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Interferometric radars are widely used sensors for structural health monitoring. They are able to perform dynamic measurements of displacement with sub-millimeter precision. Today, the Ku-band is the most common, due to the spread of commercial systems operating in this band. At the same time, the W-band sensors are gaining ever more interest. Other popular systems work in the X-band. Since the characteristics of the measurements dramatically depend on the operative frequency, it is essential to highlight their differences. For instance, higher frequency allows for high displacement resolution, but it is more subject to phase wrapping and decorrelation effects. In this paper, a direct comparison between radars operating in X, Ku, and W-band for bridge monitoring is carried out. The radars provide frequency-modulated continuous-wave signals. Experimental campaigns were performed both in controlled and realistic scenarios (a stayed bridge). The results of the experiments demonstrate that all the three sensors are suitable for performing dynamic structure monitoring despite their differences. It is worth noting that this comparative analysis has highlighted the role of amplitude variation in phase/displacement measurement. Regarding this point, the three different bands exhibit significant differences. [ABSTRACT FROM AUTHOR]

An experimental study aimed at identifying special rainfall regimes with the help of co-located disdrometers is performed. Eight potentially special events (i.e., four number-controlled events and four size-controlled events) are identified and examined. However, a detailed cross-check with additional, independent radar measurements reveals no clear evidence of special rainfall dynamics. The research underscores the difficulty of experimentally confirming seemingly straightforward questions about rainfall patterns and dynamics that have been theorized in the literature for several decades but never formally validated experimentally. The study also questions the reliability of previous claims and serves as a reminder to approach such problems with more caution, emphasizing the need for rigorous uncertainty analysis and multiple cross-checks between sensors to avoid misinterpretation. [ABSTRACT FROM AUTHOR]

In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for spoofing attacks in radar target detection (RTD). Multi-level adversarial attack prevention using deep learning is designed for the coherence pulse deep feature map from DAALnet and Range-Doppler (RD) map from TDDLnet. After the discrimination of the attack, optimization of hybrid deep learning (OHDL) integrated with enhanced PSO is used to predict the range and velocity of the target. Simulations are performed to evaluate the sensitivity of AOHDL for different radar environment configurations. RMSE of AOHDL is almost the same as OHDL without attack conditions and it outperforms the earlier RTD implementations. [ABSTRACT FROM AUTHOR]

When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) offer significant value through their capability to undertake hazardous and time-consuming missions across water surfaces. Recently, as the application of USVs has been extended to nearshore waterways, object tracking is vital to the safe navigation of USVs in offshore scenes. However, existing tracking systems for USVs are mainly based on cameras or LiDAR sensors, which suffer from drawbacks such as lack of depth perception or high deployment costs. In contrast, millimeter-wave (MMW) radar offers advantages in terms of low cost and robustness in all weather and lighting conditions. In this work, to construct a robust and low-cost tracking system for USVs in complex offshore scenes, we propose a novel MMW radar-based object tracking method (ROTracker). The proposed ROTracker combines the physical properties of MMW radar with traditional tracking systems. Specifically, we introduce the radar Doppler velocity and a designed motion discriminator to improve the robustness of the tracking system toward low-speed targets. Moreover, we conducted real-world experiments to validate the efficacy of the proposed ROTracker. Compared to other baseline methods, ROTracker achieves excellent multiple object tracking accuracy in terms of 91.9% in our collected dataset. The experimental results demonstrated that the proposed ROTracker has significant application potential in both accuracy and efficiency for USVs, addressing the challenges posed by complex nearshore environments. [ABSTRACT FROM AUTHOR]

For calibrating the measurement error of the speed sensor and estimating the speed accurately while system is nonlinear and noise is non-Gaussian, dual radar and wheel axle sensor are configured and federal particle filter is used to acquire fusion. The whole operation process of CRH3 train from start to stop between two stations is taken as an example, the speed measurement errors of different speed estimation methods under the conditions of train idling, slipping, vibration and the speed sensor with dynamic noise are analyzed and verified. The simulation results show that: The root-mean-square error can be reduced by 31.52% and 47.35% respectively in the idling and sliding stages after the calibration of dual-radar and wheel-axle combination compared with no calibration; Compared with the two types of dual-radar calibration methods, the maximum relative error of the dual-radar separation vibration speed calibration method can be 39.66% lower than that of the dual-radar angle deviation estimation calibration method when the train vibration speed ratio is 0 to 1 and the radar installation angle error is -1° to 1°. Compared with the filtering results of dual radar and axle sensors before fusion, the speed measurement results after using joint particle filtering fusion are 34.71% and 14.03% lower in MAE, and 26.51% and 10.98% lower in RMSE respectively. Compared with federated extended Kalman filter fusion, the root mean square error of velocity measurement using federated particle filter fusion is 26.97% lower and the maximum absolute error can be reduced by 16.10%. [ABSTRACT FROM AUTHOR]

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane. [ABSTRACT FROM AUTHOR]

The article presents the analysis, design, and low-cost implementation of application-specific AD converters for M-sequence-based UWB applications to minimize and integrate the whole UWB sensor system. Therefore, the main goal of this article is to integrate the AD converter's own design with the UWB analog part into the system-in-package (SiP) or directly into the system-on-a-chip (SoC), which cannot be implemented with commercial AD converters, or which would be disproportionately expensive. Based on the current and used UWB sensor system requirements, to achieve the maximum possible bandwidth in the proposed semiconductor technology, a parallel converter structure is designed and presented in this article. Moreover, 5-bit and 4-bit parallel flash AD converters were initially designed as part of the research and design of UWB M-sequence radar systems for specific applications, and are briefly introduced in this article. The requirements of the newly proposed specific UWB M-sequence systems were established based on the knowledge gained from these initial designs. After thorough testing and evaluation of the concept of the early proposed AD converters for these specific UWB M-sequence systems, the design of a new AD converter was initiated. After confirming sufficient characteristics based on the requirements of UWB M-sequence systems for specific applications, a 7-bit AD converter in low-cost 0.35 µm SiGe BiCMOS technology from AMS was designed, fabricated, and presented in this article. The proposed 7-bit AD converter achieves the following parameters: ENOB = 6.4 bits, SINAD = 38 dB, SFDR = 42 dBc, INL = ±2-bit LSB, and DNL = ±1.5 LSB. The maximum sampling rate reaches 1.4 Gs/s, the power consumption at 20 Ms/s is 1050 mW, and at 1.4 Gs/s is 1290 mW, with a power supply of −3.3 V. [ABSTRACT FROM AUTHOR]

Supremacy in the electromagnetic spectrum provides additional options and increased flexibility in any competition, even other than military conflicts, in addition to greater control of the scale of escalation. It gives policy makers and military leaders the decision space to manage events in real time while denying equivalent decision space to adversaries. At the same time, supremacy in the electromagnetic spectrum requires the collection, analysis and validation of robust information in the following key areas: electromagnetic spectrum sensors, communications, data links, radars, jamming, directed energy and infrared systems, engineering data describing the performance, characteristics and signature information of associated equipment, weapons and platforms, combat support data and modelling and simulation support. The authors describe these topics through a detailed analysis of the importance and role of supremacy in the electromagnetic spectrum. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications. [ABSTRACT FROM AUTHOR]

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°. [ABSTRACT FROM AUTHOR]

This paper delves into the problem of moving-target detection in partially homogeneous environments (PHE) with unknown Gaussian disturbance using a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. Using training data, we have derived expressions for four adaptive detectors, including the one-step and two-step generalized likelihood ratio test (GLRT), two-step Rao (TRao) test, and two-step Wald (TWald) test criteria, respectively. All the proposed detectors are characterized by the constant false-alarm rate (CFAR). The theoretical analysis and simulation results validate the effectiveness of the proposed detectors. [ABSTRACT FROM AUTHOR]

RADARs and cameras have been present in automotives since the advent of ADAS, as they possess complementary strengths and weaknesses but have been underlooked in the context of learning-based methods. In this work, we propose a method to perform object detection in autonomous driving based on a geometrical and sequential sensor fusion of 3+1D RADAR and semantics extracted from camera data through point cloud painting from the perspective view. To achieve this objective, we adapt PointPainting from the LiDAR and camera domains to the sensors mentioned above. We first apply YOLOv8-seg to obtain instance segmentation masks and project their results to the point cloud. As a refinement stage, we design a set of heuristic rules to minimize the propagation of errors from the segmentation to the detection stage. Our pipeline concludes by applying PointPillars as an object detection network to the painted RADAR point cloud. We validate our approach in the novel View of Delft dataset, which includes 3+1D RADAR data sequences in urban environments. Experimental results show that this fusion is also suitable for RADAR and cameras as we obtain a significant improvement over the RADAR-only baseline, increasing mAP from 41.18 to 52.67 (+27.9%). [ABSTRACT FROM AUTHOR]

Recently, radar sensors have been extensively used for vital sign monitoring in dogs, owing to their noncontact and noninvasive nature. However, a public dataset on dog vital signs has yet to be proposed since capturing data from dogs requires special training and approval. This work presents the first ever ultra wideband radar-based dog vital sign (UWB-DVS) dataset, which was captured in two independent scenarios. In the first scenario, clinical reference sensors are attached to the fainted dogs, and data from UWB radar and reference sensors are captured synchronously. In the second scenario, the dogs can move freely, and video recordings are provided as a reference for movement detection and breathing extraction. For technical validation, a high correlation, above 0.9, is found between the radar and clinical reference sensors for both the heart rate and breathing rate measurements in scenario 1. In scenario 2, the vital signs and movement of the dogs are shown in the form of dashboards, demonstrating the long-term monitoring capability of the radar sensor. [ABSTRACT FROM AUTHOR]

This paper deals with adaptive moving target detection for a forward scatter radar in complex Gaussian noise. The echoes received by the forward scatter radar include not only the noise and the possible target signals but also the direct signals. To suppress the direct signals and detect the target signals, Rao and Wald tests are derived in two cases: the secondary data which contain no target signal are available or not available. Different from monostatic radar, it is proved that the derived Rao and Wald detectors for the forward scatter radar have the same test statistics as the generalized likelihood ratio test-based detector in the complex Gaussian noise both when the secondary data are available or not available. The numerical evaluation further demonstrates the equivalence and the effectiveness of the proposed detectors. [ABSTRACT FROM AUTHOR]

Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user's pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range. [ABSTRACT FROM AUTHOR]

The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017–2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation. [ABSTRACT FROM AUTHOR]

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services. [ABSTRACT FROM AUTHOR]

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In recent years, the development of Advanced Driver-Assistance Systems (ADASs) is driving the need for more reliable and precise on-vehicle sensing. Radar and lidar are crucial in this framework, since they allow sensing of vehicle's surroundings. In such a scenario, it is necessary to master these sensing systems, and knowing their similarities and differences is important. Due to ADAS's intrinsic real-time performance requirements, it is almost mandatory to be aware of the processing algorithms required by radar and lidar to understand what can be optimized and what actions can be taken to approach the real-time requirement. This review aims to present state-of-the-art radar and lidar technology, mainly focusing on modulation schemes and imaging systems, highlighting their weaknesses and strengths. Then, an overview of the sensor data processing algorithms is provided, with some considerations on what type of algorithms can be accelerated in hardware, pointing to some implementations from the literature. In conclusion, the basic concepts of sensor fusion are presented, and a comparison between radar and lidar is performed. [ABSTRACT FROM AUTHOR]

Rural areas often face communication challenges due to limited mobile coverage on remote roads, posing significant difficulties in reporting emergencies and accidents. This study presented an autonomous vehicle tracking system using low-cost radar sensors to detect possible emergencies in the sections of roads with low cell coverage. The radar sensor system could determine the number of vehicles that passed through the nodes and classify them based on the vehicle type. Each node within the system is equipped with ten radars, a processor unit, and a radio transmitter to communicate with the network in real-time, achieving a rapid response time of just 0.2 s. To ensure seamless connectivity, two distinct wireless communication networks are employed, one for the connection between the towers in the same node and the other for the connection between nodes and a center with cellular coverage. The results of this study can be useful in conveying emergency messages, as well as traffic management. [ABSTRACT FROM AUTHOR]

A 19‐GHz low‐phase‐noise PLL‐frequency synthesizer is presented in this letter that is intended to be used in a K‐band FMCW radar which requires strict phase noise for detecting a small unmanned aerial vehicle. The 17.9 to 19.5‐GHz PLL includes a Darlington cross‐coupled capacitive‐degeneration VCO which presents higher maximum attainable oscillation frequency (ƒtrans) and larger negative transconductance (gm), thus resulting in low phase noise. Manufactured using the SiGe BiCMOS process, the K‐band synthesizer demonstrates −114 dBc/Hz at 1 MHz offset and −138 dBc/Hz at 10 MHz offset from 19.456 GHz locking carrier frequency. [ABSTRACT FROM AUTHOR]

The article focuses on the privacy implications of active sensors in today's technology-driven world. It discusses how active sensors, such as radar, LiDAR, and sonar, can significantly impact privacy and the need for regulations to address these concerns. It also explores the concept of "creepy" technologies, which are new technologies or uses of existing technologies that challenge traditional social norms and may not be adequately regulated by current privacy laws.

Leg tracking is an established field in mobile robotics and machine vision in general. These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail. Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state‐of‐the‐art performance, and that laser and fusion tracker outperform recent methods. [ABSTRACT FROM AUTHOR]

Inspection of the railway tracks requires relying on a precise positioning system that accurately determines the location of potential failures and deficiencies. Therefore, measuring the speed and precise location of rail vehicles is essential. Many methods and approaches have been proposed, but most of them lack several conditions that do not allow them to be optimal for this task. In this paper, the authors present a novel method for measuring the speed of a train by means of the time correlation approach, by using 2‐mm wave radar sensors. Additionally, it is also an accurate positioning system because the authors prove that the track has a unique profile, sufficiently characteristic and stable over time, that serves as a distinctive signature, which can be used to locate the vehicle at any point on that track. As a result, this new methodology, combined with a laser profilometer device, can be used as an effective inspection system for cracks or deficiencies in the rails, sleepers, fastenings, bolts etc., locating them precisely on the railway track. [ABSTRACT FROM AUTHOR]

Rice is a staple food that feeds nearly half of the world's population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions. [ABSTRACT FROM AUTHOR]

This paper focuses on the analysis of traffic streams on two-lane highways, which are crucial components of transportation networks. Traffic flow measurement technologies, such as detection stations, radar guns, or video cameras, have been used over the years to detect the level of traffic and the operating conditions. This type of sensor can record a large amount of data which is useful to evaluate and monitor road traffic conditions, and it is possible to identify free-moving and constrained vehicles by processing the collected data. This study introduces an exponential headway model to identify the headway threshold above which vehicles can be considered as unconditioned. However, this value could identify vehicles which still retain some autonomy in their speed and maneuvering. Therefore, an additional criterion to distinguish between apparently and actually conditioned vehicles has been introduced, analyzing the speed differences between a vehicle and the preceding one. Three-month sequences of traffic monitored through radar devices placed on some Italian two-lane roads have been analyzed and an exponential headway model has been introduced, as an illustrative example. The results show that introducing the criterion of maneuvering freedom can significantly improve traffic flow analysis, modifying the starting critical values of 4 and 8 s per each studied section, to 2.5 and 3 s, approaching the values suggested by international manuals for traffic flow quality analysis. [ABSTRACT FROM AUTHOR]

Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AVs) are capable of performing safety-critical control functions on their own by apprehending their environment through numerous sensor systems. However, there remain several limitations in regard to the AV sensors' operability and performance under adverse weather conditions. Diminishing sensor operability may be extremely high risk and generally negatively affect the safety of AVs, which may cause complications on the road and the rider. This research assessed the safety of AVs in weather conditions in the Philippines. CARLA simulation is studied and programmed, with the most common AV sensors integrated into CARLA such as LiDAR, RADAR, and Lane Detection Camera. An extensive literature review is conducted to authenticate the simulation results due to the nature of the research - Conducted in a virtual world. The findings from CARLA in regard to the sensors' operability under Philippine weather conditions are compared to standards set by the International Organization for Standardization (ISO). The results are hypothesized to show that AVs are safe under Philippine weather conditions, as sensor operability under such weather conditions is expected to have a drop-off of no more than 30% compared to clear weather conditions. [ABSTRACT FROM AUTHOR]

In this letter, a constant false alarm rate (CFAR) detector is proposed for skywave over-the-horizon radar (OTHR) under Weibull distribution. First, an objective function, composed of a data fidelity term and regularization terms, is formulated in the Bayesian framework. The low rank prior of the distribution parameter matrix, smoothness of the parameters along the Doppler/range dimension, and sparsity of the target in the range-Doppler (RD) matrix are used to regularize the objective function. Then, the distribution parameters of all cells under test (CUTs) in the RD matrix are estimated by optimizing the objective function, and the abnormal CUTs with targets are identified and separated. Finally, target detection is performed by calculating the detection threshold through the threshold coefficient and estimated distribution parameters. Experiments on OTHR real data demonstrate that the proposed detector has a good CFAR property and significantly improved the probability of detection, especially in the case of multitarget and clutter edge. [ABSTRACT FROM AUTHOR]

To solve the problem of range-spread targets detection in non-Gaussian clutter for dual-polarimetric radar systems, a novel dual-polarimetric persymmetric detector based on the two-step generalized likelihood ratio test (GLRT) is proposed. First, the target signal is modeled as a multi-rank subspace signal, and the clutter is modeled as a compound Gaussian model with deterministic unknown texture. Then, by taking polarimetric and persymmetric characteristics of the clutter into account, the detection performance of the proposed detector is obviously improved. Finally, through the verification via both simulated data and measured data, it is shown that the proposed detector poses better detection performance in comparison with the existing nonpolarimetric detectors. Due to the complexity of the detector form, the analytical expression of false alarm probability cannot be derived. Therefore, we verified the constant false alarm ratio (CFAR) characteristic of the proposed detector through a simulation experiment. [ABSTRACT FROM AUTHOR]

Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture. More precisely, we derive novel closed-form expressions for the probability of detection ($P_{\text {D}}$) and the probability of false alarm ($P_{\text {FA}}$) that can be readily evaluated using any mathematical software. Monte-Carlo (MC) simulations corroborate our analytical findings. [ABSTRACT FROM AUTHOR]

An increasing number of vehicles are equipped with cameras. As perception sensors, they scan the surrounding area and supply the Advanced Driver Assistance Systems (ADAS) for building up an environmental model through the use of computer vision techniques. While they perform well under good weather conditions their efficiency is reduced by adverse environmental influences such as rain, fog and occlusion through dirt. As a consequence, the vision based ADAS obtains poor quality information, and the model also becomes faulty. This paper deals with methods to estimate information quality of cameras in order to warn the assistance system of possible wrong working conditions. In particular, situations of contamination or occlusion of the windshield or camera lens, as well as foggy weather are taken into account in this paper. In the issue of occlusion total, fractional and transparent effectuations have to be recognized and distinguished. Therefore, this paper proposes an approach based on edge analysis of consecutive frames and presents initial experimental results of the implementation. In the field of Fog Detection a method based on the Logarithmic Image Processing Model is described and the results are shown. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the 'Carol I' National Defence University / Buletinul Universitatii Nationale de Aparare 'Carol I' is the property of Carol I National Defence University 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 presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles. [ABSTRACT FROM AUTHOR]

SAR images are used in many applications such as building detection. Extracting the building is very challenging due to the radar nature of the SAR images. However, due to the advantages of radar images such as day and night imaging, building extraction from SAR images is a hot topic. In this context, one of the main challenges is the effect of building orientation on the profile created in the SAR image. Also, the two geometric distortions of shadow and layover affect the SAR image. In most building extraction methods, shadow and double bounce are used as two main parameters in building detection. In this paper, different morphological profiles for detecting shadow index and double-bounce index (DMPSIDI) method have been developed using its combination with the method based on statistical features for building extraction. The DMPSIDI method is a morphological-based method that extracts buildings from SAR images independent of changing their profile. The proposed method is also robust to different data using weighting in the main parameters of shadow and double bounce. [ABSTRACT FROM AUTHOR]

Nowadays, developing the radio frequency (RF)-photonic sensors suitable for several Defence and civil applications is emerging due to its various unique advantages. In today's scientific world, the involvement of low radar cross-section (RCS) airspace targets, such as unman aerial vehicles, drones, mini-helicopters, ornithopters, bionic birds, etc., is more for the purpose of airspace traffic management/guidance, materials delivery, legal/illegal surveillance and Defence/offense applications. The detection and recognition of such targets using the conventional RF sensors (which process only the main Doppler) is almost impossible. Therefore, extracting the distinctive micro-Doppler (m-D) signatures of these low-RCS targets and using them to image/differentiate/recognize their postures become significant which is the main contribution in this paper. A C-band continuous wave RF-photonic sensor is developed and different low-RCS targets: 2/3 blades rotating propeller system (drones), cone like structure (warhead) and a bionic-bird (spy-bird); are operated in front of it. The different nonlinear postures, of these targets, covered in our experimental measurements are m-D extraction of a slowly moving propeller system, detection of back-and-forth movements of a rotational propeller system, discerning the moving and static rotational propeller systems, simultaneous extraction of m-D signatures of a rotational and moving targets and recognition of flapping/gliding motions of bionic-bird. The analysis and recognition of all these postures using the experimentally generated m-D signatures are described. [ABSTRACT FROM AUTHOR]

High frequency radar has a wide monitoring range and low range resolution. It may contain multiple targets or outlier interference phenomena in different clutter regions of the range–Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Our previous research shows that the number of targets belongs to an absolute minority compared to the number of background units. In this paper, we propose a new method for multi-target detection building on the ordered statistics constant false alarm detector (OS-CFAR). The new method fully utilizes the sparse characteristics of the target and uses the idea of introducing regularization processing to eliminate interfering targets, and obtain an estimate of shape parameters for target detection. To further improve the performance of the algorithm, a correction method is proposed for the inaccurate selection of the k value. Upon estimating the distribution parameters, the detection threshold is calculated, and the target's constant false alarm detection is completed. Simulation and measured data show that our algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions, providing a reliable target detection method. [ABSTRACT FROM AUTHOR]

This article presents a deep neural network‐based constant false alarm rate (NNB‐CFAR) detector for simulated high‐frequency surface wave radar (HFSWR) data. A deep neural network is trained to identify fluctuation parameters of each cell of a range‐Doppler power spectrum based on the patterns present in the neighbouring cells. The estimated parameters are then used for calculating a detection threshold with a user‐specified probability of false alarm. To train the network, a realistic model of HFSWR echoes is used for generating a large labelled range‐Doppler image dataset, including many possible clutter scenarios and interfering target echoes. Several CFAR windows are extracted from the training range‐Doppler dataset and used as training data. The neural network is trained to replicate the output of a maximum likelihood estimator based on the reference cells of the CFAR window. The NNB‐CFAR algorithm was then compared to traditional CFAR algorithms by identifying targets in the second set of simulated range‐Doppler images. The probability of detection was also experimentally measured in the context of HFSWR for all algorithms. Results show that the technique can significantly improve detection rates amid strong clutter. [ABSTRACT FROM AUTHOR]

The echoes collected by wideband radar systems provide abundant information on target scatterers, which is beneficial to target detection, classification, and recognition. However, as the radar range resolution increases, range cell migration (RCM) during the coherent integration (CI) period happens much easier, which may cause a degradation of target detection probability. In addition, due to the target's orientation and structure relative to the radar, the distribution characteristics of the target scatterers in high-resolution range profiles (HRRPs) and the detection window length may vary from pulse to pulse, which may reduce the performance of traditional energy integration (EI) detectors. To solve those problems, moving range-spread target (RST) detection combining the modified keystone transform (MKT) and improved EI (IEI) is proposed in this paper. Firstly, based on waveform entropy minimization, MKT using hunter–prey optimization (HPO) is introduced to reduce the CI gain loss. The target Doppler ambiguity factor is estimated using such an effective optimization technique. Then, the IEI detector optimized by the adaptive threshold and detection window is utilized to achieve target detection, which minimizes the sensitivity of the traditional EI detector to the detection window length. The proposed method significantly improves the performance of moving RSTs in sea clutter without prior knowledge of the target Doppler ambiguity factor. Experiments are conducted by comparing the proposed method with other competing methods on both simulation data and real sea clutter data. The results demonstrate that the proposed method can obtain the CI more efficiently and has a higher detection probability. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

In this article, a novel heterogeneous fusion of convolutional neural networks that combined an RGB camera and an active mmWave radar sensor for the smart parking meter is proposed. In general, the parking fee collector on the street outdoor surroundings by traffic flows, shadows, and reflections makes it an exceedingly tough task to identify street parking regions. The proposed heterogeneous fusion convolutional neural networks combine an active radar sensor and image input with specific geometric area, allowing them to detect the parking region against different tough conditions such as rain, fog, dust, snow, glare, and traffic flow. They use convolutional neural networks to acquire output results along with the individual training and fusion of RGB camera and mmWave radar data. To achieve real-time performance, the proposed algorithm has been implemented on a GPU-accelerated embedded platform Jetson Nano with a heterogeneous hardware acceleration methodology. The experimental results exhibit that the accuracy of the heterogeneous fusion method can reach up to 99.33% on average. [ABSTRACT FROM AUTHOR]

Device-free indoor identification of people with high accuracy is the key to providing personalized services. Visual methods are the solution but they require a clear view and good lighting conditions. Additionally, the intrusive nature leads to privacy concerns. A robust identification and classification system using the mmWave radar and an improved density-based clustering algorithm along with LSTM are proposed in this paper. The system leverages mmWave radar technology to overcome challenges posed by varying environmental conditions on object detection and recognition. The point cloud data are processed using a refined density-based clustering algorithm to extract ground truth in a 3D space accurately. A bi-directional LSTM network is employed for individual user identification and intruder detection. The system achieved an overall identification accuracy of 93.9% and an intruder detection rate of 82.87% for groups of 10 individuals, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

With the dangerous and troublesome nature of hollow defects inside building structures, hollowness inspection has always been a challenge in the field of construction quality assessment. Several methods have been proposed for inspecting hollowness inside concrete structures. These methods have shown great advantages compared to manual inspection but still lack autonomy and have several limitations. In this paper, we propose a range-point migration-based non-contact hollowness inspection system with sensor fusion of ultra-wide-band radar and laser-based depth camera to extract both outer surface and inner hollowness information accurately and efficiently. The simulation result evaluates the performance of the system based on the original range-point migration algorithm, and our proposed one and the result of our system show great competitiveness. Several simulation experiments of structures that are very common in reality are carried out to draw more convincing conclusions about the system. At the same time, a set of laboratory-made concrete components were used as experimental objects for the robotic system. Although still accompanied by some problems, these experiments demonstrate the availability of an automated hollow-core detection system. [ABSTRACT FROM AUTHOR]

Traffic volume data collected by radar detector contain inherent types of errors that are hard to observe. Therefore, the identification of missing data and outliers based on reliable verification volume data is essential, and the development of a methodology for correcting detector traffic data while considering various specificities at the detector installation sites is required. This study proposed a deep-learning-based long short-term memory–multiple linear regression (LSTM–MLR) hybrid model. First, the corrected detector traffic volume was calculated via the MLR model after the preprocessing of the missing and outlier detector traffic volumes. Second, the corrected radar detector traffic volume was learned via the LSTM model to predict the detector traffic volume for the target time periods. The results confirmed a correction effect in terms of radar detector traffic volume data at most of the 30 target sites. The results of this study provide three contributions. First, the missing and outlier traffic volume correction algorithm is easy to apply and can be applied to traffic volume data collected from various detectors. Second, the MLR model developed in this study derived a causal relationship between the traffic volume of the detector and the complex factors that could not reveal the obvious cause, such as the undetected small car driving next to a large vehicle and the radio transmission/reception problem. Third, complex neural networks and dropout techniques to avoid overfitting do not necessarily improve prediction accuracy. [ABSTRACT FROM AUTHOR]