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Describes the first moon-radar echo experiment conducted by Zoltan Bay in Hungary and comments on Bay as a model for contemporary physics education. (DDR)

The discovery of radio waves, infrared, and x-rays and their importance in describing the universe and its origins is discussed. Topics include radio waves from space, the radio pioneers of World War II, radio telescopes, infrared radiation, satellites, space missions, and x-ray telescopes. (KR)

Describes the nature, history, and applications of Doppler radar detecting the speed of moving object. Gives an example of measuring the radial velocity of a scattered wave. (YP)

This paper advocates for spatial simultaneous functioning (SSF) over time division multiple access (TDMA) in joint communication and sensing (JCAS) scenarios for improved resource utilization and reduced interference. SSF enables the concurrent operation of communication and sensing systems, enhancing flexibility and efficiency, especially in dynamic environments. The study introduces joint design communication and sensing scenarios for single input single output (SISO) and multiple input multiple output (MIMO) JCAS receivers. An MIMO-JCAS base station (BS) is proposed to process downlink communication signals and echo signals from targets simultaneously using interference cancellation techniques. We evaluate the communication performance and sensing estimation across both Rayleigh and measured realistic channels. Additionally, a deep neural network (DNN)-based approach for channel estimation and signal detection in JCAS systems is presented. The DNN outperforms the traditional methods in the bit error rate (BER) versus signal-to-noise ratio (SNR) curves, leveraging its ability to learn complex patterns autonomously. The DNN’s training process fine-tunes the performance based on specific problem characteristics, capturing the nuanced relationships within data and adapting to varying SNR conditions for consistently superior performance compared to the traditional approaches.

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.

This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.

We objectively analyzed historic radar reflectivity images and diagnosed mature mesoscale convective systems (MCSs) in South China during the spring season (March to May) of 2009–2019. Our goal was to understand the climatological features of mature MCSs, their interannual variations, and potential connections with surface aerosol pollution. Springtime MCSs over South China were most frequently observed in the central and east-coastal parts of Guangdong Province. The mean monthly half-hourly counts of MCSs over South China in March, April, and May were 103 ± 83, 274 ± 298, and 337 ± 225, respectively, with considerable variability from year to year. Approximately 89% of springtime MCSs over South China had a linear or quasi-linear structure, with convective precipitation covering on average 34% of the total precipitating area of each individual MCS, anmied 63% of MCSs consisted of a stratiform precipitation area trailing the convective precipitation. In March, MCSs occurred most frequently mid-day; in April and May, MCSs were most frequent around midnight. From 2013 to 2019, the MCS occurrences in April were significantly lower during years with more aerosol pollution days. This finding potentially supported our previous model study’s finding that elevated anthropogenic aerosol levels may suppress April MCS occurrences in South China via aerosol-cloud-radiation interactions. Further research is required to better understand the intricate relationship between aerosol abundance and MCS activities in this region.

The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability.

In this paper, we propose a novel method for point cloud complementation called PADPNet. Our approach uses a combination of global and local information to infer missing elements in the point cloud. We achieve this by dividing the input point cloud into uniform local regions, called perceptual fields, which are abstractly understood as special convolution kernels. The set of point clouds in each local region is represented as a feature vector and transformed into N uniform perceptual fields as the input to our transformer model. We also designed a geometric density-aware block to better exploit the inductive bias of the point cloud’s 3D geometric structure. Our method preserves sharp edges and detailed structures that are often lost in voxel-based or point-based approaches. Experimental results demonstrate that our approach outperforms other methods in reducing the ambiguity of output results. Our proposed method has important applications in 3D computer vision and can efficiently recover complete 3D object shapes from missing point clouds.

This article reviews how precipitation microphysics processes are observed in dual-polarization radar observations. These so-called "fingerprints" of precipitation processes are observed as vertical gradients in radar observables. Fingerprints of rain processes are first reviewed, followed by processes involving snow and ice. Then, emerging research is introduced, which includes more quantitative analysis of these dual-polarization radar fingerprints to obtain microphysics model parameters and microphysical process rates. New results based on a detailed rain shaft bin microphysical model are presented, and we conclude with an outlook of potentially fruitful future research directions. [ABSTRACT FROM AUTHOR]

The development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR). In order to solve the problem of radar sampling time variation exacerbated by extending the beam dwell time when detecting weak targets, a sector-matching (SM) PHD filter is proposed, which combines the actual radar system with a PHD filter and quantifies the relationship between the beam dwell time, the false alarm rate and the detection probability. The proposed filter divides the scanning area into small sectors to obtain actual multi-target measurement times and rederives the prediction and update steps based on the actual sampling time. Furthermore, a state correction step is added before state extraction. Applying the SM structure to the basic Gaussian mixture PHD (GM-PHD) filter and labeled GM-PHD filter, the simulation results demonstrate that the proposed structure can improve the accuracy of multi-weak-target state estimation in the dense clutter and can continuously generate explicit trajectories. The overall real-time performance of the proposed filter is similar to that of the PHD filter.

Unmanned aerial vehicles (UAVs) have emerged as a rapidly growing technology seeing unprecedented adoption in various application sectors due to their viability and low cost. However, UAVs have also been used to perform illegal and malicious actions, which have recently increased. This creates a need for technologies capable of detecting, classifying, and deactivating malicious and unauthorized drones. This paper reviews the trends and challenges of the most recent UAV detection methods, i.e., radio frequency-based (RF), radar, acoustic, and electro-optical, and localization methods. Our research covers different kinds of drones with a major focus on multirotors. The paper also highlights the features and limitations of the UAV detection systems and briefly surveys the UAV remote controller detection methods.

Recently, with the advancement of technology, unmanned aerial vehicles (UAVs) have had a significant impact on our daily lives. UAVs have gained critical importance due to their potential threat. In this study, the problem of UAV tracks were investigated. The first study deals with a particle filter (PF) and a diffusion map with a Kalman filter (DMK). From the experimental analysis, it is found that both PF and DMK are very suitable for drone tracking because the trajectories of drones are highly uncertain in highly dynamic and noisy environments. To address this problem, we introduce a Kalman filter (KFUEA) for drone tracking based on uncertainty and error. The KFUEA uses regularized least squares (RLS) to minimize measurement errors and provides an appropriate balance between confidence in previous estimates and future measurements. The experiment was conducted to evaluate the performance of KFUEA compared to PF and DMK, taking into account the high uncertainty and noisy UAV tracking environment. The KFUEA algorithm achieved an excellent result in the root mean square error (RMSE) compared to the non-parametric filtering algorithms PF and DMK.

In this paper, a method for estimating atmospheric refractivity from sea and land clutters is proposed. To estimate the atmospheric refractivity, clutter power spectrums based on an artificial tri-linear model are calculated using an Advanced Refractive Prediction System (AREPS) simulator. Then, the clutter power spectrums are again obtained based on the measured atmospheric refractivity data using the AREPS simulator. In actual operation, this spectrum from measured reflectivity can be replaced with real-time clutter spectrums collected from radars. A cost function for the genetic algorithm (GA) is then defined based on the difference between the two clutter power spectrums to predict the atmospheric refractivity using the artificial tri-linear model. The optimum variables of the tri-linear model are determined at a minimum cost in the GA process. The results demonstrate that atmospheric refractivity can be predicted using the proposed method from the clutter powers.

A radar guidance system is a core component of a radar-guided air-to-air missile, and its tracking accuracy of airborne targets determines the operational effectiveness of said missile. To verify the tracking accuracy of the radar guidance system against an airborne target under the real flight conditions of the missile, an experimental verification system was implemented in this study. The mechanical, electrical, and bus interfaces of the verification system were examined. A tracking accuracy evaluation model of the seeker was designed based on the data obtained from the experiments using the proposed test method, and the tracking accuracy of the seeker in the typical state was analyzed.

In This paper, a high power sub-nanosecond pulse transmitter for Ultra-wideband radar sensor is presented. The backbone of the generator is considered as a step recovery diode and unique pulse injected into the circuit, which gives rise to an ultra-wide band Gaussian pulse. The transistor driver and transmission line pulse forming the whole network are investigated in detail. The main purpose of this work is to transform a square waveform signal to a driving pulse with the timing and the amplitude parameters required by the SRD to form an output Gaussian pulse, and then into high monocycle pulses. In simulation aspect, an improved output response is required, in this way a new model of step recovery diode has been proposed as a sharpener circuit. This proposition was applied to increase the rise-time of the pulses. For a good range radar, a high amplitude pulse is indispensable, especially when it comes to penetrate thick lossy materiel. In order to overcome this challenge, a simple technique and useful solution is introduced to increase the output amplitude of the transmitter. This technique consists to connect the outputs of two identical pulse generators in parallel respecting the restrictions required. The pulse transmitter circuit is completely fabricated using micro-strip structure technology characteristics. Waveforms of the generated monocycle pulses over 10V in amplitude with 3.5 % in overshoot have been obtained. Good agreement has been achieved between measurement and simulation results.

Soil Moisture (SM) is one of the most critical factors for a crop’s growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR’s output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.

In this paper, we study an additional spatial diversity mechanism added to a cavity-backed frequency-diverse aperture for computational imaging applications at X-band frequencies. It is shown that the dynamic variation of cavity modes achieved by means of a simple, multi-port excitation mechanism can significantly simplify the diversity constraints on the frequency-diversity technique for computational imaging and can substantially improve the fidelity of the reconstructed microwave images. Leveraging the proposed technique, we present that, for the selected number of measurement modes, the condition number of the studied computational imaging problem is improved by 8.5 times in comparison to using the frequency-diversity as the only diversity mechanism. The reconstructed X-band images of various targets, including the letters “Q”, “U” and “B” reflect on this improvement and exhibit a higher reconstruction quality, significantly assisting in the identification of the imaged objects. We also present that the resolution limits of the frequency-diverse computational imaging system synthesized using the proposed multi-port cavity are in excellent agreement with the theoretical resolution limits.

Linear FMCW radar suffers from impairments in range and range rate if there are errors in the modulation rate or phase discontinuities. Often, this is a result of a nonlinearity of the voltage-controlled oscillator that is in the source of the transmit and receive local oscillator. The nonlinearity can be corrected at the source by using a nonlinear control voltage or by processing the received beat frequency. Any signal processing using the later method leads to computation time and energy costs, which can be considerable in some applications. When the range migration algorithm using the Stolt Transform is used for Synthetic Aperture Radar (SAR) image processing, the autofocus linearization technique described here costs nothing in additional hardware or computation time.

A free-space microwave nondestructive testing and evaluation module is developed for the low-power, non-ionizing, contactless, and real-time characterization of doped composite thin-film materials in an industrial context. The instrumentation proposed is built up with a handled vector network analyzer interfaced with corrugated horn antennas to measure the near-field complex reflection S11 of planar prepreg composite materials in a roll-to-roll in-line production line. Dedicated modeling and calibrations routines are developed to extract the microwave conductivity from the measured microwave signal. Practical extraction of the radiofrequency (RF) conductivity of thin film prepreg composite materials doped with nano-powders is exemplary shown at the test frequency of 10 GHz.

The aim of this investigation was to establish the validity of a GPS-based tracking system (Polar Team Pro System, PTPS) for estimating sprint performance and to evaluate additional diagnostic indices derived from the temporal course of the movement velocity. Thirty-four male soccer players (20 ± 4 years) performed a 20 m sprint test measured by timing gates (TG), and while wearing the PTPS. To evaluate the relevance of additional velocity-based parameters to discriminate between faster and slower athletes, the median-split method was applied to the 20-m times. Practical relevance was estimated using standardized mean differences (d) between the subgroups. Differences between the criterion reference (TG) and PTPS for the 10 and 20 m splits did not vary from zero (dt10: -0.01 ± 0.07 s, P = 0.7, d < -0.1; dt20: -0.01 ± 0.08 s, P = 0.4, d < -0.2). Although subgroups revealed large differences in their sprint times (d = -2.5), the average accelerations between 5 and 20 km/h as well as 20 and 25 km/h showed merely small effects (d < 0.5). Consequently, analyses of velocity curves derived from PTPS may help to clarify the occurrence of performance in outdoor sports. Thus, training consequences can be drawn which contribute to the differentiation and individualization of sprint training. [ABSTRACT FROM AUTHOR]

Fully autonomous vehicles and the need to improve road safety have led to increased demands on the reliability of various advanced driver assistance systems (ADAS). Automotive radar is a key component of ADAS as it adds both safety and comfort features in vehicles. A key challenge in developing automotive radar is demonstrating reliability especially in corner cases. Building and testing radar systems for corner cases is time‐consuming, costly, and impractical. Simulation is the only practical way of investigating the countless possible automotive radar corner cases. An interesting corner case is the reduction of radar returns due to sharp road bends. Specifically, crucial targets with low radar cross sections (RCS) such as pedestrians can become invisible to radar when driving around sharp road bends. In this article, a high fidelity, full physics‐based simulation of a 77 GHz automotive radar scene will be used to investigate this corner case. The dependence of radar returns on the position of an ego vehicle negotiating a sharp bend will be presented. Results from these simulations show a pedestrian disappearing from view at an approaching angle of 60∘ with a degradation of over 40 dB in pedestrian radar returns. Using results from this study, a terrain‐adaptive technique for improving radar target detection capabilities is proposed. This technique is shown to improve pedestrian radar returns by over 26 dB. Improved radar returns can help in early detection of pedestrians and other relevant targets. Early target detection can help reduce accidents while potentially saving pedestrian lives. [ABSTRACT FROM AUTHOR]

The traditional U-turn design has significantly improved traffic operations for relieving traffic congestion. However, the U-turn diversion and merge segments still cause traffic conflicts and delays. In this paper, an exclusive spur dike U-turn lane (ESUL) is proposed with the aim of addressing the disadvantages of the traditional U-turn design. ESUL provides a separate U-turn lane to diverge, decelerate, U-turn, accelerate and merge without interacting with through traffic. The effectiveness of ESUL is demonstrated through a field data investigation, simulation and analysis with VISSIM software. The proposed design is evaluated in terms of three parameters: travel time, delay and number of stops. Compared to the traditional U-turn design, ESUL can reduce travel time by 29.15%, delay by 66.70% and the number of stops by 100% at most. The results showed that ESUL has better performance than the traditional U-turn design and could be implemented to reduce traffic congestion and the potential hazards caused by U-turn maneuvers. [ABSTRACT FROM AUTHOR]

The purpose of Fuzzy Comprehensive CS-SVR Model (FCCS-SVR) is to evaluate and monitor the health status of a radar equipment and then keep its safe operation. Due to reasons such as few samples, slow changes and the nonlinear structure of data of fault monitoring signal, the health status evaluation of a radar system is quite difficult. By establishing the evaluation index system of a radar, the combination of AHP method and Entropy weight method is studied in this paper. In order to evaluate the value of health status, several optimization algorithms including PSO, GA, BA and CS are used for optimizing the parameters of SVR model. Meanwhile, in order to avoid the problem that the system is at the edge of the state, a radar health assessment method based on the combination of Fuzzy Comprehensive Evaluation and Cuckoo Search-Support Vector Regression (CS-SVR), which is named as Fuzzy Comprehensive CS-SVR (FCCS-SVR), is further proposed. The result of case analysis reflects that the state evaluation of the radar system is realized. The system performance analysis shows that the use of FCCS-SVR evaluation method provides a high recognition rate and can accurately assess the health status of the radar system. [ABSTRACT FROM AUTHOR]

This paper constructed a stacked-autoencoder neural network model (SAE model) to estimate sea state bias (SSB) based on radar altimeter data. Six cycles of the geophysical data record (GDR) from Jason-1/2 radar altimeters were used as a training dataset, and the other 2 cycles of the GDR from Jason-1/2 were used for testing. The inputs to this SAE model include the significant wave height (SWH), wind speed (U), sea surface height (SSH), backscatter coefficient (σ0) and automatic gain control (AGC), and the model outputs the SSB. The model includes one input layer, three hidden layers and one output layer. The SSBs in the GDR of Jason-1/2 were obtained from a nonparametric model based on the SWH and U as input variables; thus, the model has high accuracy but low efficiency. The SSBs in the GDR of HY-2A were computed using a four-parameter parametric model that uses the SWH and U as input variables; therefore, this model’s computational speed is high but its accuracy is low. Thus, we used the HY-2A radar altimeter as an unseen validation dataset to evaluate the performance of the SAE model. Then, we analyzed the contrasting results of these methods, including the differences in the SSB, explained variance, residual error and operational efficiency. The results demonstrate not only that the accuracy of the SAE model is superior to that of the conventional parametric model but also that its operational efficiency is better than that of the nonparametric model. [ABSTRACT FROM AUTHOR]