Unmanned Aircraft Systems Research Articles

Reinforcement Learning-Based Low-Altitude Path Planning for UAS Swarm in Diverse Threat Environments

Unmanned aircraft systems (UASs) with autonomous maneuvering decision capabilities are expected to play a key role in future unmanned systems applications. While reinforcement learning has proven successful in solving UAS path planning problems in simple urban environments, it remains under-researched for some complex mountain environments. In this paper, the path planning of UAS swarm for the low-altitude rapid traverse in diverse environments is studied when facing the threats of complex terrain, radars and swarm failure. First, a UAS swarm radar detection probability is built up for evaluating the radar detection threat by a networked radar system, where the detection probability of a UAS swarm is equated to a single UAS with appropriate position and radar cross section named as the swarm virtual leader. Second, a reinforcement learning based path planning method is proposed to seek the optimal path for the swarm virtual leader which balances instantaneous reward, including detection probability and path constraints with terminal reward, including normal rate. Third, a formation optimization strategy is designed to further reduce the threat of radar detection through dynamically adjusting the formation geometry. Final, simulations in the complex environment have been carried out to evaluate the performance of the proposed method, where the path quality, task success rate and normal rate are counted as the performance indicators.

Integrating RF MEMS Switch on Array Antenna to Achieve Reconfigurability in Wideband Application

Reconfigurable multi-band antennas are highly used for commercial as well as military purposes as a single antenna could be reconfigured dynamically to send and the receive multiple signals of different frequency simultaneously. In this work an antenna array is designed and integrated with RF MEMS shunt capacitive switch over it, for the applications of S,C,X-band in the frequency range of 2-12GHz. A membrane that can move freely over a coplanar waveguide serves as the MEMS switch. Here, a double meander tuned dual beam arrangement is used to increase switch isolation. Electrostatic mechanisms are used for actuators due to their low power requirements, compact design, and quick switching. By the simulation result, The beam starts to bend from 0.60793mm to 0.4116mm at the voltage 1.78492mV to 57.7382mV and it provides capacitance at 1.435pF to 1.597nF. It was noticed that the insertion loss (S21) and the return loss (S11) of a MEMS switch are around -0.079 dB and 20.618 dB correspondingly, in order to have a good switching characteristic in the up state. The switch has an insertion loss (S21) of -31.307 dB and return loss (S11) of about -0.256 dB in the up state thus exhibiting good switch characteristics. The RF MEMS shunt capacitive switch is integrated on the predesigned antenna array the dynamically shifting of the switch from on state to off state helps achieving the reconfigurability in the antenna array. Such antennas are applicable in spaceborne radar, communication satellites, unmanned aerial vehicles, unmanned aircraft system and various other communication as well as sensing applications.

Assessing Precision and Dependability of Reconstructed Three-Dimensional Modeling for Vehicles at Crash Scenes using Unmanned Aircraft System

This study focuses on the accuracy assessment of 3D reconstructions of crime scenes using Unmanned Aircraft Systems (UAS) and Terrestrial Laser Scanners (TLS) data for forensic crash investigation. Forensic crash investigation involves meticulously analyzing physical evidence, vehicles, and human factors in road collisions to determine the sequence of events. Preserving the original state of the crash scene before cleaning is essential for accurate forensic analysis. However, this preservation process can disrupt normal activities and demand considerable time. Geomatic technology, specifically UAS or drones, offers a potential solution for efficient and precise forensic mapping. The application of UAS technology enables swift data collection, leading to cost savings, enhanced safety, and data utilization. This study aims to assess the suitability of UAS techniques for forensic mapping, encompassing both relative and absolute accuracy. This research uses a UAS to rapidly and comprehensively capture evidence from a simulated crash site using predefined flight paths. The acquired image data is then processed utilizing Agisoft Metashape software, generating a detailed 3D model of the crash scene. This model can be enriched with annotations, measurements, and pertinent information. A comparative analysis is performed by preparing a table that contrasts the absolute and relative accuracy of UAS-collected data with that obtained from TLS, which serves as a benchmark. The results reveal that the UAS demonstrates a relative accuracy Root Mean Square Error (RMSE) of approximately ±4.1 cm compared to TLS. Concerning absolute precision, the UAS-produced RMSE values are determined as ±0.20719 for the X coordinate, ±0.164 for the Y coordinate, and ±0.001584 for the Z coordinate compared to GNSS data, which functions as the benchmark. The utilization of UAS technology offers a non-invasive measurement approach that eliminates direct physical contact between the operator and the documented object. This non-intrusive method ensures the preservation of the original scene characteristics and has shown its superiority over conventional approaches in managing crash scenes. Overall, this study underscores the potential of UAS technology in accurately reconstructing crime scenes for forensic investigation purposes.

Statistics and 3D Modelling on Soil Analysis by Using Unmanned Aircraft Systems and Laboratory Data for a Low-Cost Precision Agriculture Approach

The aim of this work was to elaborate a new methodology that can allow for the identification of the topsoil homogeneous area (tSHA) distribution along land parcels, supporting farmers in keeping low-cost, sustainable, and light logistic management of precision agriculture (PA) practices. This paper shows the assessment of tSHA variability over two production units (PUs), considering radiometric response (optical camera), physicochemical (texture, pH, electrical conductivity), and statistical and geostatistical data analysis. By using unmanned aircraft systems (UASs) and laboratory analysis, our results revealed that the integration between UAS-RGB and physicochemical data can improve the estimation accuracy of tSHA distribution. Firstly, the UAS-RGB dataset was used to isolate bare soil from the vegetative radiometric contribution. Secondly, starting from statistical approaches (correlation matrices), the highest correlation with UAS-RGB and physicochemical data was stated. Thirdly, by using a geostatistical approach (ordinary cokriging), the map representing the tSHA variability was finally obtained.

AAM/goods delivery: main enablers for BVLOS routine operations within environment at low and medium risk

PurposeTheadvanced air mobility (AAM) is defined by National Aeronautics and Space Administration (NASA) as safe, accessible, automated and affordable air transportation system for passengers and cargo, capable of serving previously hard-to-reach urban and rural sites. The purpose of this paper is to focus on explaining potential solutions, under study by the authors, which could support beyond visual line of sight (BVLOS) operations for goods delivery in a safe way.Design/methodology/approachAccording to recent NASA-commissioned market studies, by 2030, there will be as many as 500 million flights a year for package delivery services and 750 million flights a year for passengers’ transportation (AAM). A significant number of these aircrafts will be unmanned aerial vehicles, meaning that they are self-flying or autonomous, of which the smallest ones are quadcopters: they are relatively inexpensive and are capable to perform various tasks, such as aerial observation, crop monitoring and treatment, search and rescue, power line monitoring and goods delivery. On the other hand, there are still many difficulties in introducing them into medium- and low-risk BVLOS routine operations for goods delivery: unfortunately, there are no regulations and technologies yet that enable these operations.FindingsThis conceptual paper outlines the studies about possible solutions, identified by authors, which could support BVLOS operations in a medium- and low-risk environment; in particular, the following aspects have been analysed: regulations, integrating control systems for drones, sensors (on board obstacle detection and avoidance), emergency management (emergency on ground system to identify safe landing areas), concepts of droneway (or flight corridors) and drones recovery hub.Originality/valueThe purpose of this paper is to provide a conceptual description of the possible solutions, under study by the authors, which could contribute enabling the BVLOS operations in a medium- and low-risk environment. The paper aims describing the state of the art, terms of regulations, classifications and limitations and describing possible conceptual solutions that could guarantee safety in introducing unmanned aircraft system operations inside urban areas.

Cancer Care During the COVID-19 Pandemic: The African Narrative and Prospects.

The COVID-19 pandemic has had a significant impact on healthcare services globally. Whilst it has been particularly disruptive for cancer care in low-resource settings, a few African countries have been able to adapt strategies to enable continued delivery of medical care to persons with cancer. This study seeks to highlight how much effect the coronavirus pandemic has had on oncological care in Africa and to indicate the way forward. For this narrative review, PubMed and Google Scholar were used to search for literature addressing the effect of the coronavirus pandemic on the care of patients with cancer in Africa with ensuing coping strategies. Selection criteria were manuscripts published since the onset of the pandemic in 2019 and written in the English language with Africa being the focus. In total, 52 research papers involving up to 21 African nations were found and reviewed. Across the board, the COVID-19 pandemic resulted in the deferral of oncological screening programs and a halt in immunization activities routinely scheduled for preventable cancers. It caused a colossal shortage in the availability of appropriately trained medical personnel, reduced frequency and duration of outpatient consultations, and a delay in cancer investigations and diagnosis. It also stirred up the substandard modification of chemotherapy regimens and radiotherapy due to the scarcity of anticancer medications and radioisotopes and engendered the cancellation of cancer surgical procedures. Palliative care for patients with locally advanced and metastatic disease was in many cases interrupted and cancer research activities were abruptly deferred. Ultimately, these led to poor patient outcomes and increased cancer-related fatalities. However, a few African countries - Rwanda, Ghana, and Tunisia - have continued to adapt telemedicine, small unmanned aircraft systems (sUAS), and home therapy to facilitate cancer care. To date, there is a paucity of data concerning the successesand cost-effectiveness of these relatively new methods recently adapted to cater to the medical needs of cancer patients in Africa. The pandemic has presented the African communityan opportunity to advance her healthcare systems, especially as it pertains to the delivery of medical care to persons with cancer. The need of the hour is to study further the alternative cancer care delivery systems initiated during the pandemic in order to determine their sustainability in Africa at large.

Policy Learning in Nascent Industries’ Venue Shifting: A Study of the U.S. Small Unmanned Aircraft Systems (UAS) Industry

Industry groups engage in venue shifting when they seek to overturn or alter restrictive regulations imposed by one political venue through another. A critical step in this process is resolving uncertainties surrounding the preference of the targeted venue and the nature of the relevant policy proposal. While existing studies emphasize a long-term trial-and-error process of policy learning, we focus on nascent industries and argue that ventures seek other information sources to resolve these uncertainties quickly. In particular, nascent industry groups are likely to perceive that the targeted venue will support their policy proposal if the targeted venue is ideologically distant from the venue that has enacted the restrictive regulations, if the targeted venue has recently supported other nascent industry groups’ similar policy proposals, or if the industry groups themselves are more exposed to industry peers’ success in promoting the same policy proposal in other jurisdictions. Under these conditions, the industry groups invest more to influence the targeted venue in response to restrictive regulations enacted by other venues. We find support for our theory by examining how from 2013 to 2019 the small unmanned aircraft systems industry trade associations in the United States lobbied state governments to nullify local regulations.

Safety Assessment of an Unmanned Air Vehicle Flight Control System in accordance with SAE ARP 4761

Certification Technologies of Small Unmanned Aircraft System (CTsUAS) is an Unmanned Air Vehicle, which is on development based on KC-100. KC-100 is a four-seater civil aircraft developed by the Korea Aerospace Industries (KAI) with the type certification of the US Federal Aviation Administration (FAA). For the type certification of CTsUAS, system safety assessment and analysis have been conducted in accordance with SAE ARP 4761. This paper presents the partial results of safety assessment of the flight control system of CTsUAS, focusing on the fault detection and recovery functions. Through the lessons from the results, additional considerations needed to support safety assessment and safety compliance determination of an unmanned aircraft system have been derived.

All Electric Cooling System for Unmanned Aircrafts

This study brings out two All Electric Environmental Control System (AE-ECS) configurations for Unmanned Aircraft air-conditioning and performance evaluation of these two configurations interms of power consumption and fuel mass penalty. In conventional (or traditional) Bleed Air System, hot air is extracted from the engine compressor whereas in Bleed less Air Cycle system/Bleed less Vapour Cycle Systems (also called AE-ECS) bleed air extraction from engine is eliminated. Eliminating the extraction of bleed air from the engine in both the systems of Bleed less Air Cycle and Vapour Cycle System, saves the engine energy and thus increases the engine efficiency/thrust. In the present study performance of the Bleed less Air Cycle and Vapour Cycle systems are studied for typical flight envelop and heat loads of 5 kW. Bleed less Vapour Cycle System consumed less electric power compared to Bleed less Air Cycle System when compared for 5 kW cooling system requirements for an Unmanned Aircraft system. Fuel mass penalties are also calculated and compared for these two system configurations.

Retrieving Relevant EU Drone Legislation with Citation Analysis

Can the retrieval of relevant unmanned aircraft systems (UAS) legislation be automated? In this article, references from and to EU legislation are used to create an overview that is subsequently compared to legislation considered relevant by subject-matter experts. The overlap between the results of the citation analysis and the expert overview is promising. Additionally, an approach was proposed and tested where, first, a relatively large number of laws were identified and, second, the laws that were considered relevant were selected. The findings reveal that this approach was successful at retrieving the majority of relevant laws. The results are relevant to researchers, policymakers, practitioners, and laypeople searching for relevant EU legislation on UAS.

Індикатори змін рівня функціональних резервів організму операторів при розвитку вікових змін, втоми та асоційованих зі стресом захворювань

In the condition of war on the territory of our country, the intensity of the development of events that surround a person has changed significantly. In this regard, the need to apply simple and quick methods of assessing the functional state has increased. This especially applies to operators of unmanned aircraft systems, whose professional activity is associated with a large informational and physical load. The purpose of study was to study the possibilities of the stabilometry methods in assessing functional changes in the body of operators associated with the loss of their functional reserves. This affects the psychophysiological age, fatigue and deviations in operators of unmanned aircraft systems, which have a stress-associated diseases. The presence of unidirectionality of psychophysiological processes that occur with age changed, during the development of fatigue and diseases, caused by the influence of a stress factor is shown. A linear multiple regression model was built, which can be used to determine the level and rate of aging of operators, and a nonlinear multiple regression model that can estimate the degree of fatigue that develops during the year in the operator while performing official duties. With help of the method of discriminant analysis, special solving rules have been developed, according to which it is possible to determine whether the operator belongs to a group of healthy persons or those suffering from stress-associated diseases.

Towards Safe and Efficient Unmanned Aircraft System Operations: Literature Review of Digital Twins’ Applications and European Union Regulatory Compliance

Unmanned aerial system/unmanned aircraft system (UAS) operations have increased exponentially in recent years. With the creation of new air mobility concepts, industries use cutting-edge technology to create unmanned aerial vehicles (UAVs) for various applications. Due to the popularity and use of advanced technology in this relatively new and rapidly evolving context, a regulatory framework to ensure safe operations is essential. To reflect the several ongoing initiatives and new developments in the domain of European Union (EU) regulatory frameworks at various levels, the increasing needs, developments in, and potential uses of UAVs, particularly in the context of research and innovation, a systematic overview is carried out in this paper. We review the development of UAV regulation in the European Union. The issue of how to implement this new and evolving regulation in UAS operations is also tackled. The digital twin (DT)’s ability to design, build, and analyze procedures makes it one potential way to assist the certification process. DTs are time- and cost-efficient tools to assist the certification process, since they enable engineers to inspect, analyze, and integrate designs as well as express concerns immediately; however, it is fair to state that DT implementation in UASs for certification and regulation is not discussed in-depth in the literature. This paper underlines the significance of UAS DTs in the certification process to provide a solid foundation for future studies.

Aerial characterization of surface depressions in urban watersheds

Digital elevation models (DEM) are one of the most fundamental inputs for hydrological modeling. It has been a common practice to remove all surface depressions in a DEM as they are assumed to be data errors. The emerging technology of unmanned aircraft systems (UAS) provides an opportunity to re-examine this assumption at the hyperspatial resolution. This study was the first attempt to characterize small surface depressions in urban environments using UAS imagery. Using an urban area in south Texas as the study site, UAS flights were conducted to yield hybrid DEMs at the resolution of 8–14 cm, coupled with comprehensive ground truth collection. Surface depressions identified from the UAS DEMs were first corrected based on the vertical accuracy of DEMs and then validated through field surveys, with comparisons to two existing LiDAR DEMs (1-m and 10-m). The hydrological impacts of different DEM-derived estimates of catchment depression storage were examined using the Curve Number method across different design storms. Results show that the UAS DEMs outperformed the LiDAR DEMs in describing the microtopographic control of urban overland flow and associated hydrological connectivity across built and natural features. The 8-cm UAS DEM revealed 926% more depression storage than the 10-m LiDAR DEM. This demonstrates a compelling correlation between increasing DEM resolution and enhanced quantification of depression volume. Consequently, the increased depression storage reduced surface runoff by 41% under a two-year design storm and 13% under a 200-year design storm. The results suggest a strong relationship between the DEM resolution and the derived depression estimates, aligning with the fractal nature of watershed systems. Also, the results indicate that the centimeter-level UAS DEMs were not immune from problems. They could yield fake depressions caused by factors such as vegetation, temporary street objects, and underground sewer pipes. The findings of this study suggest the need to quantify the relationships between DEM resolution and associated hydrological attributes and develop new digital drainage analysis algorithms that could effectively incorporate UAS data into urban hydrological modeling.

Predicting Rice Lodging Risk from the Distribution of Available Nitrogen in Soil Using UAS Images in a Paddy Field.

Rice lodging causes a loss of yield and leads to lower-quality rice. In Japan, Koshihikari is the most popular rice variety, and it has been widely cultivated for many years despite its susceptibility to lodging. Reducing basal fertilizer is recommended when the available nitrogen in soil (SAN) exceeds the optimum level (80-200 mg N kg-1). However, many commercial farmers prefer to simultaneously apply one-shot basal fertilizer at transplant time. This study investigated the relationship between the rice lodging and SAN content by assessing their spatial distributions from unmanned aircraft system (UAS) images in a Koshihikari paddy field where one-shot basal fertilizer was applied. We analyzed the severity of lodging using the canopy height model and spatially clarified a heavily lodged area and a non-lodged area. For the SAN assessment, we selected green and red band pixel digital numbers from multispectral images and developed a SAN estimating equation by regression analysis. The estimated SAN values were rasterized and compiled into a 1 m mesh to create a soil fertility map. The heavily lodged area roughly coincided with the higher SAN area. A negative correlation was observed between the rice inclination angle and the estimated SAN, and rice lodging occurred even within the optimum SAN level. These results show that the amount of one-shot basal fertilizer applied to Koshihikari should be reduced when absorbable nitrogen (SAN + fertilizer nitrogen) exceeds 200 mg N kg-1.

Unmanned aircraft path planning for construction safety inspections

With its flexible and convenient features, the Unmanned Aircraft System (UAS) provides an efficient platform for safety inspections in hazardous areas. Yet sparse studies have explored path planning for construction safety inspections. This research developed a multi-precision unmanned aircraft path planning system (MP-UAS) by using MATLAB for safety inspections on partially known 3D exterior construction environments. Firstly, a detailed house-building checklist and monitoring levels are formulated. Subsequently, based on the principle of merging pixel points in four neighborhoods, the Four-neighborhood Growing algorithm extracts information from the point cloud grid model to divide inspection areas. Finally, the Ant Colony Optimization (ACO) combined with four-element optimization achieves a full coverage optimal design of UAS path in two-dimensional planes above the inspection area. The system greatly reduces the path length and minimizes energy consumption while meeting requirements of coverage accuracy in the validation case, and is expected to facilitate safety inspection and decision-making.

Plastic Contaminant Detection in Aerial Imagery of Cotton Fields Using Deep Learning

Plastic shopping bags are often discarded as litter and can be carried away from roadsides and become tangled on cotton plants in farm fields. This rubbish plastic can end up in the cotton at the gin if not removed before harvest. These bags may not only cause problems in the ginning process but might also become embedded in cotton fibers, reducing the quality and marketable value. Therefore, detecting, locating, and removing the bags before the cotton is harvested is required. Manually detecting and locating these bags in cotton fields is a tedious, time-consuming, and costly process. To solve this, this paper shows the application of YOLOv5 to detect white and brown colored plastic bags tangled at three different heights in cotton plants (bottom, middle, top) using Unmanned Aircraft Systems (UAS)-acquired Red, Green, Blue (RGB) images. It was found that an average white and brown bag could be detected at 92.35% and 77.87% accuracies and a mean average precision (mAP) of 87.68%. Similarly, the trained YOLOv5 model, on average, could detect 94.25% of the top, 49.58% of the middle, and only 5% of the bottom bags. It was also found that both the color of the bags (p < 0.001) and their height on cotton plants (p < 0.0001) had a significant effect on detection accuracy. The findings reported in this paper can help in the autonomous detection of plastic contaminants in cotton fields and potentially speed up the mitigation efforts, thereby reducing the amount of contaminants in cotton gins.

Reflectance Measurements from Aerial and Proximal Sensors Provide Similar Precision in Predicting the Rice Yield Response to Mid-Season N Applications.

Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDREUAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVIGS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDREUAS and NDVIGS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDREUAS and NDVIGS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDREUAS and NDVIGS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha-1, respectively. These results indicate that both aerial NDREUAS and proximal NDVIGS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency.

UAS-LC-GNSS: Precision Surveying with a Low-Cost GNSS System for Commercial Drones

INGV (National Institute of Geophysics and Volcanology, Italy) is one of the institutions that studies and monitors the geophysical phenomena (earthquakes, volcanic eruptions, landslides, etc.) that occur on Earth. During these events, it is essential to carry out a large, detailed and fast map of the affected areas.If we think of the difficulties encountered during the mapping of the fault sources for the 2016 earthquake in central Italy, we can understand how the UAS (Unmanned Aircraft System) can be a valid “low-cost” alternative to the traditional methods of surveys.These devices, thanks to precision instrumentation such as GNSS (Global Navigation Satellite Systems) receivers and IMU (Inertial Measurement Unit) control units, allow a detailed reconstruction of the investigated areas, especially for small-scale analysis applications. These aircraft are based on multiple technologies and show great investigative capabilities, therefore they must be considered as complete systems.Starting from these concepts, we have developed a low-cost RTK/PPK (Real Time Kinematic/Post Processing Kinematic) GNSS survey system on “commercial” UAVs (Unmanned Aerial Vehicles), i.e. professional drones that are not created to be modified.We have demonstrated how the integration of a GNSS RTK/PPK module on commercial UAVs makes the system efficient for the reconstruction of a highly detailed and precise DEM (Digital Elevation Model), without using GCP (Ground Control Point), allowing to make precision measurements in areas that are difficult to explore and investigate.

Towards A Standardized Aerial Platform: ICUAS’22 Firefighting Competition

This paper presents an Unmanned Aerial Vehicle (UAV) platform used for the competition held at the International Conference for Unmanned Aircraft Systems 2022 (ICUAS’22). The envisioned scenario in the competition is delivering an extinguishing ball to a fire with a UAV. Driven on that thought, the qualifying round of the competition is organized in a realistic simulation environment. The competitors are required to navigate through a cluttered environment into a free space, where they perform a search for the target and deliver the ball as close as possible to that target. The competition finals were organized as a part of the conference, with the same goal as in the simulation qualifiers. The UAVs used for the competition are prepared and provided by the organizers. A detailed description of the UAV software and hardware is outlined in the paper as a step towards standardizing aerial research platform.

UAS-Borne Radar for Autonomous Navigation and Surveillance Applications

The autonomous navigation of UAS requires, among others, detect-and-avoid capability as a prerequisite for enabling wide-ranging applications, including the transportation of goods and people. This article presents the design, implementation, and experimental results of a UAS-borne radar system detecting drones. The applications of the proposed system include not only detect-and-avoid systems for safe and autonomous navigation of unmanned aircraft systems but also airborne surveillance of malicious drones in controlled or restricted airspace for mitigating security and privacy threats. The system performance in terms of maximum detection range is evaluated through field tests. The experimental results show that the proposed UAS-borne radar can detect a DJI Phantom 4 and a DJI Matrice 600 Pro at a maximum distance of 440 and 500 meters, respectively. The article also provides insights into the system implementation and integration aspects, discusses future research direction, and stresses the need for standardization efforts to benchmark the required performance levels for UAS-borne radars.