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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]

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]

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]

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]

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]

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]

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]

Autonomous driving is a rapidly developing technology that is also a source of debate. People believe that autonomous vehicles will provide a better future by increasing road safety, lowering infrastructure expenses, and improving mobility for children, the old, and the disabled. On the other hand, many individuals are concerned about incidences of automotive hacking, the likelihood of fatal crashes, and the loss of driving-related professions. Autonomous driving is, without a question, a complex and problematic technology for many people. To better comprehend how safe self-driving cars are, it's necessary to first understand how they function, as well as what kind of sensors autonomous vehicles use to determine where they should travel and recognize things on the road in order to avoid automobile accidents. Data collected by the sensors exhibit heterogeneous and multimodal characteristics, which are further fused to frame effective decision rules. Thus sensors play a major role in decision making activity of Autonomous Vehicles (AVs). In order to acquire more information related to the sensors, this paper analyses and summarizes different types of AV sensors based on their mandatory attributes. This analysis helps the readers to understand the contribution of the sensors towards decision-making in AVs and also summarizes the data types collected by different sensors. The summarized inferences will be an eye-opener to most of the budding researchers and students in the field of AVs to select the appropriate sensor based on their needs for their research. The study also gives brief information regarding the specifications of different categories of sensors manufactured by leading vendors in the market. [ABSTRACT FROM AUTHOR]

Ultra-wide band radar systems have gained even more popularity as an emerging technology in health-related research and particularly in the context of ambient intelligence for activity recognition inside smart environments. Indeed, recent technological advances have facilitated their implementation on smaller SoC at lower cost. However, observed practical behaviors not always meet theoretical prediction. Therefore, this paper presents an empirical evaluation of Novelda XeThru X4-based IR-UWB radars behavior. To this end, several experiments were conducted to provide insightful analyses to consider while exploiting such recent technology. We believe that these assessments will benefit to academic researchers by guiding their implementation choices to design new robust activity recognition systems. [ABSTRACT FROM AUTHOR]

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM‐CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at −2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the electromagnetic environment and the continuous development of radar technology, more and more modern digital programmable radars using agile waveform will appear in the future battlefield. It is difficult to effectively identify these radar emitters with complex system only by relying on traditional recognition models. In response to the above problem, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism to deal with the problem of variable conventional characteristic parameters of radar emitter signals with agile waveform. First, we perform a distributed representation of the pulse signal data to generate high-dimensional sparse signal features. Then we design to use a dynamic Convolutional Neural Network to extract features of structural details of radar emitter signals with agile waveform at different levels, and use a Long Short-Term Memory to extract its timing features. In order to obtain the deep features that can characterize the agility of the waveform, the attention mechanismbased method is used to fuse the extracted structural features and timing features, and at the same time it can reduce the influence of noise in complex electromagnetic environment on the characteristic data of radar emitter. Finally, the deep feature is input into the Softmax layer to complete the recognition of radar emitters with agile waveform. The experimental results show that the method proposed in this paper can effectively solve the problem of the recognition of radar emitters with agile waveform, and the recognition accuracy is improved by 1.26% compared with the traditional models and other deep models. [ABSTRACT FROM AUTHOR]

The present study deals with adaptive detection of radar target signal with an unknown amplitude embedded in Gaussian interference that has been modeled as an AR process. Application of such a model to the interference decreased the number of parameters to be estimated; therefore, less or even no secondary data were required to obtain a detector with the desired performance. Herein, detection was accomplished based on only the primary data. The authors resorting to the modern Kalman filtering technique developed the conventional GLRT-based detection in the presence of AR interference and proposed two new detectors: AREKF based on extended Kalman filter and ARUKF based on unscented Kalman filter. The performance assessment conducted by Monte Carlo simulation compared the proposed detectors with the existing ones based on the generalised likelihood ratio test and Kalman filter. The results revealed that the ARUKF detector could significantly outperform other detectors in terms of detection for both small number of primary datasets and high Signal-to-Noise Ratio (SNR). [ABSTRACT FROM AUTHOR]

The paper concentrates on the design, architecture, and monitoring of smart LED street lighting control, with focus on traffic safety and safe road infrastructure. The use of a CMAS (Cloud-Based Multi-Agent System) as a possible framework is investigated. The work is based on previous developments by the authors in the production and design of close and longrange hybrid Pyroelectric Infrared (PIR) motion detection sensors. It also introduces the advances in radar-type sensors used in smart SLC (street lighting control) application systems. The proposed sensor solutions can detect the road user (vehicle or pedestrian) and determine its movement direction and approximate speed that can be used for dynamic lighting control algorithms, traffic intensity prediction, and increased safety for both driver and pedestrian traffic. Furthermore, the street lighting system infrastructure can monitor city environmental parameters, such as temperature, humidity, CO2 levels, thus increasing levels of safety and security for smart cities. Utilising other hybrid systems within intelligent street lighting applications represents a new specialisation area in both energy-saving, safety awareness, and intelligent management. [ABSTRACT FROM AUTHOR]

Multi‐carrier waveforms have several advantages over single‐carrier waveforms for radar communication. Employing multi‐carrier complementary phase‐coded (MCPC) waveforms in radar applications has recently attracted significant attention. MCPC radar signals take advantage of orthogonal frequency division multiplexing properties, and several authors have explored the use of MCPC signals and the difficulties associated with their implementation. The sidelobe level and peak‐to‐mean‐envelope‐power ratio (PMEPR) are the key issues that must be addressed to improve the performance of radar signals. We propose a scheme that applies pattern‐based scaling and geometric progression methods to enhance sidelobe and PMEPR levels in MCPC radar signals. Numerical results demonstrate the improvement of sidelobe and PMEPR levels in the proposed scheme. Additionally, autocorrelations are obtained and analyzed by applying the proposed scheme in extensive simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, an additional degree of freedom in phased multi‐input multi‐output (phased‐MIMO) radar with any arbitrary desired covariance matrix is proposed using space‐time codes. By using the proposed method, any desired transmit covariance matrix in MIMO radar (phased‐MIMO radars) can be realized by employing fully correlated base waveforms such as phased‐array radars and simply extending them to different time slots with predesigned phases and amplitudes. In the proposed method, the transmit covariance matrix depends on the base waveform and space‐time codes. For simplicity, a base waveform can be selected arbitrarily (ie, all base waveforms can be fully correlated, similar to phased‐array radars). Therefore, any desired covariance matrix can be achieved by using a very simple phased‐array structure and space‐time code in the transmitter. The main advantage of the proposed scheme is that it does not require diverse uncorrelated waveforms. This considerably reduces transmitter hardware and software complexity and cost. One the receiver side, multiple signals can be analyzed jointly in the time and space domains to improve the signal‐to‐interference‐plus‐noise ratio. [ABSTRACT FROM AUTHOR]

The paper searches for solutions to the problem of detecting a signal from low-flying small-sized objects against the background of interference in the urban environment using radar stations. We propose an adaptive algorithm that can be used in modern radar systems for detecting and tracking small objects. The developed adaptive algorithm for resolving signals reflected from low-flying objects allows us to take into account the multipath effect. A variant of the practical realization of the adaptive detection algorithm, implemented on an experimental sample of the X-range radar of wavelength, is presented. [ABSTRACT FROM AUTHOR]

In this paper, we describe the result of a recent hydrogeological and geophysical investigation carried out within and around Aduramigba - Onibu-Eja Estate dumpsite with the aim of detecting evidence of contamination as well as depths to contamination of the groundwater after a previous study six years ago. Twenty water samples from shallow water wells and boreholes were analyzed for their physico-chemical constituents while five vertical electrical soundings and five electromagnetic ground penetrating radar surveys were conducted. The result shows high concentrations of anions of nitrates (NO3 - ), bicarbonates (HCO3 - ), chloride (Cl-) and sulphate (SO42- ) with values of 73.97 mg/l, 91.5 mg/l, 331.2 mg/l, 222.4 mg/l respectively, cations values of Mg2+, Fe2+, Na+, and K+ having value of 25.44 mg/l, 6.23 mg/l, 37.31 mg/l and 35.41 mg/l, respectively, which were far above the World Health Organization (WHO) and Nigeria Drinking Water Quality Standard (NDWS). Heavy metal components such as Lead, Zinc, Copper and Nickel were also observed to be on the high side, having been contributed from effluents from rust metallic materials cum chemicals dumped within the dumpsite. Detailed geophysical study reveal the depth to contamination at between 4.1 meters in VES 3 to 5.9 meters in VES 2 with resistivity as low as 5.12 Ωm in VES 2 to 7.12 Ωm in VES 3 while ground penetrating radar (GPR) revealed that the leachate has permeated to a depth of 3.5 – 5.25 meters in traverse 4 and 4.0 – 6.0 meters in traverse 3 which correspond to the depth to unconfined aquifer within the dumpsite. The study conclude that this would pose a greater threat to residents living in this vicinity and as such, this might have contributed to the closure of the dumpsite. [ABSTRACT FROM AUTHOR]

In the conventional frequency diverse array (FDA) radar designs, generalized likelihood ratio test (GLRT) detection utilizes coherent pulses. However, the impacts of an FDA multiple-input multiple-output (FDAMIMO) radar system for detection with incoherent pulses have not been systematically investigated. In this paper, we present an incoherent square-law detector to analyse the performance of both the coherent and non-coherent airborne FDA-MIMO radars in a Neyman-Pearson sense. Moreover, the closed-form expressions of an incoherent square-law detector for the FDA-MIMO radars are derived. For a coherent FDA-MIMO radar, the optimal performance is achieved at a high signal-to-noise ratio (SNR), whereas the superiority of a non-coherent FDA-MIMO radar in distinguishing range dependent targets is validated. The corresponding theoretical derivations are verified by the extensive numerical results to show an improved performance. [ABSTRACT FROM AUTHOR]

During 2014-2016, the northeast Pacific Ocean experienced a large-scale marine heatwave (nicknamed 'The Blob'), an event that was associated with die-offs of several marine bird species. The Marbled Murrelet Brachyramphus marmoratus is a small seabird in this region for which we evaluated trends in abundance--for the purposes of conservation planning--during radar surveys at 58 sites in coastal British Columbia, from 1996 to 2018, and for which we determined whether trends may have been affected by 'The Blob'. A hierarchical Bayesian model allowed us to separate long-term trends from short-term annual fluctuations ('year effects') that might have resulted from changes in ocean conditions, and it also allowed us to test whether predicted regional counts were associated with two indices of marine conditions: (1) measured abundance of a prey species, Pacific Herring Clupea pallasii; and (2) sea surface temperature (SST). Province-wide mean annual rate of change in counts was significantly negative, with a posterior median of -0.023 (95% credible interval: -0.033, -0.014); mean rate of change in counts per year was negative for all six Marbled Murrelet Conservation Regions; and trends were significantly negative at the Central Mainland Coast, East Vancouver Island, Haida Gwaii, and the South Mainland Coast conservation regions. Year effects during or after 'The Blob' at West and North Vancouver Island (2016) and East Vancouver Island (2018) conservation regions were not statistically significant. Mean predicted regional counts of murrelets showed weakly positive correlations with age two herring recruitment, and no consistent associations with SST. These results indicate that the marine heatwave did not strongly affect forest-bound murrelets and are consistent with the hypothesis that ongoing loss of terrestrial nesting habitat is associated with population declines of Marbled Murrelets in British Columbia. [ABSTRACT FROM AUTHOR]

At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high‐speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C‐means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C‐means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies—generalized Nash game and cooperative and noncooperative game theory. [ABSTRACT FROM AUTHOR]

A frequency diverse array (FDA) multiple‐input multiple‐output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle‐range‐dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast‐convergence trilinear decomposition (FC‐TD) algorithm to jointly estimate FDA‐MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC‐TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramér‐Rao bounds are presented and simulation results are provided to validate the proposed FC‐TD algorithm effectiveness. [ABSTRACT FROM AUTHOR]

There are many applications which require remote and noninvasive measurement of heartbeat of a human being using an ultra-wideband (UWB) radar. Sophisticated models and their analysis need to be referred before the design of a practical radar prototype. In this paper, i) a UWB wave propagation model of human thorax and ii) the power transmission coefficients estimated from the simulations of the model in the range 1-10 GHz using MATLAB are presented. The study reveals that there is a periodic variation of the transmission coefficients in correlation with the instantaneous physical dimensions of an active heart. [ABSTRACT FROM AUTHOR]

We propose 8.2‐GHz band radar RFICs for an 8 × 8 phased‐array frequency‐modulated continuous‐wave receiver developed using 65‐nm CMOS technology. This receiver panel is constructed using a multichip solution comprising fabricated 2 × 2 low‐noise amplifier phase‐shifter (LNA‐PS) chips and a 4ch RX front‐end chip. The LNA‐PS chip has a novel phase‐shifter circuit for low‐voltage operation, novel active single‐to‐differential/differential‐to‐single circuits, and a current‐mode combiner to utilize a small area. The LNA‐PS chip shows a power gain range of 5 dB to 20 dB per channel with gain control and a single‐channel NF of 6.4 dB at maximum gain. The measured result of the chip shows 6‐bit phase states with a 0.35˚ RMS phase error. The input P1 dB of the chip is approximately –27.5 dBm at high gain and is enough to cover the highest input power from the TX‐to‐RX leakage in the radar system. The gain range of the 4ch RX front‐end chip is 9 dB to 30 dB per channel. The LNA‐PS chip consumes 82 mA, and the 4ch RX front‐end chip consumes 97 mA from a 1.2 V supply voltage. The chip sizes of the 2 × 2 LNA‐PS and the 4ch RX front end are 2.39 mm × 1.3 mm and 2.42 mm × 1.62 mm, respectively. [ABSTRACT FROM AUTHOR]