocks and skip connections to achieve performance improvement over the original 3DRIMR design. Our experiments have demonstrated that this model significantly outperforms the original 3DRIMR and other standard techniques in reconstructing 3D objects.

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“…Prior work in this thread can be broadly divided into three categories based on the class of (3) single-chip mmWave radar. Multistatic or bistatic surveillance radar is one of the most well-known technologies in the radar community and has been widely used in military for aircraft tracking [7,42,48]. These are long range, high-power with custom designed signal processing chains.…”

Section: Related Workmentioning

confidence: 99%

milliEgo

Saputra

Zhao

et al. 2020

Proceedings of the 18th Conference on Embedded Networked Sensor Systems

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Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by optical techniques e.g., visual-inertial odometry, these suffer from challenges with scene illumination or featureless surfaces. As an alternative, we propose milliEgo, a novel deep-learning approach to robust egomotion estimation which exploits the capabilities of lowcost mmWave radar. Although mmWave radar has a fundamental advantage over monocular cameras of being metric i.e., providing absolute scale or depth, current single chip solutions have limited and sparse imaging resolution, making existing point-cloud registration techniques brittle. We propose a new architecture that is optimized for solving this challenging pose transformation problem. Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g. inertial or visual sensors we introduce a mixed attention approach to deep fusion. Through extensive experiments, we demonstrate our proposed system is able to achieve 1.3% 3D error drift and generalizes well to unseen environments. We also show that the neural architecture can be made highly efficient and suitable for real-time embedded applications. CCS CONCEPTS• Computer systems organization → Robotic autonomy; • Computing methodologies → Robotic planning.

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60 GHz synthetic aperture radar for short-range imaging: Theory and experiments

Cited by 23 publications

References 8 publications

3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning

3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning

DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar

milliEgo

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