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Existing sea surface small target detection methods typically rely on intricate feature extraction techniques on transformed radar returns. However, these approaches suffer from issues of high computational complexity and low real-time performance. Temporal Convolutional Network (TCN) can enable direct processing of radar time-series echo data without the need for elaborate feature extraction, thus substantially improving computational efficiency. Building upon this, this paper presents a novel target detection algorithm based on Multi-layer Attention Temporal Convolutional Network (MA-TCN). The proposed algorithm processes the amplitude information in the original echo signals, and comprehensively extracts sequence feature information through the construction of stacked residual modules. Additionally, it integrates multi-layer attention mechanisms to adaptively adjust the output weights of each residual module, thereby further enhancing detection accuracy. Experimental results demonstrate that the proposed approach achieves significant improvements in both detection performance and efficiency compared to existing methods. [ABSTRACT FROM AUTHOR]

The shipborne High-Frequency Hybrid Sky-Surface Wave Radar integrates a sky-wave transmitting channel and a ground-wave receiving channel on a shipborne platform. This hybrid radar system combines a skywave source with the added flexibility of a far-away shipborne radar. Ionospheric stratification and height uncertainty introduce uncertainties in the sky-wave channel, resulting in multiple measurements of one target. Additionally, the shipborne platform position is affected by sea state, causing errors in azimuth accuracy setting and subsequently reducing target tracking precision. In this paper, we propose for the first time a target tracking method that combines ionospheric variations with the motion of a shipborne platform. It introduces the variational Bayesian method into the multiple detection mode, which solves the effects of ionospheric altitude error and orientation error of shipborne platforms due to different sea states on target tracking. Simulation experiments validate the effectiveness of the proposed method. Therefore, the proposed method promises advancements in shipborne radar systems for maritime surveillance applications. [ABSTRACT FROM AUTHOR]

This review is based on the radar cross-section (RCS) reduction techniques, with the prominence of the metasurface and its design. The thorough detail starts with introducing the concepts of the RCS and its reduction techniques. Then, traditional and recent techniques are provided to reduce RCS, emphasizing the scattering and absorption methods. An in-depth analysis of shaping processes and Radar Absorbing Materials (RAM) is provided, which are two standard RCS reduction techniques. It also thoroughly analyses the metamaterial-based RCS reduction, considering both active and passive configurations. The primary aspects covered regarding the utilization of metasurfaces for reducing RCS include profile, conformality, polarization sensitivity, and design complexity. To provide comparison and evaluation, the review includes tabulated information on performance parameters such as the method used, frequency range, size, optimization method, and planar or conformal design of RCS reduction. Additionally, the paper identifies areas within the field that require further in-depth research and investigation. [ABSTRACT FROM AUTHOR]

We propose a 10‐GHz 2 × 2 phased‐array radio frequency (RF) receiver with an 8‐bit linear phase and 15‐dB gain control range using 65‐nm complementary metal–oxide–semiconductor technology. An 8 × 8 phased‐array receiver module is implemented using 16 2 × 2 RF phased‐array integrated circuits. The receiver chip has four single‐to‐differential low‐noise amplifier and gain‐controlled phase‐shifter (GCPS) channels, four channel combiners, and a 50‐Ω driver. Using a novel complementary bias technique in a phase‐shifting core circuit and an equivalent resistance‐controlled resistor–inductor–capacitor load, the GCPS based on vector–sum structure increases the phase resolution with weighting‐factor controllability, enabling the vector–sum phase‐shifting circuit to require a low current and small area due to its small 1.2‐V supply. The 2 × 2 phased‐array RF receiver chip has a power gain of 21 dB per channel and a 5.7‐dB maximum single‐channel noise‐figure gain. The chip shows 8‐bit phase states with a 2.39° root mean‐square (RMS) phase error and a 0.4‐dB RMS gain error with a 15‐dB gain control range for a 2.5° RMS phase error over the 10 to10.5‐GHz band. [ABSTRACT FROM AUTHOR]

The increasing role of unmanned aerial vehicle (UAV) swarms in modern warfare poses a significant challenge to ground and air defense systems. Considering complex terrain environments and multi-sensor resources including radar and photoelectric systems constraints, a novel multi-sensor dynamic scheduling algorithm is proposed in this paper. Firstly, a transmission model with Fresnel zone under complex terrain and sensor models for radar/photoelectric systems are established. Considering the constraints of 6 factors, such as pitch angle, array scanning angle and threat levels, a detection model is developed subsequently. Secondly, to meet the real-time requirements of ground and air defense systems, a fast calculation method for Fresnel zone clearance using adaptive buffer is achieved. Thirdly, an improved Hungarian algorithm is proposed to solve the combinatorial optimization problem of sensor scheduling. Finally, simulation experiments are conducted to evaluate the algorithm performance under different conditions. The results demonstrate that the proposed approach significantly reduces the sensor switching rate while achieving a high sensor-UAV matching rate and high-threat matching rate. Furthermore, the simulation results verify the effectiveness of the proposed algorithm when applied to multi-sensor scheduling for defending UAV swarms. • Modern militaries need advanced sensor scheduling solutions to defend UAV. • Considering the Fresnel zone under complex terrain is crucial. • Adaptive buffer enables rapid calculation of Fresnel zone clearance. • Multiple sensor constraints make scheduling problems realistic. • Improved algorithm effectively enhances matching rates while reducing switching. [ABSTRACT FROM AUTHOR]

Climate change has caused fundamental changes in the pattern of rainfall worldwide. Climate change can alter precipitation patterns, consequently intensifying the frequency and severity of flash floods in specific regions, including Iran. It is important for communities to be prepared for these events and to take steps to mitigate their impact. Full control or damage management of the resulted floods through structural measures is not always feasible due to economic, technological, environmental and social limitations. Therefore, often non-structural measures play an important role in reducing probable damages and casualties. The significance of advanced systems for both short- and long-term flood forecasting cannot be overstated. In this article, short-term flood prediction model is discussed using Ensemble Prediction Systems (EPSs) Machine Learning algorithms (ML) and HEC-HMS hydrological model. Also, in order to achieve high accuracy in the assessment of flood-damaged areas, remote sensing techniques have been used. The results show that the use of EPS improves the speed and accuracy of the daily prediction model (R2 = 0.8). Also, with the use of Sentinel-1 radar satellite images and the simultaneous use of supervised learning algorithms, a suitable estimate of the evaded area has been made for seven selected floods in the Kan basin, which is a mountainous region in the north of Tehran, in 2015–2022 period. [ABSTRACT FROM AUTHOR]

Aiming at the quantitative evaluation requirements of radar second-order intermodulation false alarm (SIFA) effect, a radar SIFA effect model is established from the field-circuit coupling mechanism, and the parameter test method of the model is given. Taking a certain type of radar as the test object, the SIFA effect test is carried out by using the method of electromagnetic injection equivalent substitution irradiation. The results show that the tested radar will produce a SIFA signal higher than the selected sensitive level when the frequency difference of dual-frequency electromagnetic interference (EMI) is within 3 MHz and the frequency offset is within ±200 MHz. Using the model parameters of the SIFA interference effect measured in the experiment, it is assumed that they do not change with the interference field strength. Combined with the SIFA interference field strength of the tested radar and the single frequency blocking critical interference field strength, the effect model can evaluate the degree of radar SIFA interference. [ABSTRACT FROM AUTHOR]

Interrupted sampling repeater jamming (ISRJ) is an advanced form of coherent jamming, and the suppression of this jamming has become a critical problem for modern radar electronic countermeasures. In this paper, we propose a countermeasure based on the linear interpulse frequencycoding linear frequency modulation (LIFC-LFM) signal. The LIFC refers to the linear encoding of the frequency of each pulse transmitted by the radar system, which can change the distribution of the false targets formed by ISRJ in the range-Doppler (RD) spectrum. In this context, we design the frequency coding value to effectively separate the true and false targets in the RD spectrum. Furthermore, we propose a fast-time phase compensation method to separate the true and false targets in the Doppler dimension to facilitate false target suppression. Finally, ISRJ can be suppressed by oblique projection processing. Simulation examples demonstrate that the proposed method has an excellent and robust ISRJ suppression effect for direct forwarding ISRJ, repeated forwarding ISRJ, and frequency shifting ISRJ. Meanwhile, the signal-to-noise ratio loss caused by the jamming suppression is small. [ABSTRACT FROM AUTHOR]

Ensuring the safety and operational efficiency of hydraulic infrastructures is paramount, considering the widespread consequences that damages can inflict on people, communities, and the environment. To mitigate risks and prevent significant losses, continuous surveillance is vital. While some damages might appear minor, they can jeopardize the complete operational reliability of dams, leading to substantial economic losses, especially in energy production and related activities. The rapid growth in 20th-century infrastructure development globally has made security monitoring a necessity for numerous civil structures. Rigorous inspection programs, particularly for reservoir dams, are essential for safeguarding citizens and their properties. However, individually monitoring each dam is often impractical due to the associated costs and time constraints, potentially posing safety risks. Fortunately, satellite-based differential radar interferometry (DInSAR) offers an effective and cost-efficient remote sensing solution. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques, particularly utilizing Persistent Scatterers, have proven successful in monitoring various infrastructures, natural phenomena, and geological activities. MT-InSAR provides precise measurements without the need for fieldwork, utilizing historical SAR image archives dating back to the 1990s. Technological advancements, such as the Sentinel-1 C-band with a six-day revisit time until the end of 2021, have enhanced monitoring capabilities. Additionally, commercial radar images in the X-band and the development of multi-interferometric InSAR techniques have opened new avenues for monitoring. This study showcases the adaptation and application of MT-InSAR for monitoring dams and large ponds constructed with loose materials. By assessing vertical displacements and consolidation rates, the technique identifies potential issues, aiding in further field investigations. Case studies involving dams and large reservoirs in Andalusia illustrate the effectiveness of satellite radar interferometry in monitoring their structural stability from space as a routine practice. [ABSTRACT FROM AUTHOR]

A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using the Differential Interferometric Synthetic Aperture Radar (DInSAR) processing technique was developed and tested. Among the several factors that affect DInSAR's precision, such as temporal decorrelation and atmospheric noise, it was observed that seasonal spatial-temporal variability in radar penetration depth is the greatest contributor to the loss of precision of the long-term deformation rate measurement. This spatial-temporal signal is described for a specific region in the northern US; however, it is widely observed in multiple regions. In these regions, a gradual penetration depth increase is observed in fall and winter, and an abrupt penetration depth decrease is observed in spring. The latter effect is likely caused by rapid melting of ice and snow that, at the same time, significantly reduces interferometric coherence. Within a specific geographic region, radar penetration depth spatially varies proportionally to the elevation due to an elevation-dependent temperature gradient, producing artifacts that correlate well with the topography. This signal does not extend to high-elevation regions (above ~2500 m) that do not experience seasonal thawing, which allows for distinguishing it from the topography-dependent atmospheric signals. Without accounting for the spatial-temporal variability in radar penetration depth, deformation time series in these areas can contain either an annual periodic signal with an amplitude of over 0.1 m or, if the abrupt penetration depth decrease produced by melting is not resolved due to lower coherence, an erroneous interannual trend. Spatial-temporal variability in radar penetration depth needs to be better understood and corrected to improve the precision of long-term deformation rate products, particularly in regions susceptible to seasonal freezing and thawing. [ABSTRACT FROM AUTHOR]

Equivalent‐time sampling can improve measurement or sensing systems because it enables a broader frequency band and higher delay resolution for periodic signals with lower sampling rates than a Nyquist receiver. Meanwhile, a digital down‐conversion (DDC) technique can be implemented using a straightforward radio frequency (RF) circuit. It avoids timing skew and in‐phase/quadrature gain imbalance instead of requiring a high‐speed analog‐to‐digital converter to sample an intermediate frequency (IF) signal. Therefore, when equivalent‐time sampling and DDC techniques are combined, a significant synergy can be achieved. This study provides a parameter design methodology for optimal equivalent‐time sampling using DDC. [ABSTRACT FROM AUTHOR]

We propose a robust super-resolution algorithm for vital sign radar in a low signal-to-noise ratio (SNR) environment. Conventional approaches, such as fast Fourier transform and super-resolution based algorithms, suffered to provide reliable results due to the limited data length and high noise level. To overcome these limitations, our proposed algorithm utilizes a low-complexity least mean square (LMS) filter and relaxation (RELAX) techniques to achieve robust performance in low SNR environments. To evaluate the effectiveness of our algorithm, we conducted both simulation and experimental studies. Our results show that the proposed method significantly outperforms conventional methods, with Monte-Carlo simulations of respiration and heartbeat achieving an RMSE approximately 7 and 120 times lower than that of the conventional method, respectively. Overall, our algorithm provides a promising solution for robust vital sign detection in challenging low SNR environments [ABSTRACT FROM AUTHOR]

Weak target detection is a great challenging in radar field. To detect weak targets via radar communication integrated signal, a method for constructed step-LFM (CStep-LFM) signal is proposed based on quadrature amplitude modulation-orthogonal frequency division multiplexing (QAM-OFDM) echoes. First, a QAM-OFDM and CStep-LFM model is established, and then a bandpass filters bank is used to transform the QAM-OFDM echo into a Step-LFM signal. Finally, compared with several existing integrated signals, the analyses and the simulation results show that CStep-LFM signal not only improves the detection performance of weak targets but also has a better communication performance. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the performance of Multicarrier Phase Coding (MCPC) and P4-encoded waveforms. Researchers explored the unique properties of these signals, focusing on aspects like phase distribution, autocorrelation, power spectral density for P4 encoding, and aperiodic autocorrelation and ambiguity function for MCPC signals. The findings identified optimal MCPC sequences with reduced peak-to-mean envelope power ratios (PMEPR), improving signal performance. Complementary codes based on permutation were also generated and analyzed for MCPC sequences. The study utilized an improved genetic algorithm to develop new and improved waveforms, underscoring the importance of techniques like optimal sequence permutation, complementary sequences, and classical window frequency weighting in enhancing signal performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the automated extraction of textural features from segmented Sentinel-1A Synthetic Aperture Radar (SAR) imagery, which was captured on 25th August 2017. Textural properties play a pivotal role in discerning regions of interest in satellite imagery, essential for precise classification in diverse applications, including remote sensing, medical image analysis, biometric analysis, and document image analysis. To achieve this, the Local Adaptive Threshold (LAT) technique was employed for segmenting the foreground and background areas of the pre-processed vertical transmit-vertical receive (VV) polarized Sentinel-1A SAR data. The approach relies on second-order statistics, specifically co-occurrence measures, which evaluate the relationships between pairs of pixels within their local neighbourhood's in the input image. Four fundamental second-order statistical measures, namely correlation, energy, homogeneity, and contrast, were computed using the Grey Level Co-occurrence Matrix (GLCM). The GLCM was generated by quantizing the foreground region of the segmented grey-scale image into different levels, including 8, 16, and 64 grey levels, considering two inter-pixel distances, d=1 and d=2, and adopting an omni-directional orientation (θ = 00, 450, 900, 1350).The results underscore the effectiveness of the GLCM-based approach in computing second-order statistical texture measures from the segmented SAR image. Notably, the findings highlight that quantizing the image to Ng=8 with d=1 and considering omni-directional statistical measures (θ = 00, 450, 900, 1350) significantly enhances performance. [ABSTRACT FROM AUTHOR]

This article discusses several aspects related to modeling the impact of offshore wind farms on maritime radar systems operating in the X and S bands. The first part of the paper focuses on theoretical models and analyses, taking into account radio shadowing and false radar echoes. Additionally, the issue of spatial modeling of wind turbines, with diffraction phenomena considered, is reviewed with the help of suitable propagation models. By relaying on a software-based implementation of the proposed model, the authors carried out a detailed simulation of the impact of wind turbines on radar systems operating in the X and S bands. The results of these simulations are presented and discussed in the second part of the article. [ABSTRACT FROM AUTHOR]

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles. [ABSTRACT FROM AUTHOR]

This paper demonstrates the feasibility of reducing radar cross-section by employing resistive sheets or rings in the conducting elements of an FSS unit cell. The idea behind the approach in question is to create power-absorbing elements which may help reduce the power reflected from FSS surface. The investigated FSS unit cells have the form of double-closed rings and double-closed-split rings. A carbon paste, serving as the resistive layer, was inserted in various regions within the unit cell. The CST Microwave Studio software was used to obtain the reflection coefficient. Specific dimensions and conductivity of the paste were selected to ensure better performance. Simulation results showed that the reflection coefficient may be reduced by 8 dB, to 14 dB, by using carbon paste with the conventional copper layer. [ABSTRACT FROM AUTHOR]

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification. [ABSTRACT FROM AUTHOR]

Forward scattering radar has been widely used to classify human movements. To reduce concern of prolonged radiation exposure, Wi-Fi based passive scattering radar with bistatic topology is proposed. The movements include walking, bending, sitting, and kneeling. The enveloping algorithm applied to Wi-Fi based passive forward scattering radar signals has produced a comparable pattern to the ones from the active configuration. The features extracted from the frequency spectrum are also similar for both radar topologies. These led to comparable performance when classifying using artificial neural network. Hence, WiFi can effectively reduce the cost of radar sensors. [ABSTRACT FROM AUTHOR]

Self-propelled agricultural sprayer operators work an average of 15 h d-1 in peak season, and steering is the task that causes the operator the most stress because of the large number of stimuli involved. Automatic guidance systems help reduce stress and fatigue for operators by allowing them to focus on tasks other than steering. Physiological signals like skin conductance (electrodermal activity, EDA) change with stress and can be used to identify stressful events. The objective of this study was to determine if using a commercially available vision and radar guidance system (VSN®, Raven Industries) reduces agricultural sprayer operators' stress compared to when they are steering manually. Four male professional sprayer operators participated in this study. Each operator performed his job duties normally in GPS-guidance-planted fields, at his self-selected speed, except to drive some passes manually and others with VSN in the same field. EDA was measured with an Empatica E4 wristband, and stressful events were quantified. Machine data (e.g., speed, RTK-GPS, and VSN metrics) were collected from each sprayer via CAN logs. The steering type, stress rate (e.g., stressful events min-1), and steering performance (crosstrack error standard deviation, XTE SD) were determined for each pass. In total, 51 passes (23 manual, 28 VSN) in six fields were analyzed. Operators using VSN had a significant reduction (48% lower, p < 0.001) in their stress rate compared to when they were steering manually. There was no significant difference in the XTE SD for the steering type. The use of an automatic guidance system such as VSN could have a dramatic positive effect on the health of sprayer operators, especially during the long workdays of the peak spraying season, and could reduce the negative effects that stress and fatigue have on steering performance, mistakes, and accidents. [ABSTRACT FROM AUTHOR]

This document provides a summary of various products and technologies featured in the Chemical Engineering Progress journal. These include a sodium-sulfur battery with low degradation rate, pressure control valves for precise process control, radar sensors for rugged conditions, wastewater processing meters for real-time measurements, alkalinity analyzers for wastewater pH management, circuit simulation models for accurate simulations, automated sensor cleaning systems for maintaining sensor accuracy, vacuum pumps for hazardous applications, beer foam separators to reduce water waste, and feeding devices for medium capacity dosing. These products offer innovative solutions for a range of industrial and environmental challenges. [Extracted from the article]

Radar technologies reserve a large latent capacity in dealing with human motion recognition (HMR). For the problem that it is challenging to quickly and accurately classify various complex motions, an HMR algorithm combining the attention mechanism and convolution neural network (ECA-CNN) using vital signs is proposed. Firstly, the original radar signal is obtained from human chest wall displacement. Chirp-Z Transform (CZT) algorithm is adopted to refine and amplify the narrow band spectrum region of interest in the global spectrum of the signal, and accurate information on the specific band is extracted. Secondly, six time-domain features were extractedfor the neural network. Finally, an ECA-CNN is designed to improve classification accuracy, with a small size, fast speed, and high accuracy of 98%. This method can improve the classification accuracy and efficiency of the network to a large extent. Besides, the size of this network is 100 kb, which is convenient to integrate into the embedded devices. [ABSTRACT FROM AUTHOR]

This paper is concerned with the measurement outlier-resistant mobile robot localization problem by using multiple Doppler-azimuth radars under round-robin protocol (R-RP). In the considered robot localization system, multiple Doppler-azimuth radars are equipped on the robot platform to produce the measurement including the Doppler frequency shift and the azimuth. In order to assuage communication link congestion, the R-RP is used. For mitigating the influence of outliers, a time-varying state estimator is constructed which contains a saturation function with variable saturation levels. This paper aims at seeking out a practicable yet effective solution to the addressed robot localization problem by devising the constructed estimator which can assure that, over a finite horizon, the localization error satisfies the given H ∞ performance index. By constructing an appropriate Lyapunov function, the sufficient condition, which can guarantee the localization error to fulfill the given H ∞ performance, is established. Then, by resorting to the solution to a set of linear matrix inequalities, the constructed estimator can be devised. In the light of the estimator design strategy proposed in this paper, the corresponding robot localization algorithm is developed. At last, some simulations are conducted to testify the usefulness of the developed robot localization algorithm. • The measurement outlier and the round-robin protocol are considered simultaneously for the robot localization problem for the first time. • A discrete state estimator containing a saturation function with the variable saturation level is constructed. • By using Lyapunov theory and the RLMI method, the estimator is designed. • The corresponding measurement outlier-resistant robot localization algorithm is given. [ABSTRACT FROM AUTHOR]

In this study, a short-range mmWave radar is proposed for obtaining non-contact, non-invasive, and high-accuracy measurements of multiple vital signs. The system can extract the heart rate (HR), respiration rate (RR), and blood pressure (BP). A compact radar sensing unit operating at 122 GHz was used to transmit and receive signals reflected off a patient's chest under breathing and non-breathing (breath-holding) conditions. Algorithms were developed for extracting the BP values. The BP values were determined as 129/80 and 129/83 for the breathing and non-breathing signals, respectively. Furthermore, the extracted data were used to calculate the HR and RR based on the period of each data cycle and a fast Fourier transform (FFT). Both techniques yielded an RR of 12 breaths per minute. The HR values for breathing and non-breathing were 68 and 53 beats per minute, respectively. Furthermore, the HRs computed from the FFT of the breathing and non-breathing signals were 55 and 53 beats per minute, respectively. All values were consistent with the reference values. Thus, the system shows feasibility for low-cost, highly accurate, and continuous patient monitoring. [ABSTRACT FROM AUTHOR]

Given the recent surge in severe weather phenomena, the development of reliable computational resources that could assist meteorologists in issuing weather alerts is of great importance. To this end, deep learning models are being intensely studied for developing short-term weather forecasting tools using radar data. A drawback of these approaches, however, is the fact that their predictions tend to be blurry. In this paper, we aim to improve the visual quality of the predicted radar maps by introducing a new approach for designing perceptual losses which uses autoencoders as feature extractors. Compared to perceptual losses using VGG networks, our proposed approach uses fewer computational resources and no additional annotations or data sets. Experiments on radar reflectivity data collected by MET Norway show that the proposed method is able to provide visually sharper predictions than related work loss functions, while achieving an improvement of up to 6.45% in terms of root-mean-square error and of up to 17.65% in terms of false alarm ratio. [ABSTRACT FROM AUTHOR]

This paper proposes a corner reflector and Luneburg ball reflector group. The omnidirectional radar cross-sectional (RCS) distribution characteristics of a fighter are simulated using the sharp and smooth RCS distribution features of the corner and the Luneburg ball reflectors, respectively. A new type of Doppler signal generation principle is proposed to design a Doppler frequency simulator to transmit frequency signals by connecting in parallel with the metal layer of the corner reflector and the Luneburg sphere ball, and then transmit through their other end. The existing radar target aircraft cannot simulate the RCS and speed of the targets that are less than 0.005 m² by enhancing the echo intensity of the target location, which makes it impossible for the military to conduct practice drills and evaluate the effectiveness of the air defense systems. The experimental results show that the Doppler frequency simulator successfully simulates the target speed of 0-80 km/h and when the speed is greater than 20 km/h, the error of the simulation frequency is less than 1.5%. The proposed method can provide guidance and a theoretical basis for simulating the speed of various types of aircraft in future work. [ABSTRACT FROM AUTHOR]

In this paper, a novel coherent integration algorithm, i.e., two-dimensional frequency domain secondorder keystone transform (FDSOKT), is proposed to detect a weak maneuvering target based on bistatic radar configuration. To eliminate range migration and Doppler frequency migration, the radar echoes are transformed into two-dimensional frequency domain firstly, and then a series of rescaling transforms, matched filter functions and compensation functions are performed respectively. With the elimination of the couplings between range frequency and azimuth frequency caused by radial velocity and acceleration, the energy of the echoes is focused in twodimensional time domain, which improves the detection performance of weak target. In addition, to deal with Doppler ambiguity, different Doppler ambiguity cases are discussed and could be solved well. At last, some simulation experiments are provided and the effectiveness of FDSOKT is proved by the results. [ABSTRACT FROM AUTHOR]

Scattering characteristic extraction is an essential part of manmade target recognition. However, if two scattering points are in adjacent pixels, scattering characteristic extraction may fail to acquire accurate polarimetric scattering matrices (PSMs) of the weak scattering points due to the contamination caused by the strong scattering points. Target null theory provides a way to solve this problem. By selecting the transmitting and receiving polarization states of radar antennas simultaneously, the echo power of a strong scattering point becomes zero and the contamination effect is avoided. In this paper, a method based on target null theory for scattering characteristic extraction is proposed. First, we optimize the transmitting and receiving polarization states of the radar antenna to suppress the intensities of the strong scattering points to highlight the positions of the weak scattering points in certain polarimetric channels. Second, to suppress the contamination effects of strong scattering points in other polarimetric channels, we establish perturbation correction equations to erase the error generated by the point spread function (PSF) among adjacent scattering points in the radar image. Finally, the solved polarimetric scattering matrices of corresponding positions are implemented for target retrieval. The electromagnetic simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology. • This paper presents a novel framework to settle dynamic path planning problem for stealth unmanned aerial vehicles. • A modified A-Star algorithm that adopts the bidirectional sector variable step search strategy. • The minimal radar cross-section tactics are convenient to deal with the bandit threats and enhance real-time performance. • The combinatorial methodology has better superiority in calculation efficiency, path cost and safety. [ABSTRACT FROM AUTHOR]

Millimeter wave applications gives an efficient solution to furnish peak data rates in indoor wireless access systems. Propagation of the signal will get up-shot because of few considerations in advanced communication appliances. Superior output and high directivity antennas are mandatory to overcome these consequences. By installing unequal slots in the structure, the tree-shaped unequal slotted antenna is analyzed for the variations in impedance, directivity changes and reflection coefficient. The better quality results have been observed for the proposed structure while compare with existing structures. The maximum simulation reflection coefficient(S11) of tree-shaped unequal slotted antenna is 45.12 dB, directivity is 6.54 dBi with the efficiency of 60 % and at 60 GHz, and the measurement value of 36.35 is observed which covers the complete RADAR applications which have an unlicensed bandwidth. The dimension of tree-shaped unequal slotted antenna is observed as 12*7*0.508 mm3. The proposed tree-shaped unequal slotted antenna is well suited for millimeter-wave appliances such as Microcell, Femtocell and macrocell applications because of its good bandwidth and less dimension. [ABSTRACT FROM AUTHOR]

This study developed a novel S-band radar system for planar location applications. High-resolution range imaging and target angle estimation were achieved by using a stepped frequency continuous wave (SFCW) signal and single input multiple output (SIMO) architecture with a linear sparse array layout, respectively. An improved time-frequency method was utilized to link the independent range profile and angle spectrum results to obtain the plane positions of the targets. The radar hardware was composed of the antenna array with one transmit element and five receive elements, an RF transceiver, and a signal processing component. Under the proposed waveform parameters and signal processing scheme, a 16-ms process cycle, 0.3-m ranging error, and 0.4° angle estimation error for target positioning were achieved in field tests. These results demonstrate the effectiveness and advantages of the proposed radar system. [ABSTRACT FROM AUTHOR]

The increasing trend in air traffic density will continue in the near future with the addition of different aerial vehicles. Before the Mode-S protocol, Mode A and Mode C were in use; however, the Mode A/C configuration was only usable in sparsely dense air traffic. One of the useful features of Mode-S is the ability of probabilistic interrogation. However, there has not yet been a sophisticated algorithm for many close aircraft. Considering a futuristic air environment with a swarm of drones and airbuses equipped with transponders, we utilized the probabilistic interrogation feature of Mode-S and designed an algorithm. The proposed algorithm is able to collect close aircraft information in a relatively short time. There has also been created a high-level Mode-S uplink and downlink communication simulator in order to exchange all-call communication and record the algorithm’s performance in terms of time and number of interrogations sent. [ABSTRACT FROM AUTHOR]

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Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction-finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre-level localization accuracy, which is necessary when developing location-based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE 5.1's constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre-level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre-level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW's performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle-of-arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW- Lite and BmmW- Lite+ , both tailored for single-antenna BLE devices, which eliminates the necessity for bulky and expensive multi-antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle-of-arrival info), BmmW- Lite uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 36 cm, thus facilitating precise object tracking in indoor environments even when using budget-friendly single-antenna BLE devices. BmmW- Lite+ extends BmmW- Lite by allowing the localization task to be transferred from the edge to the cloud due to device memory and power constraints. To this end, BmmW- Lite+ employs a goal-oriented communication paradigm that compresses initial BLE features into a more compact semantic format at the edge device, which allows to minimize the amount of data that needs to be sent to the cloud. Our experimental results show that BmmW- Lite+ can compress raw BLE features by up to 12% of their initial size (hence allowing to save network bandwidth and minimize energy consumption), with negligible impact on the localization accuracy. [ABSTRACT FROM AUTHOR]

Self-propelled agricultural sprayer operators work an average of 15 h d-1 in peak season, and steering is the task that causes the operator the most stress because of the large number of stimuli involved. Automatic guidance systems help reduce stress and fatigue for operators by allowing them to focus on tasks other than steering. Physiological signals like skin conductance (electrodermal activity, EDA) change with stress and can be used to identify stressful events. The objective of this study was to determine if using a commercially available vision and radar guidance system (VSN®, Raven Industries) reduces agricultural sprayer operators' stress compared to when they are steering manually. Four male professional sprayer operators participated in this study. Each operator performed his job duties normally in GPS-guidance-planted fields, at his self-selected speed, except to drive some passes manually and others with VSN in the same field. EDA was measured with an Empatica E4 wristband, and stressful events were quantified. Machine data (e.g., speed, RTK-GPS, and VSN metrics) were collected from each sprayer via CAN logs. The steering type, stress rate (e.g., stressful events min-1), and steering performance (crosstrack error standard deviation, XTE SD) were determined for each pass. In total, 51 passes (23 manual, 28 VSN) in six fields were analyzed. Operators using VSN had a significant reduction (48% lower, p < 0.001) in their stress rate compared to when they were steering manually. There was no significant difference in the XTE SD for the steering type. The use of an automatic guidance system such as VSN could have a dramatic positive effect on the health of sprayer operators, especially during the long workdays of the peak spraying season, and could reduce the negative effects that stress and fatigue have on steering performance, mistakes, and accidents. [ABSTRACT FROM AUTHOR]

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In light of the increasing requirement for the electromagnetic spectrum, the integration of radar and communication is widely concerned because of its miniaturizing equipments and high efficiency of spectrum. To address the issue that the communication information in integration signal for radar and communication affects its detection performance. A novel integration signal is proposed in this paper. Inspired by the high communication efficiency of shaped octal phase-shift keying (S8PSK) and high spectral efficiency of the three-section integration waveform (k-CPM-LFM), we generate a new type of modulation h-CPM by introduction of a precoding method with low complexity and a time-varying modulation index h, which is used to encode communication data into LFM radar waveform to form a novel integration waveform h-CPM-LFM. Numerical results show that the designed waveform is at least 10 dB less spectrum extension than other integration waveforms when carrying large amounts of communication information and has excellent BER performance under the condition of strong out-of-band interference. Ambiguity function analysis shows that the waveform has excellent detection performance comparable to LFM. [ABSTRACT FROM AUTHOR]

Considering the increased popularity for backcountry mountain recreation activities, potentially problematic snowpack interfaces are currently of great interest given their impact on snow stability. The identification of interface vertical locations and spatial variability in the snowpack is essential for avalanche danger forecasting. The Gaspé Peninsula specific climate often leads to a complex snowpack development, where the need of improved monitoring is important. The goal of this research is to assess an automated method to detect contrasted snow interfaces using a 24 GHz Frequency Modulated Continuous Wave (FMCW) portable radar. Based on different in-situ configurations, we compared the radar amplitude signals with in-situ snow geophysical measurements, including SnowMicroPenetrometer (SMP). Radar measurements have been done following two different protocols: (1) mobile radar looking-up and down in order to understand the radar-snow wave interactions and optimize its parameters for spatial variability assessment of contrasted snow layers and (2) fixed radar looking-up to evaluate its potential in monitoring snow stratigraphy temporal variability. Results show good agreements with compared validation data with 80% of manually identified interfaces detection and a vertical positioning error of 3 cm. The presented FMCW radar appears to have a good potential for spatial and temporal variability assessment of snowpack stratigraphy. [ABSTRACT FROM AUTHOR]

A concept with low-flying UAVs is suggested, that can significantly increase the radio network capacity of a mobile tactical network. Since flying relays can have free line-of-sight to each other, a capacity increase in the radio network is possible without range limitations. The main purpose for placing the UAVs at a low altitude is to make them difficult to detect at a distance, by radar, signal surveillance or responsive jammers. From this aspect, a position just above the tree tops is preferable. In the present study, such a concept was analyzed in six snapshots from the Anglova scenario, showing that a limited number of UAVs at low altitudes makes it possible to use 10 times the link data rates commonly used today and still achieve a connected network. Based on the results, it is apparent that for the evaluated snapshots it is possible to connect the network at high data rates with a fairly small UAV network, around a tenth of the size of the ground network, flying at tree-top altitude (25 m). [ABSTRACT FROM AUTHOR]

Timed array technology is rapidly evolving in multiple areas such as high resolution imaging radar, automotive, medical, high data rate communication applications etc. Timed arrays by utilising True Time Delay (TTD) lines in place of phase shifters mitigate beam squint and pulse dispersion issues associated with wide instantaneous bandwidth arrays. This paper presents on review of evolutionary trends in TTD line architectures starting from coaxial cable to photonic integrated circuit. The paper also reports on critical parameters of TTD lines, their importance and implication in design of typical X-band imaging radar. Comparison of different TTD line architectures in terms of configuration, implementation, merits and demerits are discussed in detail for wideband array application. The paper also brings out the integration aspects of TTD lines as part of T/R modules and proposes suitable design schemes towards performance optimization and realisation of timed arrays. [ABSTRACT FROM AUTHOR]

Real radar data often consist of a mixture of Gaussian and non-Gaussian clutter. Such a situation creates one or more inexion points in the curve of the empirical cumulative distributed function (CDF). In order to obtain an accurate fit with sea reverberation data, we propose, in this paper, a trimodal gamma disturbance model and two parameter estimators. The non-linear least-squares (NLS) fit approach is used to avoid computational issues associated with the maximum likelihood estimator (MLE) and moments-based estimator for parameters of the mixture model. For this purpose, a combination of moment fit and complementary CDF (CCDF) NLS fit methods is proposed. The simplex minimization algorithm is used to simultaneously obtain all parameters of the model. In the case of a single gamma probability density function, a zlog(z) method is derived. Firstly, simulated life tests based on a gamma population with different shape parameter values are worked out. Then, numerical illustrations show that both MLE and zlog(z) methods produce closer results. The proposed trimodal gamma distribution with moments NLS fit and CCDF NLS fit estimators is validated to be in qualitative agreement with diérent cell resolutions of the available IPIX database. [ABSTRACT FROM AUTHOR]

Blood pressure is one of the key physiological parameters for determining human health, and can prove whether human cardiovascular function is healthy or not. In general, what we call blood pressure refers to arterial blood pressure. Blood pressure fluctuates greatly and, due to the influence of various factors, even varies with each heartbeat. Therefore, achievement of continuous blood pressure measurement is particularly important for more accurate diagnosis. It is difficult to achieve long-term continuous blood pressure monitoring with traditional measurement methods due to the continuous wear of measuring instruments. On the other hand, radar technology is not easily affected by environmental factors and is capable of strong penetration. In this study, by using machine learning, tried to develop a linear blood pressure prediction model using data from a public database. The radar sensor evaluates the measured object, obtains the pulse waveform data, calculates the pulse transmission time, and obtains the blood pressure data through linear model regression analysis. Confirm its availability to facilitate follow-up research, such as integrating other sensors, collecting temperature, heartbeat, respiratory pulse and other data, and seeking medical treatment in time in case of abnormalities. [ABSTRACT FROM AUTHOR]

Detecting risky driving has been a significant area of focus in recent years. Nonetheless, devising a practical, effective, and unobtrusive solution remains a complex challenge. Presently available technologies predominantly rely on visual cues or physical proximity, complicating the sensing. With this incentive, we explore the possibility of utilizing mmWave radars exclusively to identify dangerous driving behaviors. Initially, we scrutinize the attributes of unsafe driving and pinpoint distinct patterns in range-doppler readings brought about by nine common risky driving manoeuvres. Subsequently, we create an innovative Fused-CNN model that identifies instances of hazardous driving amidst regular driving and categorizes nine distinct types of dangerous driving actions. After conducting thorough experiments involving seven volunteers driving in real-world settings, we note that our system accurately distinguishes risky driving actions with an average precision of approximately 97% with a deviation of ± 2 %. To underscore the significance of our approach, we also compare it against established state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The increasing use of Global Positioning System (GPS)-based autonomous drones in various civilian and military applications has raised concerns about malicious or unintentionally harmful activities that can be carried through them. It is necessary to detect these intruding drones within protected areas and prevent their unauthorized access by denying them entry. We propose a framework that combines the detection of intruding drones using an L-band radar and then counters by transmitting fake GPS coordinates toward the drones, effectively redirecting them. This article explains the setup required to add to an existing monostatic radar that provides two-dimensional information, i.e., range and azimuth information, to enable the proposed setup to get the elevation angle of the drone. We propose a linear array design using digital receive-only beamforming techniques in the elevation domain to compute the elevation angle in addition to the range, velocity, and azimuth information being provided by the monostatic radar to get complete information about the intruding drone. The simulation of drone detection is followed by an examination of the impact of transmitting fabricated GPS coordinates to the drone. Experimental verification has been conducted to validate both the digital beamforming algorithm and the spoofing technique. This approach blocks the reception of actual GPS signals in the drone and replaces the drone's GPS coordinates with alternative, desired coordinates. The proposed framework can be used to prevent unauthorized drone intrusions in the protected area. [ABSTRACT FROM AUTHOR]

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. [ABSTRACT FROM AUTHOR]

The steady progress in the fields of material science and processing technologies has made multi-layered Radar Absorbing Structures (RAS) an attractive option w.r.t. stealth technologies. They possess the ability to reduce radar cross-section with minimum thickness and is therefore most preferred in airborne applications. As far as their electromagnetic performance is concerned, the sequence of material layers and thickness profile plays a pivotal role. Optimisation of these two factors becomes complex in case of availability of large number of potential materials. Commonly used EM simulation software can be employed for the optimisation of thickness profile. However, selection of suitable material layer sequence is out of their scope. In this context, a Particle Swarm Optimisation (PSO) based algorithm is presented for sequencing of material layers and optimisation of thickness profile of multi-layered RAS configurations. The fitness function has been appropriately formulated to achieve maximum power absorption over broad band of frequencies and wide range of incident angles. Further, the efficacy of the algorithm has been demonstrated using a suitable case study. [ABSTRACT FROM AUTHOR]