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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]

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 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]

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]

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]

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.)

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]

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.)

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]

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]

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]

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]

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]

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]

With the proliferation of radio frequency (RF) systems and radar applications, Electronic Warfare (EW) is receiving increasing importance. The analysis of the radar signals is a critical EW task that decides the nature of counter employments. In an Electronic Support (ES) system, the challenge is to detect hostile radiation sources efficiently and trigger a counter response. Detection of types of Pulse Repetition Interval (PRI) modulation of radar signal significantly facilitates the manifestation of RF emitters during recognition which is difficult in a dense EW environment. Recent developments in artificial intelligence (AI) methods suggest that this emerging technology can be effective for such purposes. In this direction, an automatic approach for recognizing several kinds of complex PRI modulation based on Continuous Wavelet Transform (CWT) and a combination of the vanilla Convolutional Neural Network (CNN), a multi-head self-attention (MHSA) mechanism and the popular Long Short-Term Memory (LSTM) is proposed. The CWT is used to decompose the PRI modulation sequence and obtain different time–frequency components. Further, aided by the proposed CNN-MHSA-LSTM combination, the features extracted from the CWT 2D-scalograms are used to execute PRI modulation discrimination. In this method, the vanilla CNN is employed for the extraction of deep features to figure out the class details while capturing the spatial attributes. Thereafter, to improve the discriminative power of the entire framework a MHSA mechanism is used. The temporal attributes are acquired by the LSTM which works in concert with the CNN for executing the detection of the PRI classes based on the extracted features. Also to assess the effectiveness of the proposed method, three models based on ResNet, popular CNN and SqueezeNet are implemented for benchmark comparison in terms of overall performance and complexity. The simulation results show that the proposed method enhances performance and achieves robustness in the noise-filled and imperfect channel knowledge environment. The best recognition accuracy is 98.3% with 50% spurious pulses in the environment which fluctuates with imperfect channel knowledge cases. [ABSTRACT FROM AUTHOR]

With the development of modern electronic countermeasure technology, radar reconnaissance equipment receives pulse sequences of increasing density and complexity. Achieving concise as well as robust feature representation of Radar Emitter Pulse Sequences (REPS), and improving the classification and recognition performance of REPS signals have always been the focus at present. Current research did not consider the distribution information and co-information buried in REPS data. To solve this problem, we propose to visualize and classify REPS signals based on 2D feature map. First, we extract the Sequence Distribution Property (SDP) of REPS for spatial coding to obtain an encoded feature vector that is deformed into a 2D matrix. Three types of informative 2D matrices generated from REPS are combined into a color 2D feature map. Different from previous feature extraction methods, the proposed method can not only express the single sequence information concisely and accurately but also express the correlation of multiple parameter sequences synchronously in the interaction of different color channels. Second, based on the 2D feature map, we design a 'L&FCN' framework for classification, in which we fed the 2D feature map into a network composed of lower convolution neural layers and fully connected layers to judge the category and output recognition result. Finally, we put forward a novel strategy with integrating global and local segments' information of REPS to train the network, making it converge with smaller classification errors. Simulation results verify the effectiveness and superiority of our method. • We propose a radar pulse sequence's feature extraction method based on distribution information, representing complex REPS data as concise 2D feature maps and possessing excellent distinguishable abilities. Different from previous methods, our proposal can not only express the key information concisely but also embed the correlation of multiple parameter sequences in radar pulses synchronously. • We construct a 'L&FCN' framework for classification based on 2D feature map. In the 'LCN' module, we adopt the Lower Convolution Neural layer (LCN) to process the 2D feature map. In the 'FCN' module, we design a three-layer Fully Connected Network (FCN) to further process the output of 'LCN' module and recognize the radar pulses' signal. • To use the collaborative relationship among different parameter sequences included in radar pulses, we put forward a novel strategy via integrating global and local patterns' information to train the network, making it converge with smaller training loss and achieving state-of-the-art results. [ABSTRACT FROM AUTHOR]

Subsidence in coastal deltas is a challenging issue globally, influenced by both climate and anthropogenic pressures. Owing to its rich economic value, the Godavari Delta on the east coast of India attracts the human population and is therefore a hub of developmental activities. We quantified the line of sight (LOS) rate of land deformation in the Godavari Delta, India, for two different periods using the time series interferometric analysis of Advanced Land Observing Satellite Phase Arrayed L-band Synthetic Aperture Radar (ALOS PALSAR) (2007–2011) and C-band Sentinel-1A (2017–2020) datasets. This approach was based on the Small Baseline Subset (SBAS) algorithm, which constructed 76 (ALOS) and 333 (Sentinel-1A) pairs of interferograms. The results indicated that the majority of deltas had varying LOS deformation rates in both periods. The southern and northern coastline of the deltas showed subsidence (-10 to -20 mm/yr) and uplift (5 mm to 7 mm/yr), respectively, from 2007 to 2011. The uplift (11 mm/yr) during 2017–2020 was attributed to coastal erosion and accretion during the study period. Statistical analyses showed that the majority of the coastline experienced erosion from 2006 to 2011, ranging from 0 ‐30 m/yr. Conversely, during 2017–2019, accretion ranging from 0-30 m/yr was dominant. The Kakinada spit has recently experienced an uplift of 8 mm/yr. Many factors contributing to deformation, such as groundwater fluctuations, rainfall, land use and land cover (LULC) changes, and water quality, were explored. Groundwater level variations were strongly correlated with the rate of deformation. A direct relationship was also observed between water recharge through rainfall and the ground deformation rate. The subsidence rate was estimated as 0 to ‐8 mm/yr from 2007-2011 and -10 to -20 mm/yr for 2017–2020 in the Godavari Delta. Increased urban areas and land conversion from the LULC analysis were the causative factors of subsidence in this region. Thus, the Godavari Delta will become highly vulnerable, as the region has shown various LOS deformation rates. Studies on land subsidence are highly relevant for effective coastal management planning and management. • First study to explore the ground deformation in Godavari delta. • Role of freshwater influence and land use are discussed. • Brought out the human interventions in the Godavari delta. • Needed for environmental management of the Godavari delta. [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]

Airborne early warning systems are deployed for collecting surveillance information on airborne enemy targets in real-time. The Maritime Patrol Radar system is used for surveillance of sea surface for various types of ships and low flying aircraft. Radio Detection And Ranging system, or RADAR, in short, is an Electromagnetic sensor integrated on such airborne platforms. An antenna of this radar system is generally mounted under the belly of the aircraft and protected by a cover called a radome. This radome is installed to protect the radar antenna from environmental disturbances. Due to the installation of the radome, increased drag is experienced by aircraft during its flight due to resistance to the flow of the oncoming air. Radome design is a multidisciplinary effort involving structural, aerodynamics, and electromagnetic disciplines. In this study, the multi-disciplinary design of the maritime patrol aircraft radome for optimality in terms of structural strength and aerodynamic performance is carried out by integrating both disciplinary analyses on an optimisation software platform. The utopia point in terms of these two disciplines is found. [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]

Jamming is protecting the original target from being detected by the enemy radar. There are different types of jamming methods that can mislead the enemy radar, but they are not as effective and still have a chance to retrieve the original signal from the enemy radar. Thus, a deception jamming is proposed using interrupted sampling repeater jamming called ISRJ. ISRJ generates a train of false targets and also uses an active echo cancellation technique which totally cancels the original target echo signal and generates only a train of false targets. The techniques used before to cancel the targets are based on Linear Frequency Modulation (LFM) radar waveform. A Non-Linear Frequency Modulation (NLFM) waveform is used because it has an advantage of very low peak side lobe level. In this paper, we generated a train of false targets along with real radar target echos to deceive the enemy radar and to protect the target from being detected. In addition, the amplitude of the real target is made very small to confuse enemy radars. However, the fact cannot be ignored that the original target echo is present in the false targets, so there is a chance that the enemy radar can detect the original target. Therefore, using the active cancellation technique cancelled the target echo signal. The results are compared with both LFM and NLFM pulse compression techniques. The simulation results show how the target echo is cancelled and protected by the false targets. [ABSTRACT FROM AUTHOR]

Historically, reliability studies of electronic systems, including radar, have primarily focused on the trustworthiness and endurance of the hardware components of the system. While the radar's hardware is indeed a crucial part of the system, the signal processing aspects of the radar and the actual operating conditions, such as the environment and aging, have been neglected in previous reliability studies. In this paper, the operational reliability paradigm is used to analyze the parameters of the radar's signal processor and their effects on the radar's target detection performance while in service. A detection threshold is derived using the operational reliability formulation that can be optimized for given target and clutter statistics. [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]

Reclaimed airports usually have fragile geological structures and are susceptible to the uneven ground settlements caused by filling-material consolidation, underground construction, and dynamic loading from takeoff and landing of aircrafts. Therefore, deformation monitoring is of great significance to the safe operation of reclaimed airports. This study adopts an improved permanent-scatterer interferometric synthetic-aperture radar strategy to map the spatiotemporal deformation of Shenzhen Bao'an International Airport in China using ascending and descending Envisat/ASAR data acquired from 2007 to 2010 and Sentinel-1 data from 2015 to 2019. The results show that uneven settlements of the airport concentrate in the new reclaimed land. Then we explore the settlement characteristics of each functional area. Furthermore, we separate out the dynamicload settlement of runway No. 2 and confirm the settlements caused by dynamic load. This study provides new ideas for studying deformation in similar fields, and technical references for the future construction of Shenzhen Airport. [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]