Wave Radar Data Research Articles

Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data

Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.

Chaotic Properties of Gravity Waves during Typhoons Observed by HFSWR

The gravity wave produced by typhoons has been an essential subject of study that concerns numerous researchers. The damage to human beings and infrastructure in coastal regions caused by typhoon disasters annually is very severe, and analyzing gravity wave variation is a reliable approach to research typhoons. High-frequency surface wave radar (HFSWR) as an over-the-horizon radar can achieve real-time monitoring of an extensive sea area and space. This paper derived the gravity wave perturbation spectrum by handling high-frequency surface wave radar data during typhoons. The gravity wave spectrum data were examined by applying the chaos examination approaches of the Lyapunov exponent and phase-space reconstruction to the gravity wave spectrum data after processing and extraction. The reconstructed phase space had a specific shape in a certain direction, with the maximum Lyapunov exponent greater than zero. The gravity wave spectrum data are suggested to have chaotic properties through two chaos examination approaches. This paper demonstrated that the gravity waves observed by a radar have chaotic properties through the measurement data of HFSWR. While the chaotic properties suggest that observed gravity wave data are predictable in the short term, they are unpredictable in the long term. Predicting gravity wave data is important for understanding the chaotic properties of the atmosphere and for future gravity wave prediction.

Improved radar range and velocity resolution using the fast orthogonal search

AbstractThe authors show improved radar range and velocity resolution is achieved using fast orthogonal search in place of the standard fast Fourier transform. The method reliably detects targets that are close either in range or in velocity, which is relevant today given recent advances in target swarms. The method presented also allows radars to detect slower targets closer to the zero‐Doppler region, like uncrewed aerial systems (drones) moving slowly to escape detection. The authors also show a novel method for estimating the velocity spectrum of a pulse‐Doppler radar using the in‐phase and quadrature samples directly by presenting unique terms to the fast orthogonal search. This eliminates the need to convert into complex samples for processing. The authors compared the performance of the new fast orthogonal search method against the fast Fourier transform using both simulated pulse‐Doppler radar data and real frequency‐modulated continuous wave radar data taken in an anechoic chamber. The simulated pulse‐Doppler scenarios included targets closely spaced in velocity and targets near the zero‐Doppler null, all at a variety of signal‐to‐interference ratios. In all cases, the fast orthogonal search method was shown capable of detecting both the closely‐spaced and near‐zero‐Doppler targets a greater percentage of time compared to the fast Fourier transform. Furthermore, when the methods were compared using real frequency‐modulated continuous wave radar data, the stationary targets were resolved at 0.4 m using the fast orthogonal search as compared to 0.6 m for the fast Fourier transform. Thus, this new method is capable of greater radar range and velocity resolution than the fast Fourier transform.

A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data

AbstractThis article presents a deep neural network‐based constant false alarm rate (NNB‐CFAR) detector for simulated high‐frequency surface wave radar (HFSWR) data. A deep neural network is trained to identify fluctuation parameters of each cell of a range‐Doppler power spectrum based on the patterns present in the neighbouring cells. The estimated parameters are then used for calculating a detection threshold with a user‐specified probability of false alarm. To train the network, a realistic model of HFSWR echoes is used for generating a large labelled range‐Doppler image dataset, including many possible clutter scenarios and interfering target echoes. Several CFAR windows are extracted from the training range‐Doppler dataset and used as training data. The neural network is trained to replicate the output of a maximum likelihood estimator based on the reference cells of the CFAR window. The NNB‐CFAR algorithm was then compared to traditional CFAR algorithms by identifying targets in the second set of simulated range‐Doppler images. The probability of detection was also experimentally measured in the context of HFSWR for all algorithms. Results show that the technique can significantly improve detection rates amid strong clutter.

BEV-radar: bidirectional radar-camera fusion for 3D object detection

Exploring millimeter wave radar data as complementary to RGB images for ameliorating 3D object detection has become an emerging trend for autonomous driving systems. However, existing radar-camera fusion methods are highly dependent on the prior camera detection results, rendering the overall performance unsatisfactory. In this paper, we propose a bidirectional fusion scheme in the bird-eye view (BEV-radar), which is independent of prior camera detection results. Leveraging features from both modalities, our method designs a bidirectional attention-based fusion strategy. Specifically, following BEV-based 3D detection methods, our method engages a bidirectional transformer to embed information from both modalities and enforces the local spatial relationship according to subsequent convolution blocks. After embedding the features, the BEV features are decoded in the 3D object prediction head. We evaluate our method on the nuScenes dataset, achieving 48.2 mAP and 57.6 NDS. The result shows considerable improvements compared to the camera-only baseline, especially in terms of velocity prediction. The code is available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/Etah0409/BEV-Radar">https://github.com/Etah0409/BEV-Radar</ext-link>.

Swell and wind-sea partitioning of HF radar directional spectra

ABSTRACT Partitioning is a process used to separate wind-sea and swell contributions in an ocean wave directional spectrum to simplify, and hence make more useful, the interpretation of the spectrum for users of wave data. HF radar systems can measure the wave spectrum over regions of the coastal ocean from the coast to over 100 km offshore with good spatial and temporal resolution depending on the operating frequency and bandwidth. Such systems can measure hundreds of directional spectra across the field of view of the radar, so there is very strong motivation to reduce the dimensionality of the data set for practical applications using partitioning. For similar reasons partitioning methods are increasingly being used for wave model and satellite-measured spectra. A partitioning method, which extends the method of Waters J, Wyatt LR, Wolf J, Hines A. [2013. Data assimilation of partitioned HF radar wave data into Wavewatch III. Ocean Model. 72:17–31.] for HF radar data, is described, assessed using buoy data and used to demonstrate the spatial variability of both swell and wind waves in three coastal regions. The results are very encouraging. HF radar systems could therefore provide very useful data for wave model and satellite partitioning validations in coastal waters where model and satellite measurements are most challenged by wave-current interactions, coastal topography and bathymetry.

Gait Recognition Using Spatio-Temporal Information of 3D Point Cloud via Millimeter Wave Radar

Gait recognition is one of the crucial methods in identity recognition, which has a wide range of applications in many fields, such as smart home, smart office, and health monitoring. The camera is the most mainstream traditional solution. But the camera is difficult to maintain stable performance in the dark, low light, and bad weather conditions. In addition, privacy leakage is also one of the important issues that people worry about. In contrast, as the latest research progress in gait recognition, millimeter wave radar can not only protect people’s privacy, but also maintain normal perception performance in dark conditions. In this paper, we propose a system for gait recognition named MTPGait using spatio-temporal information via millimeter wave radar. We specially design a neural network that can extract multiscale spatio-temporal features along space and time dimensions of 3D point cloud concisely and efficiently. We use LSTM to design the context flow of local and global time and space, fusing local and global spatio-temporal features. In addition, we construct and release a millimeter wave radar 3D point cloud data set, which consists of 960-minute gait data of 40 volunteers. Using the data set, we evaluate the system and compare it with four state-of-the-art algorithms. The experimental results show that MTPGait is able to achieve 96.7% recognition accuracy in a single-person scene on fixed route and 90.2% recognition accuracy when two people coexist, while none of the existing methods is more than 90% recognition accuracy in either scenario.

A Comprehensive Study of a Winter Haze Episode over the Area around Bohai Bay in Northeast China: Insights from Meteorological Elements Observations of Boundary Layer

Based on wind profile radar observations, along with high-frequency wave radar data, meteorological data, and air quality monitoring data, we studied a haze episode in Panjin—a coastal city around Bohai Bay in Northeast China—that occurred from 8 to 13 February 2020. The results show that this persistent pollution event was dominated by PM10 and PM2.5 and their mass concentrations were both ~120 μg/m3 in the mature stage. In the early stage, the southerly sea breeze of ~4.5 m/s brought a large amount of moist air from the sea, which provided sufficient water vapor for the condensation and nucleation of pollutants, and thus accelerated the formation of haze. In the whole haze process, a weak updraft first appeared in the boundary layer, according to the vertical profile, contributing to the collision and growth of particulate matter. Vertical turbulence was barely observed in the mature stage, with the haze layer reaching 900 m in its peak, suggesting stable stratification conditions of the atmospheric boundary layer. The explosive growth of pollutant concentrations was about 10 h later than the formation of the stable stratification condition of the boundary layer. The potential source areas of air pollutants were identified by the WRF-FLEXPART model, which showed the significant contribution of local emissions and the transport effect of sea breeze. This study provides insights into the formation mechanism of haze pollution in this area, but the data observed in this campaign are also valuable for numerical modeling.

A Multi-Stage Vessel Tracklet Association Method for Compact High-Frequency Surface Wave Radar

A compact high-frequency surface wave radar, used for target detection, suffers from a low signal-to-noise ratio, low detection probability, a high false alarm rate, and low positioning accuracy; this is due to its low transmit power and the reduced aperture size of the receiving antenna array. When target tracking algorithms are applied to compact high-frequency surface wave radar data, track fragmentation often occurs and a long track may be broken into several track segments (a.k.a. tracklets), which degrade the tracking continuity for a maritime surveillance system. We present a multi-stage vessel tracklet association method, based on bidirectional prediction and optimal assignment, to associate the broken tracklets belonging to the same target, and connect them to form one continuous track in a multi-target tracking scenario. Firstly, two global motion parameters, i.e., the average heading and average speed, were, respectively, extracted from the newly initiated and terminated tracklets as features for a rough tracklet association, then k-means clustering was used to produce the preliminary tracklet pairs. Subsequently, the temporal and spatial constraints on the initiated and terminated tracklets were considered to refine the preliminary tracklet pairs, to obtain the candidate tracklet pairs. Finally, the tracklet association costs were calculated using Doppler velocity, range, and azimuth to determine the similarity between tracklets in the candidate tracklet pairs, and an association cost matrix was obtained. Then an optimal assignment method based on Jonker–Volgenant–Castanon algorithm was applied to the association matrix to achieve optimal tracklet matching by minimizing the total association costs. Tracklet association experiments with both simulated and field data were conducted; experimental results show that, compared with existing track segment association methods, the association accuracy of the proposed method is significantly improved with better tracking continuity.

The Improved A* Obstacle Avoidance Algorithm for the Plant Protection UAV with Millimeter Wave Radar and Monocular Camera Data Fusion

To enhance obstacle avoidance abilities of the plant protection UAV in unstructured farmland, this article improved the traditional A* algorithms through dynamic heuristic functions, search point optimization, and inflection point optimization based on millimeter wave radar and monocular camera data fusion. Obstacle information extraction experiments were carried out. The performance between the improved algorithm and traditional algorithm was compared. Additionally, obstacle avoidance experiments were also carried out. The results show that the maximum error in distance measurement of data fusion method was 8.2%. Additionally, the maximum error in obstacle width and height measurement were 27.3% and 18.5%, respectively. The improved algorithm is more useful in path planning, significantly reduces data processing time, search grid, and turning points. The algorithm at most increases path length by 2.0%, at least reduces data processing time by 68.4%, search grid by 74.9%, and turning points by 20.7%. The maximum trajectory offset error was proportional to the flight speed, with a maximum trajectory offset of 1.4 m. The distance between the UAV and obstacle was inversely proportional to flight speed, with a minimum distance of 1.6 m. This method can provide a new idea for obstacle avoidance of the plant protection UAV.

Inversion for the density–depth profile of Dome A, East Antarctica, using frequency-modulated continuous wave radar

AbstractThe density–depth relationship of the Antarctic ice sheet is important for establishing a high-precision surface mass balance model and predicting future ice-sheet contributions to global sea levels. A new algorithm is used to reconstruct firn density and densification rate by inverting monostatic radio wave echoes from ground-operated frequency-modulated continuous wave radar data collected near four ice cores along the transect from Zhongshan Station to Dome A. The inverted density profile is consistent with the core data within 5.54% root mean square error. Due to snow redistribution, the densification rate within 88 km of ice core DT401 is correlated with the accumulation rate and varies greatly over horizontal distances of <5 km. That is, the depth at which a critical density of 830 kg m−3is reached decreases and densification rate increases in high-accumulation regions but decreases in low-accumulation regions. This inversion technique can be used to analyse more Antarctic radar data and obtain the density distribution trend, which can improve the accuracy of mass-balance estimations.

Generalization of Channel Micro-Doppler Capacity Evaluation for Improved Finer-Grained Human Activity Classification Using MIMO UWB Radar

In this article, a channel micro-Doppler ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{D}$ </tex-math></inline-formula> ) signatures’ quantitative evaluation method for multiple-input–multiple-output (MIMO) radar system, named relative signal–noise ratio in the time–frequency domain and denoted as RSNRtf, is introduced. The impact on RSNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tf</sub> by some key dominant factors in real practical and diverse detection environments was theoretically analyzed first. Then, this proposed method is exploited to conduct effective and superior channel selection for improved human activity classification at random human target positions. Based on numerous MIMO stepped-frequency continuous wave (SFCW) radar data from eight subjects with seven activities at nine positions in a through-wall within a view angle of ±20° and up to 6-m distance away, the influence of various primary factors on the proposed method is investigated. Investigative experiments showed that the proposed method has a wide range of applicability and a high interference tolerance even for different detection distances, angles, activities, and subjects. Classification experimental results based on typical classifiers also showed that the proposed method could effectively improve human activity classification. Moreover, we can claim that the proposed method can achieve a higher outstanding performance in the channel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{D}$ </tex-math></inline-formula> capacity evaluation and enhanced classification for the MIMO-radar-based human activity classification in free space or with a wider detection field with large angle range.

Observing Sea Surface Current by Gaofen-3 Satellite Along-Track Interferometric SAR Experimental Mode

Sea surface current is a research hotspot in oceanography. Space-borne along-track interferometric synthetic aperture radar (along-track InSAR, ATI) is a promising sensor for measuring high-resolution sea surface current field, and there is no operational system in orbit yet. To support future space-borne ATI systems, based on the ATI experimental mode of the Gaofen-3 (GF-3) satellite, the first sea surface current observing experiment was conducted in the Jiaozhou Gulf in China, in 2019. Meanwhile, SAR observations and in situ instrument measurements of the current are obtained in the experiments. The data is first preprocessed by a processor specially developed for the GF-3 ATI data. Then, the current is extracted based on the M4S mode. The retrieved current of the Jiaozhou Gulf is compared with ground-based high frequency surface wave radar data. The results show that the root mean square error of the surface current observed by the GF-3 satellite is less than 0.2 m/s.

Historical surface mass balance from a frequency-modulated continuous-wave (FMCW) radar survey from Zhongshan station to Dome A

AbstractUsing frequency-modulated continuous wave radar data from the 32nd Chinese Antarctic Research Expedition in 2015/16, subsurface profiles were obtained along an East Antarctic inland traverse from Zhongshan station to Dome A, and four distinct regions were selected to analyze the spatiotemporal variability in historical surface mass balance (SMB). Based on depth, density, and age data from ice cores along the traverse, the radar data were calibrated to yield average SMB data. The zone 49–195 km from the coast has the highest SMB (235 kg m−2a−1). The 780–892 km zone was most affected by the Medieval Warm Period and the Little Ice Age, and the SMB duringad1454–1836 (71 kg m−2a−1) was only one-quarter of that in the 20th century. The SMB in the 1080–1157 km zone fluctuates the most, possibly due to erosion or irregular deposition of snow by katabatic winds in low SMB areas with surface elevation fluctuations. Dome A (1157–1236 km) has the lowest SMB (29 kg m−2a−1) and did not decrease during Little Ice Age. Understanding the spatiotemporal variability of SMB in a larger space can help us understand the complex climate history of Antarctica.

Validation of CODAR 25MHz High-Frequency Radar derived Waves in Donghea, Korea

In order to examine the possibility of wave observation of CODAR HF radar, which is mostly used in Korea for surface ocean current observation, CODAR HF radar was installed on the coast to observe real-time waves. Unlike other HF radars, CODAR HF radars are easy to install because they are integrated with transmission and reception. and It is suitable for the domestic coastal environment where the coastline was developed. The CODAR HF radar used in this study transmits and receives waves using a center frequency of 25.4 MHz and a bandwidth of 150 kHz to observe the ocean wave. From the center of the observation point, a circular area of 140 degrees excluding land is observed, and ocean wave data is observed every 10 minutes from the total observation point to the shells 1 to 31 through the spatial average in units of 1 km from 31 km. According to the specifications of the 25Mhz CODAR HF radar, it is said that it is possible to observe a wave height of 4m, but the observation shows that it can be observed up to 6m.The observed CODAR HF radar wave data was compared and validated with the wave data observed by three wave measurement equipments (WAVE rider, AWAC, KMA wave buoy). In the case of the result of comparison of significant wave height and wave period in each installed shell region 1) WAVE rider was Corr. 0.76, 0.87 and RMSE 0.57 m, 0.65 s 2) AWAC was Corr. 0.92, 0.91 and RMSE 0.46 m, 0.45 s 3) KMA wave buoy was Corr. 0.80, 0.89 RMSE 0.65 m, 0.55 s, and all three types of underwater wave observation equipment confirmed high similarity to the wave result observed in HF radar. However, since the underwater wave observation equipment was located only at the 2 and 3 shells (2 to 3 km from the HF radar), additional verification is necessary through the installation of the underwater wave observation equipment at a remote shell location. Also, it is necessary to technically solve that the wave data acquisition rate of HF radar is lower than that of the radial vector. However, HF radar s wide-area wave observation data is expected to compensate for the disadvantages of underwater wave observation data (fixed point observation, difficulty in management and installation, and high maintenance cost). The wave observation data of HF radar could contribute greatly to ocean wave research.

Millimeter wave radar data of people walking.

This dataset contains complex signals coming from a mmWave FMCW radar system. Signals were acquired during a measurement campaign taken indoor and aimed to assess people's different ways of walking. Measurement setup and devices are described.The dataset consists of the acquisitions of six different types of activities, performed by 29 subjects who repeat each activity several times. Therefore, the dataset contains multiple different experiments for each activity, for a total of 231 acquisitions. The subjects walk without any constraint or do not follow any pattern, thus making this dataset useful not only for human gait recognition but also to evaluate the performance of different radar data processing algorithms.

Measurement and Quality Control of MIROS Wave Radar Data at Dokdo

Measurement and Quality Control of MIROS Wave Radar Data at Dokdo

Estimation of on-site directional wave spectra using measured hull stresses on 14,000 TEU large container ships

Container ships are becoming larger and larger in recent years, requiring more evident assurance of the structural safety. To achieve this, it is essential to grasp actual stress history experienced by the ship structures to facilitate efficient design and maintenance, and to use them for optimal operation of the ship. To perform accurate estimation of these stress histories, it is important to precisely estimate the sea state which the ship is actually encountering. In this study, the authors studied a new method to estimate directional wave spectra using measured ship responses and discussed the following three cases. The first one is the combination of two components, vertical bending stress and horizontal bending stress. The second one is the combination of three components, vertical bending stress, horizontal bending stress and double bottom bending stress. The last one is the combination of three components of ship motion (pitch, roll and heave). The estimated sea states are compared with the ocean wave hindcast database and radar data, and then, accuracy and selection of appropriate combination of the responses are discussed.

Nonlinear Extraction of Directional Ocean Wave Spectrum From Synthetic Bistatic High-Frequency Surface Wave Radar Data

This paper proposes the use of a nonlinear inversion technique for the extraction of the directional ocean wave spectrum from bistatic high-frequency surface wave radar (HFSWR) Doppler data. The extraction method is combined with nonlinear regression expressions, solely based on the Doppler data, to retrieve wind speed and direction. Once the initialization parameters have been defined, a blind iterative algorithm based on Tikhonov regularization in Hilbert Scales is used to extract the nondirectional spectrum. The extracted spectrum is then used to determine the directional factor, which is assumed to be described by a cosine-power model. The proposed method yields fairly good results with synthetic noise-contaminated HFSWR data with a priori regularization parameters.

HF radar for wind waves measurements in the Malta-Sicily Channel

The CALYPSO HF radar network is a permanent and fully operational observing system currently composed of four CODAR SeaSonde stations. The system is providing real-time hourly maps of sea surface currents and waves data in the Malta-Sicily Channel. The present work aims to compare significant wave height measurements by HF Radar to wave data from numerical models and satellite altimeter. This is the first time that this set of wave data are analysed since the four HF radars were installed between 2012 and 2015. Results suggest that CODAR HF Radar wave data are a reliable source of wave information even in the case of extreme events, providing an avenue to improve and complete the offer of services deriving from the CALYPSO system. Comparisons of HF radar data with both numerical sea wave model and satellite altimeter data confirm agreement, in particular for radar measurements in the annular sectors within the central range-cells which are also characterized by a more reliable and homogeneous temporal behaviour.