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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]

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]

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]

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]

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]

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]

More than 42 000 fires occur nationwide and cause over 2500 casualties every year. There is a lack of specialized equipment, and rescue operations are conducted with a minimal number of apparatuses. Through‐the‐wall radars (TTWRs) can improve the rescue efficiency, particularly under limited visibility due to smoke, walls, and collapsed debris. To overcome detection challenges and maintain a small‐form factor, a TTWR system‐on‐chip (SoC) and its architecture have been proposed. Additive reception based on coherent clocks and reconfigurability can fulfill the TTWR demands. A clock‐based single‐chip infrared radar transceiver with embedded control logic is implemented using a 130‐nm complementary metal oxide semiconductor. Clock signals drive the radar operation. Signal‐to‐noise ratio enhancements are achieved using the repetitive coherent clock schemes. The hand‐held prototype radar that uses the TTWR SoC operates in real time, allowing seamless data capture, processing, and display of the target information. The prototype is tested under various pseudo‐disaster conditions. The test standards and methods, developed along with the system, are also presented. [ABSTRACT FROM AUTHOR]

Presently, the extraction of hand‐crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high‐level abstract representations from the time‐frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning‐based architecture for multi‐platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives. [ABSTRACT FROM AUTHOR]

Since the Gyeongju earthquakes in 2016, there have been increased research interests in the areas of seismic design, building collapse, and rescue radar applications in Korea. Ground penetrating radar (GPR) is a nondestructive electromagnetic method that is used for underground surveys. To properly design ground penetrating radar that detects buried victims precisely, it is important to study electromagnetic wave propagation channel characteristics in advance. This work presents an electromagnetic propagation environment analysis of a trapped victim for GPR applications. In this study, we develop a realistic collapse model composed of layered reinforced concrete and a victim positioned horizontally. In addition, the effects of rebars and the distance between the radar antenna and target are investigated. The numerical analysis presents the electromagnetic wave propagation characteristics, including amplitude loss and phase difference, in the 450‐MHz and 1,500‐MHz frequency band, and it shows the electric field distribution in the environment. [ABSTRACT FROM AUTHOR]

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal‐to‐noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust waveform design for distributed multiple-radar systems (DMRSs) based on low probability of intercept (LPI), where signal- to-interference-plus-noise ratio (SINR) and mutual information (MI) are utilized as the metrics for target detection and information extraction, respectively. Recognizing that a precise characterization of a target spectrum is impossible to capture in practice, we consider that a target spectrum lies in an uncertainty class bounded by known upper and lower bounds. Based on this model, robust waveform design approaches for the DMRS are developed based on LPI-SINR and LPI-MI criteria, where the total transmitting energy is minimized for a given system performance. Numerical results show the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

There are several remote-sensing tools readily available for the study of nocturnally flying animals (e.g., migrating birds), each possessing unique measurement biases. We used three tools (weather surveillance radar, thermal infrared camera, and acoustic recorder) to measure temporal and spatial patterns of nocturnal traffic estimates of flying animals during the spring and fall of 2011 and 2012 in Lewes, Delaware, USA. Our objective was to compare measures among different technologies to better understand their animal detection biases. For radar and thermal imaging, the greatest observed traffic rate tended to occur at, or shortly after, evening twilight, whereas for the acoustic recorder, peak bird flight-calling activity was observed just prior to morning twilight. Comparing traffic rates during the night for all seasons, we found that mean nightly correlations between acoustics and the other two tools were weakly correlated (thermal infrared camera and acoustics, r -- 0.004 ± 0.04 SE, n -- 100 nights; radar and acoustics, r -- 0.14 ± 0.04 SE, n -- 101 nights), but highly variable on an individual nightly basis (range -- --0.84 to 0.92, range -- --0.73 to 0.94). The mean nightly correlations between traffic rates estimated by radar and by thermal infrared camera during the night were more strongly positively correlated (r -- 0.39 ± 0.04 SE, n -- 125 nights), but also were highly variable for individual nights (range -- --0.76 to 0.98). Through comparison with radar data among numerous height intervals, we determined that flying animal height above the ground influenced thermal imaging positively and flight call detections negatively. Moreover, thermal imaging detections decreased with the presence of cloud cover and increased with mean ground flight speed of animals, whereas acoustic detections showed no relationship with cloud cover presence but did decrease with increased flight speed. We found sampling methods to be positively correlated when comparing mean nightly traffic rates across nights. The strength of these correlations generally increased throughout the night, peaking 2-3 hours before morning twilight. Given the convergence of measures by different tools at this time, we suggest that researchers consider sampling flight activity in the hours before morning twilight when differences due to detection biases among sampling tools appear to be minimized. [ABSTRACT FROM AUTHOR]

The article presents news items on environmental issues. Researchers Frank and William von Hippel have suggested that a global increase in the impotency drug Viagra may result in a decrease in threats to animals such as deer and seals that are used in traditional Chinese medicine. Spanish scientists are planning to use radar in 2003 in an effort to solve a bizarre mystery which surrounds the greater noctule (Nyctalus lasiopterus), which is Europe's largest bat.

A new computationally efficient algorithm-based propagator method for two-dimensional (2-D) direction of- arrival (DOA) estimation is proposed, which uses two parallel uniform linear arrays. The algorithm takes advantage of the special structure of the array which enables 2-D DOA estimation without pair matching. Simulation results show that the proposed algorithm achieves very accurate estimation at a computational cost 4 dB lower than that of standard methods. [ABSTRACT FROM AUTHOR]

Reveals the plan of Spanish scientists to use radar in their research on Nyctalus lasiopterus, Europe's largest bat. Comments from Carlos Ibañez of Doñana Biological Research Station; Question raised by the specie on its choice of menu; Idea behind the research.