Radar Technology Research Articles

Deformation Analysis and Reinforcement Effect Evaluation for the No. 65 Slope on the Shangsan Expressway Based on SBAS-InSAR

The deformation of the No. 65 slope on the Shangsan Expressway poses a potential threat to road safety. In July 2021, the deformation rate of this slope accelerated significantly, leading to the implementation of reinforcement measures in 2022. To comprehensively analyze the historical deformation characteristics of the slope and evaluate the effectiveness of the reinforcement measures, this study employs Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to calculate and analyze the historical deformation characteristics of the slope and the adjacent hillside for two periods: from 10 January 2018 to 22 August 2021, and from 3 September 2021 to 22 December 2023. The SBAS-InSAR monitoring results were compared with in situ data from borehole inclinometers to verify the reliability of the calculations. The SBAS-InSAR results indicate that before reinforcement, the slope exhibited slow movement; however, after the implementation of the reinforcement measures, the displacement significantly decreased, demonstrating the success and effectiveness of the interventions. The consistency between the SBAS-InSAR results, borehole inclinometer data, and surface observations confirms the substantial potential of SBAS-InSAR technology for slope engineering monitoring.

A Study of Various Clustering Algorithms Used for Radar Signal Sorting

Radar signal sorting plays a significant part in radar countermeasure technology and reconnaissance systems. By means of radar signal sorting, several radars and their parameters in the battlefield are precisely recognized and placed in the radar records for subsequent positioning and jamming processing. The basic sorting methods cannot fulfill the sorting process with accurate and efficient results. Therefore, in this paper, we conduct a study on two main classes clustering techniques, the first is hierarchical based clustering, and the second one is partition based clustering, which have different characteristics into groups of pulses and they can sort and handle large number of radar sequence with high precision and accuracy. The numerical simulations studied and compared both methods under different perspectives to clarify a new directions based on the insightful investigations of these sorting techniques.

The Latest Development of Synthetic Aperture Radar: An Overview

Abstract. Synthetic Aperture Radar (SAR) technology enables high-resolution imaging regardless of weather conditions, offering immense potential for target detection and recognition. This paper reviews the latest advancements in SAR technology, highlighting key developments and applications. The limitations of traditional SAR imaging techniques, particularly the trade-off between resolution and swath width, have been addressed by High Resolution Wide Swath (HRWS) SAR, enabling simultaneous acquisition of high-resolution and wide-swath data. Multi-polarimetric SAR provides richer scattering information for improved target recognition accuracy, while deep learning algorithms enhance image quality and recognition performance. Emerging technologies like 3D-ISAR and holographic imaging offer novel approaches to target recognition and tracking. We discuss the development history, performance advantages, and limitations of these technologies, providing a comprehensive overview of their practical applications. We identify current challenges and future trends in SAR target imaging and recognition, including computational efficiency, adversarial attack defense, data acquisition and processing, and feature extraction and classification. Potential research directions, such as integrated multi-mode SAR systems, multi-source data fusion, and adversarial attack defense strategies, are outlined to fully harness the potential of SAR technology in target imaging and recognition.

Signal Processing for Novel Noise Radar Based on de-chirp and Delay Matching

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments.

Modeling of the leader-follower satellite formation for long baseline

Abstract Interferometric Synthetic Aperture Radar (InSAR) represents a crucial component of synthetic aperture radar technologies, with the accuracy of measurements significantly improving as the baseline length increases. In comparison with the traditional Hill-Clohessy-Wiltshire (HCW) equations, the application of curvilinear coordinate systems for modeling satellite formations in circular orbits has demonstrated enhanced precision, especially when the formations span larger distances. However, these improvements were initially limited to circular orbits. Addressing this limitation, this study introduces a novel approach by incorporating elliptical orbits into the model, thereby refining the state transition matrix through the incorporation of eccentricity factors and maintaining the direct correlation with the temporal dynamics of satellite motion in three dimensions. Through rigorous numerical simulations, it has been established that this refined model substantially improves accuracy in leader-follower satellite formations.

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

A high precision vital signs detection method based on millimeter wave radar

Millimeter wave (mmWave) radar technology has potential applications in vital signs detection and medicine. In order to minimize the influence of human micro-movements and respiratory harmonics on heart rate estimation, the vital signs detection method based on mmWave radar is studied in this paper. First, we use median filtering to eliminate baseline drift caused by human micromotion. Next, a differential recursive least squares multiple classification (DR-MUSIC) algorithm is proposed based on the combination of recursive least squares-based adaptive filter (RLS) and multiple signal classification (MUSIC) algorithm. This algorithm effectively suppresses respiratory harmonics and separates respiratory and heartbeat signals. Finally, heart rate value can be precisely estimated using spectral peak search. We invite a number of people to participate in the experiment, which demonstrate that the method successfully suppresse the impact of respiratory harmonics at low SNR. The error rate between the estimated heart rate and the reference heart rate is only 1.69% to 2.61%, which is significantly better than the existing algorithms.

Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.

Comparative Study of GPR Acquisition Methods for Shallow Buried Object Detection

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features.

Efficient Nanofibrous Electromagnetic Wave Absorber Inspired by Spider Silk Hunting.

With the rapid advancements in wireless communication and radar detection technologies, there has been a significant uptick in the utilization frequency of electromagnetic waves across both civilian and military sectors, consequently generating substantial electromagnetic radiation and interference. These electromagnetic pollutants present a considerable threat to public health and information security. Consequently, materials capable of absorbing and mitigating electromagnetic pollution have garnered significant attention.The nanomaterials with multiple components and various heterogeneous interfaces have been considered as preferred efficient electromagnetic wave (EMW) absorbers. In this work, carbon nanofibers doped with magnetic metal oxide particles (Co/Ni-CNFs) are prepared by electrospinning and heat treatment. In these samples, the minimum reflection loss can reach -40.13dB at 4.6mm, and the widest effective absorption bandwidth is 4.4GHz. The excellent microwave absorption is attributed to the appropriate impedance matching. The electromagnetic parameters of Co/Ni-CNFs are balanced by adjusting the concentration of magnetic metal-organic framework material (MOFs) in the precursor solution. It is believed that this work can provide a reference for developing lightweight, flexible, and robust electromagnetic wave-absorbing materials.

Using Advanced InSAR Techniques and Machine Learning in Google Earth Engine (GEE) to Monitor Regional Black Soil Erosion—A Case Study of Yanshou County, Heilongjiang Province, Northeastern China

The black soil region experiences complex erosion due to natural processes and intense human activities, leading to soil degradation and adverse ecological and agricultural impacts. However, the complexities involved in quantifying regional erosion poses remarkable challenges in accurately assessing the current status of regional soil erosion for effective soil conservation. To solve this issue, we proposed a new method for monitoring soil erosion using Interferometric synthetic aperture radar (InSAR) technology and machine learning algorithms within the Google Earth Engine platform. The new method not only enables regional-scale monitoring, but also ensures high accuracy in measurement (millimeter-level). The erosion susceptibility of the study area (Yanshou County, Heilongjiang Province, Northeastern China) was also classified using random forest algorithms to refine the monitored and predicted soil erosion. The results indicate that the five-year (2016–2021) deformation in Yanshou County was −11.08 mm, with a significant mean cumulative deformation of −8.08 mm yr−1 occurring in 2017. The driving factor analysis shows that the region was subject to the compound effect of water and freeze–thaw erosion, closely related to crop phenological stages. The susceptibility analysis indicates that 73.3% of the region was susceptible to erosion, with a higher probability in river areas, at high altitudes, and on steep slopes. However, good vegetation cover can reduce the risk of soil erosion to some extent. This study offers a new perspective on monitoring regional soil erosion in the black soil region of China. The proposed method holds potential for future expansion to monitor soil erosion in a larger areas, thereby guiding the strategies development for protection of the agriculturally important black soil.

Research on Radar Target Detection Based on the Electromagnetic Scattering Imaging Algorithm and the YOLO Network

In this paper, the radar imaging technology based on the time-domain (TD) electromagnetic scattering algorithm is used to generate image datasets quickly and apply them to target detection research. Considering that radar images are different from optical images, this paper proposes an improved strategy for the traditional You Only Look Once (YOLO)v3 network to improve target detection accuracy on radar images. The speckle noise in radar images can cover the real information of a target image and increase the difficulty of target detection. The attention mechanisms are added to the traditional YOLOv3 network to strengthen the weight of the target region. By comparing the target detection accuracy under different attention mechanisms, an attention module with higher detection accuracy is obtained. The validity of the proposed detection network is verified on a simulation dataset, a measured real dataset, and a mixed dataset. This paper is about an interdisciplinary study of computational electromagnetics, remote sensing, and artificial intelligence. Experiments verify that the proposed composite network has better detection performance.

Ground subsidence prediction with high precision: a novel spatiotemporal prediction model with Interferometric Synthetic Aperture Radar technology

ABSTRACT As the extraction of mineral resources intensifies, ground subsidence in mining areas has escalated, posing substantial challenges to sustainable development and operational safety. This subsidence, resulting directly from mining activities, significantly compromises the safety of nearby residents by damaging residential structures and infrastructure. Thus, developing precise and dependable methods for predicting ground subsidence is crucial. This study introduces an innovative Cabs-Unet model, which enhances the U-Net architecture by integrating a Convolutional Block Attention Module (CBAM) and Depthwise Separable Convolutions (DSC). This model aims to predict the spatiotemporal dynamics of the Interferometric Synthetic Aperture Radar (InSAR) time series. Employing Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) technology, we gathered and validated data on ground subsidence at the Pengzhuang coal mine from May 2017 to November 2021, covering 130 scenes, with its accuracy corroborated by levelling survey results. An empirical evaluation of the Cabs-Unet model in two distinct subsidence zones demonstrated superior performance over conventional methods like Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN), with Root Mean Square Error (RMSE) values of 1.44 and 1.70, respectively. These findings highlight the model’s efficacy in accurately predicting spatiotemporal InSAR ground subsidence. Further predictive analysis using InSAR data indicated an expected increase in subsidence, projecting cumulative declines of −457 mm in Area A and −1278 mm in Area B by 17 July 2022. Our model proves effective in assessing subsidence, promptly detecting potential risks and facilitating the rapid implementation of risk mitigation strategies.

Modelling permafrost deformation with InSAR technology considering soil moisture variation and spatial-temporal heterogeneity of the freeze‒thaw process

ABSTRACT Monitoring and modelling surface deformation are crucial components of understanding the freeze‒thaw process and preventing disasters in permafrost regions. However, previous methods had limitations that inhibited the interpretation of freeze‒thaw deformation, such as a lack of physical meaning, an inability to reflect the physical freeze–thaw process and consideration of only a single external factor’s impact on permafrost deformation. This study proposes an improved degree-day model (IDM) for quantitatively isolating surface deformation using interferometric synthetic aperture radar (InSAR) technology over permafrost. We considered the effect of soil moisture variation on permafrost deformation and incorporated interannual variation in the freeze‒thaw process due to climate change. By applying small baseline subset (SBAS) technology to Sentinel-1 InSAR measurements over the Wudaoliang permafrost region on the Qinghai‒Tibet Plateau from 2018 to 2019, we estimated long-term and seasonal permafrost deformation. The reliability of InSAR results was validated using in situ measurements, with root mean square errors (RMSEs) less than 10 mm. The results showed that the average linear deformation rates in 2018 and 2019 were −3.8 mm a−1 and −11.0 mm a−1, respectively, and the maximum seasonal deformations were 15.7 mm and 13.2 mm, respectively. Compared with the original degree-day model (ODM), the method used in this study produced smaller residual deformations of 6.9 mm and 6.4 mm, highlighting its ability to improve a quantitative description of permafrost deformation.

Remote Gait Analysis Using Ultra-Wideband Radar Technology Based on Joint Range-Doppler-Time Representation.

In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments. A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers. On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0. Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait analysis, with quality similar to gold standard assessment. Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.

Analysis of Deformation and Flood Inundation Spatial Distribution Using Sentinel-1 SAR Data for The 2021 South Kalimantan Floods

Abstract Major flood disasters that have occurred in Indonesia, one of which occurred in South Kalimantan in early 2021. This flood disaster was caused by high rainfall intensity. There are several relationships between rainfall, flood, and deformation. The objective of this study is to monitor deformation in flood inundation that occurred during the flood disaster in South Kalimantan. Observations with direct measurements in the field provide high data accuracy, but in its implementation there are limitations. Therefore, we utilize the development of Synthetic Aperture Radar (SAR) technology in observing flood inundation spatial distribution and deformation in South Kalimantan. Using SAR cannot be as accurate as direct measurements in the field, but it is effective in saving time, can be applied to wide area coverage, and can be observed multitemporally. The SAR data used is Sentinel-1 imagery with C-band which can be freely accessed and has high spatial and temporal resolution. In flood mapping with backscatter using change detection, the flood inundation distribution was 2022.558 km2. The results show deformation variations in the form of land subsidence of up to 15.9 cm in the flood distribution area.

Identification of complex system radar emitter source based on one-dimensional convolutional neural network

Abstract With the continuous innovation of radar technology, the signal modulation of radar emitter sources has become more complex, which makes their identification face more challenges. We propose a method based on a one-dimensional convolutional neural network for the identification of complex system radar emitter sources and conduct an experiment on eight radar emitter sources with similar parameters and complex inter-pulse modulation. The identification accuracy reaches 88.24%.

Clustering and Cooperative Guidance of Multiple Decoys for Defending a Naval Platform against Salvo Threats

The threat to naval platforms from missile systems is increasing due to recent advancements in radar seeker technology, which have significantly enhanced the accuracy and effectiveness of missile targeting. In scenarios where a naval platform with limited maneuverability faces salvo attacks, the importance of an effective defense strategy becomes crucial to ensuring the protection of the platform. In this study, we present a multi-agent reinforcement learning-based decoy deployment approach that employs six decoys to increase the survival likelihood of a naval platform against salvo missile strikes. Our approach entails separating the decoys into two clusters, each consisting of three decoys. Subsequently, every cluster is allocated to a related missile threat. This is accomplished by training the decoys with the multi-agent deep reinforcement learning algorithm. To compare the proposed approach across different algorithms, we use two distinct algorithms to train the decoys; multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed deep deterministic policy gradient (MATD3). Following training, the decoys learn to form groups and establish effective formation configurations within each group to ensure optimal coordination. We assess the proposed decoy deployment strategy using parameters including decoy deployment angle and maximum decoy speed. Our findings indicate that decoys positioned on the same side outperform those positioned on different sides relative to the target platform. In general, MATD3 performs slightly better than MADDPG. Decoys trained with MATD3 succeed in more successful formation configurations than those trained with the MADDPG method, which accounts for this enhancement.

Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study.

In the last few decades, major progress has been made in the medical field; in particular, new treatments and advanced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life. As a consequence, the number of elderly people is expected to increase in the following years. This trend, along with the need to improve the independence of frail people, has led to the development of unobtrusive solutions to monitor daily activities and provide feedback in case of risky situations and falls. Monitoring devices based on radar sensors represent a possible approach to tackle postural analysis while preserving the person's privacy and are especially useful in domestic environments. This work presents an innovative solution that combines millimeter-wave radar technology with artificial intelligence (AI) to detect different types of postures: a series of algorithms and neural network methodologies are evaluated using experimental acquisitions with healthy subjects. All methods produce very good results according to the main parameters evaluating performance; the long short-term memory (LSTM) and GRU show the most consistent results while, at the same time, maintaining reduced computational complexity, thus providing a very good candidate to be implemented in a dedicated embedded system designed to monitor postures.

Investigating hailstorm updrafts and nowcasting hail size using a novel radar-based updraft detection

Abstract Nowcasting hail size poses a major challenge in operational practice due to physical limitations of weather radar technology once hailstones are sufficiently large to enter the resonance scattering regime. Numerous radar-based hail size proxies have been derived in recent decades, but their performance is generally poor in identifying giant hail (≥ 10 cm). Using a novel thunderstorm updraft detection method, we examine the updraft characteristics of hailstorms in the U.S. Great Plains based on a NEXRAD dataset of 114 hail events between 2013 and 2023. We find that some radar-derived variables within the detected updraft are well suited for discriminating between small (1.0 - 3.0 cm) and severe (≥ 3.5 cm) hail, e.g. minimum co-polar cross-correlation coefficient in the mid-level updraft, whereas other radar metrics such as the area of reflectivity > 50 dBZ in the upper portion of the updraft suggest the presence of giant hail. However, the statistical distributions of each variable overlap for different hail sizes and there is no single metric which performs well across the entire hail size spectrum. Therefore, we trained a Random Forest model to nowcast hail size categories using a multitude of these radar metrics. The model shows promising performance for discriminating hail sizes > 5 cm but requires further refinement for smaller hail. We showcase the model’s capabilities for a set of hailstorms in the Great Plains.