Dual-Mode Radar Sensor for Indoor Environment Mapping

Lee, Seongwook; Kwon, Song-Yi; Kim, Bong-Jun; Lim, Hae-Seung; Lee, Jae-Eun

doi:10.3390/s21072469

Cited by 14 publications

(5 citation statements)

References 28 publications

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“…In scientific literature, the radar was used for terrestrial mapping [10,11], or for UAS obstacle detection and avoidance [12,13], but no one used the same radar for both tasks. This is the main achievement of this work.…”

Section: Discussionmentioning

confidence: 99%

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Geo-Referenced Mapping through an Anti-Collision Radar Aboard an Unmanned Aerial System

Miccinesi

Bigazzi

Consumi

et al. 2022

Drones

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Unmanned aerial systems (UASs) have enormous potential in many fields of application, especially when used in combination with autonomous guidance. An open challenge for safe autonomous flight is to rely on a mapping system for local positioning and obstacle avoidance. In this article, the authors propose a radar-based mapping system both for obstacle detection and for path planning. The radar equipment used is a single-chip device originally developed for automotive applications that has good resolution in azimuth, but poor resolution in elevation. This limitation can be critical for UAS application, and it must be considered for obstacle-avoidance maneuvers and for autonomous path-planning selection. However, the radar-mapping system proposed in this paper was successfully tested in the following different scenarios: a single metallic target in grass, a vegetated scenario, and in the close proximity of a ruined building.

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Section: Discussionmentioning

confidence: 99%

“…In [11], S. Lee et al illustrated the mapping results of an indoor environment obtained with a dual-mode radar. The sensor was a multiple-input multiple-output (MIMO) system working at a central frequency of 62 GHz.…”

Section: Introductionmentioning

confidence: 99%

Geo-Referenced Mapping through an Anti-Collision Radar Aboard an Unmanned Aerial System

Miccinesi

Bigazzi

Consumi

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

“…In this paper, we conducted indoor environment mapping using a dual-mode radar sensor that offers both long-range and short-range detection capabilities using a single radar sensor [18]. Figure 5 shows the time-frequency slope in each frame of the dual-mode radar system.…”

Section: Radar Sensor Used In Measurementsmentioning

confidence: 99%

Instantaneous Extraction of Indoor Environment from Radar Sensor-Based Mapping

Cho,

Kwak,

Lee

2024

Remote Sensing

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In this paper, we propose a method for extracting the structure of an indoor environment using radar. When using the radar in an indoor environment, ghost targets are observed through the multipath propagation of radio waves. The presence of these ghost targets obstructs accurate mapping in the indoor environment, consequently hindering the extraction of the indoor environment. Therefore, we propose a deep learning-based method that uses image-to-image translation to extract the structure of the indoor environment by removing ghost targets from the indoor environment map. In this paper, the proposed method employs a conditional generative adversarial network (CGAN), which includes a U-Net-based generator and a patch-generative adversarial network-based discriminator. By repeating the process of determining whether the structure of the generated indoor environment is real or fake, CGAN ultimately returns a structure similar to the real environment. First, we generate a map of the indoor environment using radar, which includes ghost targets. Next, the structure of the indoor environment is extracted from the map using the proposed method. Then, we compare the proposed method, which is based on the structural similarity index and structural content, with the k-nearest neighbors algorithm, Hough transform, and density-based spatial clustering of applications with noise-based environment extraction method. When comparing the methods, our proposed method offers the advantage of extracting a more accurate environment without requiring parameter adjustments, even when the environment is changed.

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“…The angle of the object can be estimated from the phase difference of the signals incident on the horizontally arranged antenna element [23]. In this study, we use a delay-and-sum beamforming method [24] to estimate the angle information of multiple targets [25].…”

Section: Distance Velocity and Angle Estimation In Fmcw Radar Systemmentioning

confidence: 99%

Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

Kim

Jeong

Lee

2021

Sensors

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In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

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Dual-Mode Radar Sensor for Indoor Environment Mapping

Cited by 14 publications

References 28 publications

Geo-Referenced Mapping through an Anti-Collision Radar Aboard an Unmanned Aerial System

Geo-Referenced Mapping through an Anti-Collision Radar Aboard an Unmanned Aerial System

Instantaneous Extraction of Indoor Environment from Radar Sensor-Based Mapping

Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

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