An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities

Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology

This paper discusses the challenges of detecting and categorizing small drones with radar automatic target recognition (ATR) technology. The authors suggest integrating ATR capabilities into drone detection radar systems to improve performance and manage emerging threats. The study focuses primarily on drones in Group 1 and 2. The paper highlights the need to consider kinetic features and signal signatures, such as micro-Doppler, in ATR techniques to efficiently recognize small drones. The authors also present a comprehensive drone detection radar system design that balances detection and tracking requirements, incorporating parameter adjustment based on scattering region theory. They offer an example of a performance improvement achieved using feedback and situational awareness mechanisms with the integrated ATR capabilities. Furthermore, the paper examines challenges related to one-way attack drones and explores the potential of cognitive radar as a solution. The integration of ATR capabilities transforms a 3D radar system into a 4D radar system, resulting in improved drone detection performance. These advancements are useful in military, civilian, and commercial applications, and ongoing research and development efforts are essential to keep radar systems effective and ready to detect, track, and respond to emerging threats.Comment: 17 pages, 14 figures, submitted to a journal and being under revie

Effect of Pathogenic Fungal Infestation on the Berry Quality and Volatile Organic Compounds of Cabernet Sauvignon and Petit Manseng Grapes

The effect of pathogenic fungal infestation on berry quality and volatile organic compounds (VOCs) of Cabernet Sauvignon (CS) and Petit Manseng (PM) were investigated by using biochemical assays and gas chromatography-ion mobility spectrometry. No significant difference in diseases-affected grapes for 100-berry weight. The content of tannins and vitamin C decreased significantly in disease-affected grapes, mostly in white rot-affected PM, which decreased by 71.67% and 66.29%. The reduced total flavonoid content in diseases-affected grape, among which the least and most were anthracnose-affected PM (1.61%) and white rot-affected CS (44.74%). All diseases-affected CS had much higher titratable acid, a maximum (18.86 g/100 ml) was observed in the gray mold-affected grapes, while only anthracnose-affected grapes with a higher titratable acid level (21.8 g/100 mL) were observed in PM. A total of 61 VOCs were identified, including 14 alcohols, 13 esters, 12 aldehydes, 4 acids, 4 ketones, 1 ether, and 13 unknown compounds, which were discussed from different functional groups, such as C6-VOCs, alcohols, ester acetates, aldehydes, and acids. The VOCs of CS changed more than that of Petit Manseng’s after infection, while gray mold-affected Cabernet Sauvignon had the most change. C6-VOCs, including hexanal and (E)-2-hexenal were decreased in all affected grapes. Some unique VOCs may serve as hypothetical biomarkers to help us identify specific varieties of pathogenic fungal infestation

Urbanization and air quality as major drivers of altered spatiotemporal patterns of heavy rainfall in China

Context Land use/land cover change and other human activities contribute to the changing climate on regional and global scales, including the increasing occurrence of extreme precipitation events, but the relative importance of these anthropogenic factors, as compared to climatic factors, remains unclear. Objectives The main goal of this study was to determine the relative contributions of human-induced and climatic factors to the altered spatiotemporal patterns of heavy rainfall in China during the past several decades. Methods We used daily precipitation data from 659 meteorological stations in China from 1951 to 2010, climatic factors, and anthropogenic data to identify possible causes of the observed spatiotemporal patterns of heavy rainfall in China in the past several decades, and quantify the relative contributions between climatic and human-induced factors.This research was supported by the 973 Project ‘‘National Key Research and Development Program– Global Change and Mitigation Project: Global change risk of population and economic system: mechanisms and assessments’’ under Grant No. 201531480029, Ministry of Science and Technology of China, People’s Republic of China, the National Natural Science Foundation of Innovative Research Group Project ‘‘Earth Surface Process Model and Simulation’’ under Grant No. 41621061

Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times

Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones

Using a Pair of Different-Sized Spheres to Calibrate Radar Data in the Microwave Anechoic Chamber

Traditional radar calibration methods often use one standard sphere as the standard calibration object, but researchers seldom discuss the size and the material content of the calibration sphere. We propose using a pair of different-sized spheres to verify the standard method. In a microwave anechoic chamber, the simulated and measured results show that the radar cross-section (RCS) values of two spheres with radii of 0.15 m and 0.05 m in the Mie region differ from their physical cross-sections in the optic region, and only in the optic region, their difference of RCS value (i.e., 9.5424 dB) can be approximately equal to the theoretical one (i.e., 9.6803 dB). Thus, radar calibration should be conducted in the same scattering region for both the calibration object and the calibrated target. The use of two different-sized spheres can aid in three applications: (1) verifying the scattering regions, (2) searching the pure area in the microwave anechoic chamber, and (3) locating the positions of the targets in the range profiles

STUDY AND IMPROVEMENT FOR SLICE SMOOTHNESS IN SLICING MACHINE OF LOTUS ROOT

Abstract: Concerning the problem of the low cutting quality and the bevel edge in the piece of lotus root, the reason was analyzed and the method of improvement was to reduce the force in the vertical direction of link to knife. 3D parts and assemblies of cutting mechanism in slicing machine of lotus root were created under Pro/E circumstance. Based on virtual prototype technology, the kinematics and dynamics analysis of cutting mechanism was simulated with ADAMS software, the best slice of time that is 0.2s~0.3s was obtained, and the curve of the force in the vertical direction of link to knife was obtained. The vertical force of knife was changed accordingly with the change of the offset distance of crank. Optimization results of the offset distance of crank showed the vertical force in slice time almost is zero when the offset distance of crank is -80mm. Tests show that relative error of thickness of slicing is less than 10% after improved design, which is able to fully meet the technical requirements

Using Classify-While-Scan (CWS) Technology to Enhance Unmanned Air Traffic Management (UTM)

Drone detection radar systems have been verified for supporting unmanned air traffic management (UTM). Here, we propose the concept of classify while scan (CWS) technology to improve the detection performance of drone detection radar systems and then to enhance UTM application. The CWS recognizes the radar data of each radar cell in the radar beam using advanced automatic target recognition (ATR) algorithm and then integrates the recognized results into the tracking unit to obtain the real-time situational awareness results of the whole surveillance area. Real X-band radar data collected in a coastal environment demonstrate significant advancement in a powerful situational awareness scenario in which birds were chasing a ship to feed on fish. CWS technology turns a drone detection radar into a sense-and-alert planform that revolutionizes UTM systems by reducing the Detection Response Time (DRT) in the detection unit

Comparison of Radar Signatures from a Hybrid VTOL Fixed-Wing Drone and Quad-Rotor Drone

Current studies rarely mention radar detection of hybrid vertical take-off and landing (VTOL) fixed-wing drones. We investigated radar signals of an industry-tier VTOL fixed-wing drone, TX25A, compared with the radar detection results of a quad-rotor drone, DJI Phantom 4. We used an X-band pulse-Doppler phased array radar to collect tracking radar data of the two drones in a coastal area near the Yellow Sea in China. The measurements indicate that TX25A had double the values of radar cross-section (RCS) and flying speed and a 2 dB larger signal-to-clutter ratio (SCR) than DJI Phantom 4. The radar signals of both drones had micro-Doppler signals or jet engine modulation (JEM) produced by the lifting rotor blades, but the Doppler modulated by the puller rotor blades of TX25A was undetectable. JEM provides radar signatures such as the rotating rate, modulated by the JEM frequency spacing interval and the number of blades for radar automatic target recognition (ATR), but also interferes with the radar tracking algorithm by suppressing the body Doppler. This work provides an a priori investigation of new VTOL fixed-wing drones and may inspire future research