Millimeter-wave Data Research Articles

Optimization of the automotive millimeter-wave radar data processing using the modified MFNN neural network

BACKGROUND: Recognition and representation of the road scene is a relevant task in the field of autonomous driving. One of the ways to improve the characteristics of the existing sensory hardware is the use of neural networks for signal processing. AIM: To conduct an experimental study of the possibilities of using the modified MFNN neural network to increase the resolution of a radar with a small number of channels, to compare it with the classical algorithm based on the fast Fourier transform (FFT), and, in addition, to compare the results with the data obtained from other types of sensors (lidars). METHODS: The algorithm of the road scene representation, in particular, the detection of pedestrians and cars, is used with a millimeter-range automotive radar and a method for determining data components in relation to the DOA problem, based on the MFNN neural network, modified for the case of representing signals taking complex values in the form of excessive basis coefficients minimizing these coefficients according to the L1 norm. The algorithm based on the fast Fourier transform (FFT) is used for comparative analysis. RESULTS: As a result of the conducted research, confirmation of the practical feasibility of the developed modifications of the MFNN method was obtained, and the advantage of using a neural network, consisting in increasing the degree of detail of objects, the accuracy of determining their shape and position using a radar with a small number of channels, was demonstrated. CONCLUSION: The obtained results can be used to develop solutions to improve the efficiency of detecting obstacles on the way of transport, automatic vehicle control, continuous environmental monitoring, and so on, which helps to improve the safety and efficiency of highly automated and autonomous systems.

Sea Fog Recognition near Coastline Using Millimeter-Wave Radar Based on Machine Learning

Sea fog is a hazardous natural phenomenon that reduces visibility, posing a threat to ports and nearshore navigation, making the identification of nearshore sea fog crucial. Millimeter-wave radar has significant advantages over satellites in capturing sudden and localized sea fog weather. The use of millimeter-wave radar for sea fog identification is still in the exploratory stage in operational fields. Therefore, this paper proposes a nearshore sea fog identification algorithm that combines millimeter-wave radar with multiple machine learning methods. Firstly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to partition radar echoes, followed by the K-means clustering algorithm (KMEANS) to divide the partitions into recognition units. Then, Sea-Fog-Recognition-Convolutional Neural Network (SFRCNN) is used to classify whether the recognition units are sea fog areas, and finally, the partition coverage algorithm is employed to improve identification accuracy. The experiments conducted using millimeter-wave radar observation data from the Pingtan Meteorological Observation Base in Fujian, China, achieved an identification accuracy of 96.94%. The results indicate that the proposed algorithm performs well and expands the application prospects of such equipment in meteorological operations.

Flaring Stars in a Nontargeted Millimeter-wave Survey with SPT-3G

We present a flare star catalog from 4 yr of nontargeted millimeter-wave survey data from the South Pole Telescope (SPT). The data were taken with the SPT-3G camera and cover a 1500 deg2 region of the sky from 20h40m0s to 3h20m0s in right ascension and from −42° to −70° in declination. This region was observed on a nearly daily cadence from 2019 to 2022 and chosen to avoid the plane of the galaxy. A short-duration transient search of this survey yields 111 flaring events from 66 stars, increasing the number of both flaring events and detected flare stars by an order of magnitude from the previous SPT-3G data release. We provide cross-matching to Gaia DR3, as well as matches to X-ray point sources found in the second ROSAT all-sky survey. We have detected flaring stars across the main sequence, from early-type A stars to M dwarfs, as well as a large population of evolved stars. These stars are mostly nearby, spanning 10–1000 pc in distance. Most of the flare spectral indices are constant or gently rising as a function of frequency at 95/150/220 GHz. The timescale of these events can range from minutes to hours, and the peak ν L ν luminosities range from 1027 to 1031 erg s−1 in the SPT-3G frequency bands.

RCRFNet: Enhancing Object Detection with Self-Supervised Radar-Camera Fusion and Open-Set Recognition.

Robust object detection in complex environments, poor visual conditions, and open scenarios presents significant technical challenges in autonomous driving. These challenges necessitate the development of advanced fusion methods for millimeter-wave (mmWave) radar point cloud data and visual images. To address these issues, this paper proposes a radar-camera robust fusion network (RCRFNet), which leverages self-supervised learning and open-set recognition to effectively utilise the complementary information from both sensors. Specifically, the network uses matched radar-camera data through a frustum association approach to generate self-supervised signals, enhancing network training. The integration of global and local depth consistencies between radar point clouds and visual images, along with image features, helps construct object class confidence levels for detecting unknown targets. Additionally, these techniques are combined with a multi-layer feature extraction backbone and a multimodal feature detection head to achieve robust object detection. Experiments on the nuScenes public dataset demonstrate that RCRFNet outperforms state-of-the-art (SOTA) methods, particularly in conditions of low visual visibility and when detecting unknown class objects.

Unveiling the gas phase H2NCO radical: Laboratory rotational spectroscopy and interstellar searches in the direction of IRAS 16293-2422

Context. The carbamoyl radical (H2NCO) is believed to play a central role in the ice-grain chemistry of crucial interstellar complex organic molecules such as formamide and acetamide. Yet, little is known about this radical, which remains elusive in laboratory gasphase experiments. Aims. In order to enable interstellar searches of H2NCO, we have undertaken a mandatory laboratory characterisation of its pure rotational spectrum. Methods. We report the gas-phase laboratory detection of H2NCO, produced by H-atom abstraction from formamide, using pure rotational spectroscopy at millimetre and submillimetre wavelengths. Millimetre-wave data were acquired using chirped-pulse Fourier-transform spectroscopy, while submillimetre-wave ones were obtained using Zeeman-modulated spectroscopy. Experimental measurements were guided by quantum-chemical calculations at the ωB97X-D/cc-pVQZ level of theory. Interstellar searches for the radical have been undertaken in the Protostellar Interferometric Line Survey (PILS) towards the solar-type protostar IRAS 16293-2422. Results. From the assignment and fit of experimental transitions up to 660 GHz, reliable spectroscopic parameters for H2NCO in its ground vibrational state have been derived, enabling accurate spectral predictions. No transitions of the radical were detected in the PILS survey. The inferred upper limit shows that the H2NCO abundance is at least 60 times below that of formamide and 160 times below that of HNCO in this source; a value that is in agreement with predictions from a physico-chemical model of this young protostar.

Anti-low angle resolution: Some adaptive improvements in anchor-based object detection algorithm for automotive radar target detection

Automotive radar has extensive and well-established applications in advanced driver assistance systems (ADAS). It has been the most reliable and preferred sensor for functions such as adaptive cruise control and automatic emergency braking. Nevertheless, the limited angle resolution of traditional millimeter-wave radar data reduces its effectiveness in autonomous driving systems (ADS). Our study presents some adaptive improvements in anchor-based object detection algorithm framework to address the inaccuracies in target detection and positioning resulting from the limited angle resolution of millimeter-wave radar data. Our model utilizes the Range-Angle (RA) and Range-Doppler (RD) views of a RAD cube as inputs. An anchor assignment strategy - Max Intersection over Union (IoU) assigner is used to generate more positive samples than before, enabling the model to learn more about the relevant features of targets. We propose a multi-detection box fusion Non-Maximum Suppression (NMS) mechanism to fuse prediction results with varying confidence scores, thus improving the accuracy of target detection positions by addressing the issue of target box position deviation caused by low angle resolution. In addition, an actual distance loss function is designed to constrain the regularity that the size of the bounding box is inversely proportional to the distance between the target and the radar. Based on experimental results, the proposed method increases the mean average precision (mAP) by approximately 10% compared to the baseline method.

Sliced chaotic encrypted transmission scheme based on key masked distribution in a W-band millimeter-wave system

In order to guarantee the information of the W-band wireless communication system from the physical layer, this paper proposes the sliced chaotic encrypted (SCE) transmission scheme based on key masked distribution (KMD). The scheme improves the security of free space communication in the W-band millimeter-wave wireless data transmission system. In this scheme, the key information is embedded into the random position of the ciphertext information, and then the ciphertext carrying the key information is encrypted by multi-dimensional chaos. Chaotic system 1 constructs a three-dimensional discrete chaotic map for implementing KMD. Chaotic system 2 constructs complex nonlinear dynamic behavior through the coupling of two neurons, and the masking factor generated is used to realize SCE. In this paper, the transmission of 16QAM signals in a 4.5 m W-band millimeter-wave wireless communication system with a rate of 40 Gb/s is proved by experiments, and the performance of the system is analyzed. When the input optical power is 5 dBm, the bit error rate (BER) of the legitimate encrypted receiver is 1.23 × 10−3. When the offset of chaotic sequence x and chaotic sequence y is 100, their BERs are more than 0.21. The key space of the chaotic system reaches 10192, which can effectively prevent illegal attacks and improve the security performance of the system. The experimental results show that the scheme can effectively distribute the keys and improve the security of the system. It has great application potential in the future of W-band millimeter-wave wireless secure communication.

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

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.

Multi-Dimensional Information Fusion You Only Look Once Network for Suspicious Object Detection in Millimeter Wave Images

Millimeter wave (MMW) imaging systems have been widely used for security screening in public places due to their advantages of being able to detect a variety of suspicious objects, non-contact operation, and harmlessness to the human body. In this study, we propose an innovative, multi-dimensional information fusion YOLO network that can aggregate and capture multimodal information to cope with the challenges of low resolution and susceptibility to noise in MMW images. In particular, an MMW data information aggregation module is developed to adaptively synthesize a novel type of MMW image, which simultaneously contains pixel, depth, phase, and diverse signal-to-noise information to overcome the limitations of current MMW images containing consistent pixel information in all three channels. Furthermore, this module is capable of differentiable data enhancements to take into account adverse noise conditions in real application scenarios. In order to fully acquire the augmented contextual information mentioned above, we propose an asymptotic path aggregation network and combine it with YOLOv8. The proposed method is able to adaptively and bidirectionally fuse deep and shallow features while avoiding semantic gaps. In addition, a multi-view, multi-parameter mapping technique is designed to enhance the detection ability. The experiments on the measured MMW datasets validate the improvement in object detection using the proposed model.

MmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation Using mmWave Radars.

In this article, we presented mmPose-NLP, a novel natural language processing (NLP) inspired sequence-to-sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave) radar data. To the best of our knowledge, this is the first method to precisely estimate up to 25 skeletal key points using mmWave radar data alone. Skeletal pose estimation is critical in several applications ranging from autonomous vehicles, traffic monitoring, patient monitoring, and gait analysis, to defense security forensics, and aid both preventative and actionable decision making. The use of mmWave radars for this task, over traditionally employed optical sensors, provides several advantages, primarily its operational robustness to scene lighting and adverse weather conditions, where optical sensor performance degrade significantly. The mmWave radar point-cloud (PCL) data are first voxelized (analogous to tokenization in NLP) and N frames of the voxelized radar data (analogous to a text paragraph in NLP) is subjected to the proposed mmPose-NLP architecture, where the voxel indices of the 25 skeletal key points (analogous to keyword extraction in NLP) are predicted. The voxel indices are converted back to real-world 3-D coordinates using the voxel dictionary used during the tokenization process. Mean absolute error (MAE) metrics were used to measure the accuracy of the proposed system against the ground truth, with the proposed mmPose-NLP offering < 3 cm localization errors in the depth, horizontal, and vertical axes. The effect of the number of input frames versus performance/accuracy was also studied for N = {1,2, ... ,10} . A comprehensive methodology, results, discussions, and limitations are presented in this article. All the source codes and results are made available on GitHub for further research and development in this critical yet emerging domain of skeletal key-point estimation using mmWave radars.

DCS-CTN: Subtle Gesture Recognition Based on TD-CNN-Transformer via Millimeter-Wave Radar

Gesture recognition has been a hot research topic in human-computer interaction, since contactless gesture recognition will provide increasing applications in many fields. Millimeter-wave (mmWave) radar well serves this technology because of its high accuracy, easy integration, and strong anti-jamming ability in moving object detection. However, it is still challenging to meet the requirement of high precision in subtle gesture recognition based on traditional methods via point cloud or Range-Doppler heat map of millimeter-wave radar. Considering the raw data from millimeter-wave radar with more information such as phase, we propose a system that uses the constructed millimeter-wave radar data cube sequence and Timedistributed-CNN-Transformer network (CTN), called DCS-CTN system, to get higher hand gesture recognition accuracy. In this system, we introduce a time-distributed wrapper (TD) and convolution neural network (CNN) to extract local features of the data cube sequence, a position encoder to retain time information of the sequence, and a transformer network to get global features of the sequence. The experiments results show that this system can achieve hand gesture recognition accuracy of 99.75%, which is significantly higher than the other traditional approaches.

Mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud.

Automatic driving technology refers to equipment such as vehicle-mounted sensors and computers that are used to navigate and control vehicles autonomously by acquiring external environmental information. To achieve automatic driving, vehicles must be able to perceive the surrounding environment and recognize and understand traffic signs, traffic signals, pedestrians, and other traffic participants, as well as accurately plan and control their path. Recognition of traffic signs and signals is an essential part of automatic driving technology, and gesture recognition is a crucial aspect of traffic-signal recognition. This article introduces mm-TPG, a traffic-police gesture recognition system based on a millimeter-wave point cloud. The system uses a 60 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar as a sensor to achieve high-precision recognition of traffic-police gestures. Initially, a double-threshold filtering algorithm is used to denoise the millimeter-wave raw data, followed by multi-frame synthesis processing of the generated point cloud data and feature extraction using a ResNet18 network. Finally, gated recurrent units are used for classification to enable the recognition of different traffic-police gestures. Experimental results demonstrate that the mm-TPG system has high accuracy and robustness and can effectively recognize traffic-police gestures in complex environments such as varying lighting and weather conditions, providing strong support for traffic safety.

Interference Mitigation Method for Millimeter-Wave Frequency-Modulation Continuous-Wave Radar Based on Outlier Detection and Variational Modal Decomposition

Aiming at the problem of mutual interference between millimeter-wave frequency-modulation continuous-wave (FMCW) radars, an interference mitigation method based on outlier detection and variational mode decomposition (VMD) is proposed in this paper. Firstly, by differential processing of the raw millimeter-wave FMCW radar data, combined with threshold detection, the interfered sample area is located. Adaptive amplitude limiting is applied to the interfered samples to achieve initial suppression of the interference. Then, based on the VMD algorithm, the processed data are adaptively decomposed to obtain multiple intrinsic mode functions (IMFs). The Pearson correlation coefficient between each IMF and the signal before decomposition is calculated, and the IMF with the maximum Pearson correlation coefficient is extracted as the signal component to achieve the separation of the target signal from the interference and noise. The proposed method was validated based on simulation and experimental data. The results show that the proposed method achieves the best performance in terms of signal-to-interference-plus-noise ratio (SINR), mean square error (MSE), and kurtosis in frequency (KF) compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition (CEEMD). Further comparison was made with two typical methods, and the Range–Doppler (RD) map and SINR results showed that the proposed method exhibited certain performance advantages.

Doppler-free Spectroscopy of Buffer-Gas-Cooled Calcium Monohydroxide

In this study, we report the Doppler-free spectra of buffer-gas-cooled CaOH. We observed five Doppler-free spectra containing low-J Q1 and R12 transitions, which were only partially resolved by previous Doppler-limited spectroscopies. The spectra frequencies were corrected using the Doppler-free spectra of iodine molecules; accordingly, the uncertainty was estimated to be below 10 MHz. We determined the spin-rotation constant in the ground state, which agrees with the values reported in the literature obtained based on millimeter-wave data within 1 MHz. This suggests that the relative uncertainty is much smaller. The present study demonstrates the Doppler-free spectroscopy of a polyatomic radical and the broad applicability of the buffer gas cooling method to molecular spectroscopy. CaOH is the only polyatomic molecule that can be directly laser-cooled and trapped in a magneto-optical trap. High-resolution spectroscopy of such molecules is useful for establishing efficient laser cooling schemes of polyatomic molecules.

Rotational spectra of twenty-one vibrational states of [35Cl]-and [37Cl]-chlorobenzene

The rotational spectra of [35Cl]- and [37Cl]-chlorobenzene (C6H5Cl) have been studied from 2 to 18 GHz and 130–360 GHz, resulting in the measurement, assignment, and least-squares fitting of almost 40,000 transitions of twenty-one vibrational states. Previously measured [35Cl]- and [37Cl]-chlorobenzene ground-state transitions were combined with newly measured transitions and fit to A-reduced, partial sextic Hamiltonian models with low-error (σtotal fit <0.05 MHz). Analysis of the 2–18 GHz spectrum allowed for refinement of the nuclear hyperfine coupling constants for the ground-state spectra of both isotopologues, while measurement of the 130–360 GHz spectrum provided sufficient information to determine the sextic centrifugal distortion constants of the ground states for the first time. From these millimeter-wave data collected at room temperature, the spectroscopic constants for the lowest-energy fundamentals of [35Cl]-chlorobenzene (ν20, ν30, ν11, ν14, ν19, ν29 and ν18) and [37Cl]-chlorobenzene (ν20, ν30, ν11) were determined. As with previous studies of chloroarenes, the computed (B3LYP/6–311+G(2d,p)) spectroscopic constants show quite close agreement with the experimentally determined values.

Simultaneous Millimeter-wave, Gamma-Ray, and Optical Monitoring of the Blazar PKS 2326-502 during a Flaring State

Including millimeter-wave data in multiwavelength studies of the variability of active galactic nuclei (AGN) can provide insights into AGN physics that are not easily accessible at other wavelengths. We demonstrate in this work the potential of cosmic microwave background (CMB) telescopes to provide long-term, high-cadence millimeter-wave AGN monitoring over large fractions of sky. We report on a pilot study using data from the SPTpol instrument on the South Pole Telescope (SPT), which was designed to observe the CMB at arcminute and larger angular scales. Between 2013 and 2016, SPTpol was used primarily to observe a single 500 deg2 field, covering the entire field several times per day with detectors sensitive to radiation in bands centered at 95 and 150 GHz. We use SPT 150 GHz observations to create AGN light curves, and we compare these millimeter-wave light curves to those at other wavelengths, in particular γ-ray and optical. In this Letter, we focus on a single source, PKS 2326-502, which has extensive, day-timescale monitoring data in gamma-ray, optical, and now millimeter-wave between 2013 and 2016. We find PKS 2326-502 to be in a flaring state in the first 2 yr of this monitoring, and we present a search for evidence of correlated variability between millimeter-wave, optical R-band, and γ-ray observations. This pilot study is paving the way for AGN monitoring with current and upcoming CMB experiments such as SPT-3G, Simons Observatory, and CMB-S4, including multiwavelength studies with facilities such as Vera C. Rubin Observatories Large Synoptic Survey Telescope.

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion.

To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes.

Galaxy clusters at z ∼ 1 imaged by ALMA with the Sunyaev–Zel’dovich effect

Abstract We present high angular resolution measurements of the thermal Sunyaev–Zel’dovich effect (SZE) toward two galaxy clusters, RCS J2319+0038 at $z$ = 0.9 and HSC J0947−0119 at $z$ = 1.1, by the Atacama Large Millimeter/submillimeter Array (ALMA) in Band 3. They are supplemented with available Chandra X-ray data, optical data taken by Hyper Suprime-Cam on Subaru, and millimeter-wave SZE data from the Atacama Cosmology Telescope. Taking into account departures from spherical symmetry, we have reconstructed non-parametrically the inner pressure profile of two clusters as well as electron temperature and density profiles for RCS J2319+0038. This is one of the first such measurements for an individual cluster at $z \gtrsim 0.9$. We find that the inner pressure profile of both clusters is much shallower than that of local cool-core clusters. Our results consistently suggest that RCS J2319+0038 hosts a weak cool core, where radiative cooling is less significant than in local cool cores. On the other hand, HSC J0947−0119 exhibits an even shallower pressure profile than RCS J2319+0038 and is more likely to be a non-cool-core cluster. The SZE centroid position is offset by more than 140 $h_{70}^{-1}$ kpc from the peaks of galaxy distribution in HSC J0947−0119, suggesting a stronger influence of mergers in this cluster. We conclude that these distant clusters are at a very early stage of developing the cool cores typically found in clusters at lower redshifts.

Fusing mmWave Radar With Camera for 3-D Detection in Autonomous Driving

Three-dimensional detection is essential for autonomous driving and intelligent transportation system, as it enables vehicles to detect and track surrounding objects. Usually, autonomous vehicles are equipped with multiple sensing modalities to achieve robust and precise detection. This work focuses on fusing millimeter-wave radar data with monocular images, as radar can make up for the lack of explicit depth information. We propose a novel approach that fuses radar data and images at the feature level for 3-D detection. Radar points are first merged into a raw feature map with data set statistics by a novel transformation method. With this transformation, radar features can be extracted by convolutional neural networks and fused with image features. Object properties, including location, dimension, and rotation are regressed from the fused features. In this article, the proposed fusion strategy is implemented with a keypoint-based 3-D detection framework and evaluated on the challenging NuScenes data set. Experimental results suggest that the fusion of radar data promotes 3-D detection capability in public benchmarking.

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Human activity recognition (HAR) can effectively improve the safety of the elderly at home. However, non-contact millimeter-wave radar data on the activities of the elderly is often challenging to collect, making it difficult to effectively improve the accuracy of neural networks for HAR. We addressed this problem by proposing a method that combines the improved principal component analysis (PCA) and the improved VGG16 model (a pre-trained 16-layer neural network model) to enhance the accuracy of HAR under small-scale datasets. This method used the improved PCA to enhance features of the extracted components and reduce the dimensionality of the data. The VGG16 model was improved by deleting the complex Fully-Connected layers and adding a Dropout layer between them to prevent the loss of useful information. The experimental results show that the accuracy of our proposed method on HAR is 96.34%, which is 4.27% higher after improvement, and the training time of each round is 10.88 s, which is 12.8% shorter than before.