Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers

Mafukidze, Harry Dzingai; Mishra, Amit Kumar; Pidanič, Jan; Francois, Schonken W. P.

doi:10.1109/access.2022.3211673

Cited by 17 publications

(15 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main information in the payload output by the radar is the point clouds, which contain reflections from the radar, positioning on the

axes, velocity, and signal strength. The term “radar point cloud” universally defines a compilation of detected objects reflected by the radar processing chain [ 40 ]. Originally, the concept of a point cloud emerged to characterize multi-dimensional data points derived from sensors like LIDAR and range cameras [ 40 ].…”

Section: Mmwave Radar Preliminariesmentioning

confidence: 99%

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

Mattos,

Brante,

Moritz

et al. 2024

Sensors

View full text Add to dashboard Cite

Millimeter-wave (mmWave) radars attain high resolution without compromising privacy while being unaffected by environmental factors such as rain, dust, and fog. This study explores the challenges of using mmWave radars for the simultaneous detection of people and small animals, a critical concern in applications like indoor wireless energy transfer systems. This work proposes innovative methodologies for enhancing detection accuracy and overcoming the inherent difficulties posed by differences in target size and volume. In particular, we explore two distinct positioning scenarios that involve up to four mmWave radars in an indoor environment to detect and track both humans and small animals. We compare the outcomes achieved through the implementation of three distinct data-fusion methods. It was shown that using a single radar without the application of a tracking algorithm resulted in a sensitivity of 46.1%. However, this sensitivity significantly increased to 97.10% upon utilizing four radars using with the optimal fusion method and tracking. This improvement highlights the effectiveness of employing multiple radars together with data fusion techniques, significantly enhancing sensitivity and reliability in target detection.

show abstract

“…The main information in the payload output by the radar is the point clouds, which contain reflections from the radar, positioning on the

Section: Mmwave Radar Preliminariesmentioning

confidence: 99%

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

Mattos,

Brante,

Moritz

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Fast Fourier transformations were performed on the IF signals to estimate the range, velocity, and angle of arrival (AoA) of the moving target [14,37]. A cloud point is a 3D model composed of a set of points used in the literature to describe a list of detected targets provided by radar signal processing [38]. Figure 3 illustrates a four-step signal preprocessing workflow to generate a series of cloud points, each comprising different features, such as the 3D spatial position (x, y, and z), velocity, and AoA [39].…”

Section: Signal Preprocessingmentioning

confidence: 99%

“…where X t s is the target reflection and g t is the white Gaussian noise in a certain frame t. From the received signal, the CFAR algorithm [42] is applied to detect the presence or absence of the target. A fixed threshold value is used in traditional detectors such as the Neyman-Pearson detector [38]. The assumption is that interference (noise or clutter) is spread similarly across the test range bins such that, if the signal in the test bin exceeds a specific threshold γ, the bin contains a target.…”

Section: Cfar Algorithmmentioning

confidence: 99%

“…The detector calculates the noise level inside a sliding window and uses this estimate to assess the presence or absence of a target in the test bin. If a target is found in a bin, the algorithm returns the target range-Doppler localization [38]. Finally, all CFAR-identified targets are organized into groups based on their positions in a 3D matrix.…”

Section: Cfar Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network

Solaiman,

Alsuwat,

Alharthi

2023

ASI

View full text Add to dashboard Cite

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively.

show abstract

“…Radar can detect distant objects and determining their position, velocity, or other characteristics by analysis of very high frequency electromagnetic waves (generally 2–18 GHz) reflected from their surfaces. Therefore, it is the most important equipment in information acquisition and precision guidance and is widely used in navigation, reconnaissance, fire control, and seeker missiles [ [1] , [2] , [3] ]. In order not to be detected by radar, radar jamming technology is gradually developed.…”

Section: Introductionmentioning

confidence: 99%

An Innovative model of magnetically intercalated expanded graphite for calculating radar attenuation performance at 2–18 GHz

Zhang

Tang

Chen

et al. 2023

Heliyon

View full text Add to dashboard Cite

Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers

Cited by 17 publications

References 104 publications

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network

An Innovative model of magnetically intercalated expanded graphite for calculating radar attenuation performance at 2–18 GHz

Contact Info

Product

Resources

About