Radar Sensor Research Articles

Integrating satellite-based forest disturbance alerts improves detection timeliness and confidence

Satellite-based near-real-time forest disturbance alerting systems have been widely used to support law enforcement actions against illegal and unsustainable human activities in tropical forests. The availability of multiple optical and radar-based forest disturbance alerts, each with varying detection capabilities depending mainly on the satellite sensor used, poses a challenge for users in selecting the most suitable system for their monitoring needs and workflow. Integrating multiple alerts holds the potential to address the limitations of individual systems. We integrated radar-based RAdar for Detecting Deforestation (RADD) (Sentinel-1), and optical-based Global Land Analysis and Discovery Sentinel-2 (GLAD-S2) and GLAD-Landsat alerts using two confidence rulesets at ten 1° sites across the Amazon Basin. Alert integration resulted in faster detection of new disturbances by days to months, and also shortened the delay to increased confidence. An increased detection rate to an average of 97% when combining alerts highlights the complementary capabilities of the optical and cloud-penetrating radar sensors in detecting largely varying drivers and environmental conditions, such as fires, selective logging, and cloudy circumstances. The most improvement was observed when integrating RADD and GLAD-S2, capitalizing on the high temporal observation density and spatially detailed 10 m Sentinel-1 and 2 data. We introduced the highest confidence class as an addition to the low and high confidence classes of the individual systems, and showed that this displayed no false detection. Considering spatial neighborhood during alert integration enhanced the overall labeled alert confidence level, as nearby alerts mutually reinforced their confidence, but it also led to an increased rate of false detections. We discuss implications of this study for the integration of multiple alert systems. We demonstrate that alert integration is an important data preparation step to make use of multiple alerts more user-friendly, providing stakeholders with reliable and consistent information on new forest disturbances in a timely manner. Google Earth Engine code to integrate various alert datesets is made openly available.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Micro-Doppler Signature Analysis for Space Domain Awareness Using VHF Radar

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing.

Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights

The advancement of Advanced Driver Assistance Systems (ADAS) marks a pivotal evolution in automotive technology, aiming to enhance road safety and driving efficiency through a wide array of functionalities like blind spot detection, emergency braking, and adaptive cruise control. This research paper delves into the operational integrity, performance metrics, and maintenance strategies of ADAS components, underpinned by a comprehensive methodology involving data collection, pre-processing, feature engineering, machine learning model development, and rigorous validation processes. Systematic inspection of ADAS components indicates their importance in vehicle safety and reliability. The visibility, distance, speed, and steering angle of front cameras, LiDAR, radar, and ultrasonic sensors are carefully evaluated. Maintenance logs show proactive error code management, boosting efficiency. SVM, Gradient Boosting, and Random Forest machine learning models predicted ADAS component failures during validation and testing. Random Forest scored 90% accuracy, 92% precision, 88% recall, and 90% F1. Gradient Boosting was the most accurate, with 93% accuracy, 94% precision, 91% recall, and 92% F1. SVM predicted ADAS component failures with 88% accuracy, 90% precision, 85% recall, and 87% F1 score. Machine learning helps shift from reactive to proactive maintenance. Modelling sensor signal quality, actuator reaction times, error code frequencies, and maintenance intervals enables predictive maintenance and failure detection. Feature engineering builds predictive models using maintenance logs and operational KPIs. The models predict ADAS component failures, boosting vehicle safety and dependability. Using external data improves predictive maintenance models. The maintenance model's adaptability and forecast accuracy are proved by ADAS operation after traffic, accident, and manufacturer upgrades. Predictive maintenance and machine learning improve ADAS dependability and safety, the study found. Advanced analytics and data-driven insights can reduce automotive system failures, improving safety and reliability.

People counting using IR-UWB radar sensors and machine learning techniques

This study aims to detect and count people using impulse radio ultra-wideband radar and machine learning algorithms. However, the data quality, difficulty distinguishing human signals from noise and clutter, and instances where human presence is not detected make it challenging to count multiple humans. To overcome these challenges, we apply wavelet transformation to reduce signal size and use simple moving averages to eliminate noise. Next, we create features based on statistical and entropic properties of the signal and apply several classification algorithms, including ANN, Random Forest, KNN, XGBOOST, and multiple linear regression, to predict the number of people present. Our findings reveal that using the ANN classifier with the Daubechies 4 (db4) wavelet provides better results than other classifiers, with an accuracy rate of 99%. Additionally, filtering the data improves accuracy, and labeling the data after extracting essential characteristics significantly improves the model’s accuracy.

A framework for automated landslide dating utilizing SAR-Derived Parameters Time-Series, An Enhanced Transformer Model, and Dynamic Thresholding

Determining the timing of landslide occurrence is crucial for establishing an accurate, comprehensive and systematic landslide inventory while assessing the potential for reducing landslide risk. Unfortunately, many existing landslide inventories lack temporal information such as the precise time of landslide events. Optical and Synthetic Aperture Radar (SAR) sensors are the most commonly used remote sensing technologies for landslide detection. Unlike optical sensors, SAR sensors are not affected by cloudy conditions and provide valuable imagery regardless of sunlight availability. Therefore, SAR-derived parameters, i.e., SAR amplitude, interferometric coherence, and polarimetric features (alpha and entropy), offer a higher temporal resolution for detecting landslide occurrence times compared to optical data. Despite the advantages, there is currently no universally accepted automatic method for determining the time of landslide events using SAR data. This is due to the lack of anomaly labels and the high time-series volatility in detecting landslide occurrence times. Despite advances in deep-learning methods for anomaly detection in time-series, only a few of them can address these challenges in our case. In this paper, we propose an unsupervised multivariate transformed-based deep-learning model to automatically and efficiently estimate landslide occurrence times using multivariate SAR-derived parameters time-series analysis. The designed gated relative position can increase robustness and temporal context information, by learning global temporal trends in the time-series. Subsequently, the time-series of the anomaly score derived from the proposed Transformer model is analyzed using an adaptive thresholding strategy to dynamically and automatically mark anomalies related to the landslide occurrence. Our research focuses on collapsed landslides characterized by dramatic changes in ground surface topography, with a particular attention for the need of a prior knowledge about landslide boundaries. We assess the performance of the proposed methodology for several collapsed landslides including the July 21, 2020 Shaziba and 23 July, 2019 Shuicheng landslides in China, March 19, 2019 Takht landslide in Iran, June 15, 2018 Jalgyz-Jangak and May 25, 2018 Kugart landslides in Kyrgyzstan, July 7, 2018 Hitardalur landslide in Iceland, and January 25, 2019 Brumadinho landslide in Brazil. In comparison to commonly used neural networks like the LSTM algorithm, our proposed framework leads to a more accurate estimate for the time of landslide failure using time-series of SAR-derived parameters. Furthermore, our results suggest the great potential of SAR data to narrow the time period detected from optical data when used in conjunction with them.

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Discrimination of various upper structures and vehicles using radar cross section of frequency‐modulated continuous‐wave radar

AbstractAlthough radar sensors have been known for their robustness in challenging environmental conditions, they face difficulties in distinguishing between upper structure (US), such as road signs, traffic lights, overpasses, and pedestrian bridges, and forward vehicle (FV). This misclassification can lead to false alarms and unwanted braking events. In this paper, the authors propose a method distinguishing between US and FV at different distances based on change in radar cross section (RCS). The study collects real‐world RCS data from various objects identified as US and FV during road driving scenarios.

Antenna Array and Waveform Design for 4-D-Imaging mmWave MIMO Radar Sensors

Antenna Array and Waveform Design for 4-D-Imaging mmWave MIMO Radar Sensors

面向对海监视的舰船目标跟踪与航迹融合数据集

Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, uniform data formats, and incomplete data descriptions. We used simulation software to emulate multi-sensor multi-target detection data in complex scenarios, so as to present a dataset tailored for typical maritime surveillance scenarios—targeting ships using 2D radar and Electronic Support Measures (ESM) sensors. The simulation software comprises a scenario generator and a sensor simulator, creating a mature target tracking scenario simulation environment with realistic detection data modeling capabilities. The dataset includes data from 2D radar and ESM sensors, covering typical maritime ship categories, supporting configurations with radiation sources. It is designed for a variety of scenarios such as high-speed motion, dense traffic, multi-sensor data fusion, specific ship detection, and cross-positioning. The dataset contains a total of 368,155 target tracks from 101 ships, spanning a duration of 15,000 seconds. The data format conforms to actual equipment reporting scenarios, while the detection error model accurately reflects real-world conditions. Accuracy assessment and data completeness are ensured through various methods including normality testing of data error, scenario testing of detection rate and false alarm rate, as well as field research. This dataset can provide fundamental data for algorithmic research and validation in the ship's target tracking, trajectory fusion and other related areas.

Integration of Sentinel-1A Radar and SMAP Radiometer for Soil Moisture Retrieval over Vegetated Areas.

NASA's Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA's Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields.

Bayesian Gaussian Mixture Models for Enhanced Radar Sensor Modeling: A Data-Driven Approach towards Sensor Simulation for ADAS/AD Development.

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar's perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar's perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model's effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model's applicability.

Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar

Detecting vulnerable road users is a major challenge for autonomous vehicles due to their small size. Various sensor modalities have been investigated, including mono or stereo cameras and 3D LiDAR sensors, which are limited by environmental conditions and hardware costs. Radar sensors are a low-cost and robust option, with high-resolution 4D radar sensors being suitable for advanced detection tasks. However, they involve challenges such as few and irregularly distributed measurement points and disturbing artifacts. Learning-based approaches utilizing pillar-based networks show potential in overcoming these challenges. However, the severe sparsity of radar data makes detecting small objects with only a few points difficult. We extend a pillar network with our novel Sparsity-Robust Feature Fusion (SRFF) neck, which combines high- and low-level multi-resolution features through a lightweight attention mechanism. While low-level features aid in better localization, high-level features allow for better classification. As sparse input data are propagated through a network, the increasing effective receptive field leads to feature maps of different sparsities. The combination of features with different sparsities improves the robustness of the network for classes with few points.

Joint multitemporal SAR and optical mapping of urban changes

AbstractThis paper presents a methodology designed to leverage multitemporal sequences of synthetic aperture radar (SAR) and multispectral data and automatically extract urban changes. The approach compares results using different radar and optical sensors, describing the advantages and drawbacks of using SAR data from the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO)/SkyMed, SAtélite Argentino de Observación COn Microondas (SAOCOM), and Sentinel-1 constellations, as well as nighttime light data or Sentinel-2 images. Multiple indexes obtained from multispectral data are compared, too, and results obtained by an unsupervised clustering procedure are analyzed. The results show that using different datasets it is possible to obtain consistent results about different types of changes in urban areas (e.g., demolition, development, and densification) with different levels of spatial details.

Non-contact Respiration Monitoring Using Bio- Radar Sensor Based on Linear Regression Classifier

Tuberculosis (TB) is an infectious disease that mainly attacks the lungs, caused by the bacterium Mycobacterium tuberculosis. To reduce its spread, hospitals use special rooms for TB patients and health workers follow strict Standard Operating Procedures (SOP). Recent advances in medical technology have led to the development of contactless respiratory monitoring techniques, such as bio-radar sensors that utilize the Doppler principle to detect lung movement. This research aims to explore the application of bio-radar sensors for contactless respiratory rate monitoring and then combine it with machine learning methods, specifically using linear regression algorithms, to translate bio-radar output into measurable respiratory rate values. By training a regression model using a processed raw data set to identify inspiration and expiration, where 1 is inspiration and 0 is expiration. To test the performance of the contactless breathing module, it was compared to a patient monitor. The module and comparison tool were run simultaneously with 10 measurement distance points for 10 patients or respondents with each distance point taken three times. The data that has been obtained from the results of comparisons between modules and comparison tools is entered into machine learning data analysis techniques, namely accuracy, precision and recall. The accuracy results were 74.9%, precision 71.4% and recall 83.3%. This research has proven that bio radar can be used to detect lung movement.

Navigating the future: A comprehensive survey of localization systems in autonomous vehicles

<p>This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.<strong></strong></p>

Path Planning for Heterogeneous UAVs With Radar Sensors

Path Planning for Heterogeneous UAVs With Radar Sensors

Navigating the future: A comprehensive survey of localization systems in autonomous vehicles

<p>This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.<strong></strong></p>

Cooperative Game Theoretic Power Allocation Method to Distributed MIMO Radar Sensor Network for Multitarget Detection in Low-Altitude

Cooperative Game Theoretic Power Allocation Method to Distributed MIMO Radar Sensor Network for Multitarget Detection in Low-Altitude