Search

Due to the increase in air traffic, additional systems with high reliability and highperformance must be adopted in order to keep the safety requirements. On the other hand, they must be compliant with the specifications in terms of light-weight, small size, and low power-consumption for the small aerial vehicles. The paper presents an advanced integrated solution for increasing the performance of the traditional Air Data System adopting an Inertial Measurement Unit based on Micro Electro-Mechanical System sensors, i.e., accelerometer and gyroscope. The aim is to identify the Air Data System failure, thus overcoming the issues of adopting additional technology that increase the size, weight, and power consumption of the systems and make. This way, the considered solution is tailored for small manned and unmanned vehicles applications. To this aim, the authors propose an

Distributed Synthetic Aperture Radar (DSAR) is defined as a SAR in which the signal emitted by the transmitter and scattered from the area of interest is not collected by a single receiver but by many, conveniently distributed, formation flying, receivers. The concept of distributed aperture can enable new SAR working modes, but more important, thanks to passive operations, can achieve very high performance through a series of very compact, low weight, agile, satellite platforms. Such a distributed space system can be regarded as a system in which the payload functionality is broken apart and distributed among the different elements of the system. While fractionation and formation flying may lead to many advantages, distributed space systems pose a number of technological and operational issues at system and subsystem level. This paper focuses on basic system requirements and formation flying aspects relevant to DSAR. In particular, trajectory design approaches that can fulfil payload requirements while relaxing formation control issues are addressed. Long term stability is also investigated.

SAR4BAT project (SAR data fusion for bathymetric data retrieval of sea bottom in coastal areas) is funded by Italian Space Agency and it is focused on development of SAR-based bathymetric products for coastal area by processing SAR data from ALOS and COSMO-SkyMed satellites. Since Synthetic Aperture Radar (SAR) signals are unable to penetrate sea surface and reach seabed, indirect processes with sea floor morphology sensed through its effects on sea surface must be used. Promising techniques for SAR-based bathymetry have been studied in the last years but they still need additional research to be effectively used. Improvement of these techniques could provide up-to-date bathymetric maps for coastal regions with appropriate resolution to monitor geological processes. The performed research has focused on the two different indirect processes which have been identified up to the present to relate the sea surface features visible on SAR images to seabed morphology and water depth: the indirect process based on swell waves and the one based on sea surface currents. Both the methods have been deeply studied and proper mathematical expressions have been identified to retrieve bathymetric data from SAR images. Implementation of the developed algorithms has been performed in the Gulf of Naples, Italy, by using SAR images from ALOS and COSMO-SkyMed. Bathymetric data have been retrieved by using both the two indirect processes. Comparison with the values reported on the Nautical Chart provided by the Italian Navy Hydrographic Institute have been performed.

This paper aims at investigating the performance of a LIDAR-based system for pose determination of a known debris. A customized template matching tech-nique is implemented for pose initial acquisition, while pose tracking is carried out by Iterative Closest Point algorithms based on different matching approach-es. In order to evaluate the achievable accuracy in pose estimation, a numerical simulator is developed implementing realistic debris geometry, target/removal-system relative dynamics, and sensor operation. Results relevant to a large de-bris in Low Earth Orbit show that even relatively sparse point clouds allow the pose to be computed with sub-degree accuracy in attitude and sub-centimeter accuracy in the relative position.

This paper presents the most recent activities at the Laboratory of Guidance, Navigation, and Control of the Department of Industrial Engineering dealing with design, development, and test of attitude sensors for space applications, in particular a micro sun sensor and a star tracker, along with laboratory facilities to test them indoors. The paper presents a detailed description of sensors as well as test facilities, and the results of two test campaigns that assessed the performance of the two devices.

Formation Flying Synthetic Aperture Radar (FF-SAR) is defined as a SAR in which the signal emitted by the transmitter and scattered from the area of interest is not collected by a single receiver but by many, conveniently distributed, formation flying, receivers. The concept of distributed aperture can enable new SAR working modes, but more important, thanks to passive operations, can achieve very high performance through a series of very compact, low weight, agile, satellite platforms. Such a distributed space system can be regarded as a system in which the payload functionality is broken apart and distributed among the different elements of the system. While fractionation and formation flying may lead to many advantages, distributed space systems pose a number of technological and operational issues at system and subsystem level. Signal modeling, radar processing, system operations and formation flying aspects are analysed in this paper and an end-to-end space system demonstrator concept is also proposed including 3 satellites working in X-band, flying in a LEO close formation. Mission operations and system budgets are performed at a preliminary level showing the possibility to achieve mission objective by platforms of micro-satellite class (

This paper presents a mission concept for an end-to-end space demonstration of Formation Flying Synthetic Aperture Radar (FF-SAR) system. This is realized by means of 3 satellites working in X-band, flying in Low Earth Orbit (LEO) formation. One satellite embarks a Transmitting-Receiving (Tx/Rx) radar, i.e. it is a monostatic SAR. The other two satellites are Receiving-only platforms. From the FF-SAR perspective, starting from the design of a general purpose monostatic SAR, targeted to achieve 8m x 8m resolution on ground from a 550-km orbit, FF-SAR properties are exploited to demonstrate performance improvement, namely the achievement of 4m x 4m resolution with the same Noise Equivalent Sigma Zero (NESZ) requirement, testing suitable combinations of FF-SAR techniques like Signal-to-Noise Ratio (SNR) enhancement, Pulse Repetition Frequency (PRF) reduction, Coherence Resolution Enhancement (CRE). Moreover, at the nominal 8m x 8m resolution, 3D imaging and Ground Moving Target Indication (GMTI) techniques can be also tested. The paper focuses on space system design aspects related to the end-to-end demonstration mission, aiming at showing the feasibility of a FF-SAR with micro-satellite class platforms (~100 kg) .

This paper deals with the performance analysis and preliminary design of an integrated system for autonomous attitude state determination of spacecrafts based on the fusion of MEMS (Micro-Electro-Mechanical-System) gyros with an advanced CMOS-based star sensor. In the integration scheme the star sensor is used as aiding sensor. Sensor data fusion is performed by means of Kalman filtering, following a detailed analysis of star trackers and gyros measurement errors. As a consequence, the integrated system performances are estimated by covariance propagation. In particular, the steady state error covariance for different inertial and stellar sensor typologies and aiding sensor update intervals is numerically analysed, in order to evaluate different sensor configurations considering the performances of sensors available in the market. Analysis results will help in selecting the star sensor data rate and the updating frequency to be used for the inertial sensor aiding. The study is performed considering the following sensor physical parameters: gyros drift rate, resolution and bias stability; star sensor sensitivity, detection limit, field-of-view and instantaneous field of view; star catalogue size and coverage. The above parameters are modelled with reference to state-of-art MEMS gyros and MOS and CMOS-based star sensors. Results show that stellar-inertial sensors for coarse/medium (0.01°) and high (

This paper analyses some aspects dealing with indoor testing of modern star trackers for attitude determination. An end-to-end approach has been considered, which requires that realistic star field scenes are simulated and supplied to the sensor under test. First of all, the features of the scene that is observed during a space mission are presented. Characterising astronomic parameters are converted into radiometric quantities to be directly exploited for simulation. Then, the need of collimating optics to reproduce the huge distance of the true scene is demonstrated by quantitative analysis of typical sensor performance. Relevant design is also presented. Finally, the relationship between the accuracy of sensor installation in the test facility and the consequent attitude measurement uncertainty is evaluated.

This paper aims at characterizing the attitude determination performance of a non-conventional star tracker based on a hyper-hemispheric panoramic camera designed as a multi-functional sensor for applications onboard small/micro-satellites. The peculiar features of the optical system allow imaging a field of view of 360° in azimuth and up to 135° in elevation (zenith), thus being able to observe an extremely wide portion of the celestial sphere, though at the expense of limited capabilities in terms of detector resolution and sensitivity. For this reason, each mode of the multi-functional sensor requires ad-hoc, original algorithmic solutions. In this respect, the star tracker mode relies on an innovative approach for star identification, based on template matching and image registration concepts inherited from the computer vision and robotic research community, combined with a standard solution to the Wahba's problem to determine the spacecraft attitude parameters during the lost-in-space condition. A numerical simulation environment is developed to realistically reproduce the star pattern imaged by the sensor including all the major sources of noise (e.g., outliers, hot pixels). Hence, the performance of the proposed approach is evaluated in terms of attitude determination accuracy and reliability over a wide set of attitude states characterized by a uniform distribution of the camera pointing in the celestial sphere. Also, this is done considering highly-variable settings in terms of sensor's specifications and algorithm's operational parameters.

Echinococcosis hydatidosis is a chronic disabling disease of parasitic origin that constitutes an exemplary case of one-health. According to data from the national epidemiological bulletin, is one of the most important zoonoses in terms of the number of cases, severity, and economic costs in Italy. This paper aims to contribute to the prevention of the spread of this infection by proposing the development of an embedded system on a drone that can release decoys containing canine medicine. To this aim has been chosen a microcontroller, which is responsible for interfacing and controlling the different peripherals required to accomplish the release of decoys from the custom payload. In addition to the hardware part of the embedded system, has been implemented an ad hoc program that is able to coordinate all the elements, satisfying the proposed purpose. In particular, suitable software architecture has been implemented by means of an ARM-Cortex microcontroller not only for the advantages related to size, cost, and power consumption but also to exploit their high-performance and safety requirements.

The growing interest in Unmanned Aerial Systems has led, especially in recent years, to the need for a precise structure of rules and procedures aimed to guarantee safe traffic management. As an important type of dynamic data-driven application system, Unmanned Aerial Systems are widely used for civilian, commercial, and military applications across the globe and require a high level of autonomy. Indeed, the absence of the pilot onboard leads to stricter requirements regarding the level of accuracy for the Unmanned Systems, with the need to provide autonomous services such as Conflict Detection and Resolution during the strategic and tactical phases, avoiding possible collisions. In order to explore efficient and robust solutions for autonomous Collision Avoidance, an increasing research effort has been devoted to Trajectory Prediction for non-cooperative UASs. In this work, an analysis of existent solutions for Trajectory Prediction has been evaluated, with particular emphasis on methods based on Machine Learning (ML). Besides, four different Machine Learning methods based on Regression (Linear Regression, Regression Tree, Gaussian Process Regression, and Support Vector Machine for Regression) have been implemented for the prediction of the flight times of a single drone over planned paths. The four regression methods are used as benchmark for a proposed Deep Learning based approach, and the results have been compared.

This paper describes an adaptive method to predict the battery discharge of a multirotor drone over a generic path. A proper assessment of battery state of discharge trend is critical to ensure a safe operation of battery-powered aerial vehicles in critical environments, such as the urban ones. Several standard paths were executed by a commercial, battery-powered drone to acquire the data needed to train a Deep Learning based method; telemetry files and ground-collected data were processed to train the proposed method according to a trajectory segmentation strategy. Two learning configurations were trained to predict the time-of-flight and the integral of the battery current needed to fly the standard path segments. The current integral for each standard path segment is exploited to estimate the corresponding reduction of the battery state of charge. Based on path segmentation into predefined standard sections, the presented solution allows to predict time of flight and battery consumption along any generic path. This can be exploited to plan a complete path at strategic stage, as well as to estimate the remaining available power resource at any intermediate point along a generic trajectory both at strategic stage and during mission execution to be exploited by the drone operator or by a traffic management service. To validate the technique, a scaled package delivery mission is presented as an example of contingency management application. The maximum distance that the drone can reach from selected points along the mission path was computed according to the remaining battery level. Considering a random distribution of safe-landing areas around the planned path, the computed maximum distance was used to determine which safe-landing areas can be reached by the drone from a generic point of the path after an unexpected event.

The paper describes the development of a drone swarm configuration that can electromagnetically obscure a target-drone from radar detection. At the beginning, different drone swarm architectures were analyzed in order to select the best solution to develop ground and in-flight tests. During the tests execution, a compact software radar was used to detect a target-drone. Moreover, trihedral corner reflectors were used to perform the electromagnetic obscuration of the target-drone. Several ground tests were executed to characterize the radar echo response of the target-drone installed on a wooden tripod. Then, the radar echo response of the target-drone was compared with the one obtained by the target-drone obscured by corner reflectors. Different configurations of corner reflectors were tested on wooden tripods to select the swarm configuration that better obscures the target-drone. At the end, flight tests were executed involving decoy-drones equipped with a corner reflector and a target-drone to assess and validate the developed drone swarm as countermeasure against ground-based radar detection.

It is well known that the initial field of use of Unmanned Aircraft Systems (UAS, commonly known as drones) was the military one, but nowadays they are used in a huge number of civilian tasks such as agriculture, cinematography, and on demand transport of goods and services and many other applications are in development. Particularly, the healthcare area is a prime concern, and during the Covid-19 pandemic the considerable potential of drones was highlighted both in terms of contrasting and overcoming the emergency. Indeed, their applications included monitoring for prevention, sanitization, and transfer (deliveries / collections) of material without approaching between operators and users of the service. This paper presents the service for equipment delivery by drone remotely operated, performed using drones for the delivery or picking up of small and medium-sized objects to/from the home of people subjected to quarantine. The users of these services can include all those who are affected by pathologies similar to the pandemic disease Covid-19 and who are forced into quarantine situations, finding themselves in an asymptomatic or symptomatic state.

This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is predicted employing a properly trained neural network. The final flight-time over the complete path is computed summing the partial results related to the standard sub-paths. Real drone flight-tests were performed in order to realize an adequate database needed to train the adopted neural network as a classifier, employing the Bayesian regularization backpropagation algorithm as training function. For the network, the relative angle between two sides of a corner and the wind condition are the inputs, while the flight-time over the corner is the output parameter. Then, generic paths were designed and performed to test the method. The total flight-time as resulting from the drone telemetry was compared with the flight-time predicted by the developed method based on machine learning techniques. At the end of the paper, the proposed method was demonstrated as effective in predicting possible collisions among drones flying intersecting paths, as a possible application to support the development of unmanned traffic management procedures.

This paper describes a configuration of drone swarm that can be used in support of the actions to limit the virus spread during a pandemic period, such as the COVID-19 emergency. The proposed study analyzes a system architecture for the identification of individuals affected by the virus, estimating their biomedical parameters. The presented method exploits different techniques, such as stereoscopy vision, thermal measures and remote photoplethysmography, to acquire standalone data that can be compared to evaluate the target risk. The tested solutions are proposed to measure the social distancing among multiple individuals, the skin temperature of a target person, and the image photoplethysmography signal, that are critical parameters to detect a potentially infect individual during the COVID-19 pandemic. Different test strategies were adopted to carry out the mentioned tasks. To measure the distance between target individuals, two drones equipped with visible band cameras were employed. To measure the skin temperature of a target, a drone equipped with a thermal camera was adopted, performing measures at different distances and heights. To obtain the image photoplethysmography signal, a video file from drone camera is processed. Image processing techniques are required to elaborate the data coming from several images and videos acquired by drones. Comparing the measures, altered biomedical parameters of several targets can be detected and later tested with medical equipment.

This paper describes the study conducted to predict the trajectory flight-time of a drone adopting a machine learning approach. The proposed method has been carried out developing a feedforward neural network to estimate the flight-time needed by the drone to perform a selected corner of a designed path. To acquire a consistent database for the neural network training several reference corner paths have been flown by a test drone. The reference corners have fixed side length and different turning angle. Neural network input parameters are the corner angle, relative orientation and intensity of wind. From the telemetry analysis the flight-time to fly the corner path has been computed and employed to train the neural network. The Levenberg-Marquardt algorithm and the Bayesian Regularization backpropagation algorithm have been exploited as training functions, analyzing several neural network architectures with a different number of hidden layers and neurons. At the end, the neural networks that are characterized by the best training and test performance have been selected for each training function. Stating the trained network, a generic path has been planned to test the proposed method. The error between the estimated flight-time and the real flight-time from the drone telemetry has been evaluated.

This paper reports on a model developed for evaluating major system performance of a spaceborne bistatic synthetic aperture radar (SAR) for remote sensing applications. The procedure accounts for formation flying aspects. It is particularly aimed at comparison of monostatic and bistatic cases, and, as a test case, it is applied to study a novel configuration, based on a small satellite equipped with a receiving-only antenna orbiting in tandem with a large, noncooperative transmitting spacecraft, the Italian COSMO-SkyMed mission. Numerical results and plots show the effectiveness of the procedure as a mission design tool and put in evidence key issues and characteristics of the proposed spaceborne bistatic formation.

Long gravity waves or swell dominating the sea surface is known to be very useful to estimate seabed morphology in coastal areas. The paper reviews the main phenomena related to swell waves propagation that allow seabed morphology to be sensed. The linear dispersion is analysed and an error budget model is developed to assess the achievable depth accuracy when Synthetic Aperture Radar (SAR) data are used. The relevant issues and potentials of swell-based bathymetry by SAR are identified and discussed. This technique is of particular interest for characteristic regions of the Mediterranean Sea, such as in gulfs and relatively close areas, where traditional SAR-based bathymetric techniques, relying on strong tidal currents, are of limited practical utility.

The present paper reports on some insights in the use of Synthetic Aperture Radars (SAR) for bathymetric data retrieval by exploiting variation in swell wave parameters approaching the shoreline. Since SAR signals are unable to penetrate sea surface and to reach seabed, echoed signals from sea surface are used to investigate underwater bathymetry in coastal area. A suitable data processing methodology is proposed to properly detect swell shoaling and refraction phenomena, and to accurately measure swell parameters. The described methodology is tested by using ALOS L-band SAR images over the Gulf of Naples, Italy. A Digital Elevation Model (DEM) of the investigated area is thus obtained. Results are consistent with the values reported in the Official Nautical Chart provided by the Italian Navy Hydrographic Institute.

Surface current variations generated by underwater bottom topography produce intensity modulations in SAR images. The direct inversion from image intensity to bathymetry is difficult, in general, because of the theoretical complexity of the involved physical mechanisms and the practical consideration that most of the required parameters cannot be derived from SAR data. The conventional approach for SAR bathymetry relies on the forward mechanism, i.e. the simulation of SAR images from (partially) known bottom topography and, then, the adjustment of the bathymetry through iterative comparison between simulated and collected images. The present paper deals with the development of a bathymetric SAR algorithm able to perform the direct inversion limiting the need for a-priori information or in-situ measurements, and for human intervention in the processing chain. The proposed approach is tested on COSMO-SkyMed data collected over coastal regions in the Gulf of Naples, showing that dense coverage and metric accuracy can be achieved even when the current is not enough strong to dominate SAR response.

A simplified algorithm for SAR-based bathymetry is presented able to measure surface current variations generated by the bottom topography from SAR intensity images and to reduce the need for both a-priori information and human-inthe- loop operations. The algorithm is first analyzed from a theoretical point of view and an error budget model is developed to estimate the achievable depth accuracy as a function of the uncertainty in the input parameters. Preliminary experimental results are also presented in which the algorithm is applied to both COSMO-SkyMed and ALOS images of the Gulf of Naples. The results show that the technique has the potential to generate depth measures that are significantly denser than those commonly reported in the nautical charts.

This paper describes scientific rationale and technical approach for a spaceborne bistatic synthetic aperture radar (SAR) mission (BISSAT). The studied configuration is based on a small satellite, equipped with a receiving-only microwave system that catches the echoes of an already existing, main orbiting SAR, without requiring its design modification or additional operating complexities. This experiment has been proposed to the Italian Space Agency as candidate for the third Italian Small Scientific and Technological Mission and preliminary founded for a competitive phase A study. BISSAT experiment offer unique features: (a) to conduct original scientific experiments and to exploit novel applications; (b) to perform a technology demonstration of an original space formation; (c) to keep cost within a small mission budget. Bistatic scattering has proven to be of fundamental importance to many branches of earth sciences and several investigations have been conducted, but considering one or both earth-based antennas and targets of limited dimensions because bistatic observation requires accurate time synchronization and antenna pointing. As an example, bistatic echoes allows innovative characterization of vegetated and urban surfaces for biomass evaluation, or sea surface waves and current to be mapped. Proposed bistatic experiment will allow original scientific activities to be carried out for the first time from space, such as: (i) evaluation of bistatic radar cross section of natural and man-made targets, by means of multi-angle bistatic SAR observations; (ii) acquisition of terrain elevation and slope by means of range and bistatic scattering measurements; (iii) acquisition of velocity measurements thanks to the simultaneous measurement of two Doppler frequencies; (iv) stereogrammetric applications thanks to the large antenna separation involved; (v) improvement of image classification and pattern recognition procedures; (vi) high-resolution measurements of components of sea wave spectra; (vii) signal processing of bistatic data; (viii) across-track and along-track interferometry. Technical feasibility of the mission has been demonstrated, in particular taking advantage from Italian COSMO/SkyMed X-band SAR2000 experience, which has been selected as main mission. Development of bistatic receiver chain can be carried out with wide reuse of components and instruments already qualified within COSMO project. Furthermore, BISSAT will add value and exploit further scientific applications of COSMO mission. Ground segment and bistatic SAR processing require novel, although limited, implementations, again founded on well-assessed expertise. Regarding the platform, BISSAT mission will allow novel experience and potentialities to be fulfilled, such as: propulsion for formation flying and orbit maintenance, and fine attitude and pointing control. It is worth noting that formation flying is an internationally recognized important scenario.

This paper describes an innovative method to measure the heading angle of a platform by exploiting the Sun light polarization state. The single-scattering Rayleigh model is applied to identify the Sun meridian/anti-meridian in a polarized image after the knowledge of the Sun light polarization state in the considered sky region. A polarimetric camera is adopted to acquire several images that include the Sun meridian/anti-meridian. Based on the knowledge of the Sun azimuth and elevation at given place and time, it is possible to compare the position of the Sun meridian/anti-meridian in the local reference frame with its position in the camera reference frame. From this comparison, the heading angle can be accurately estimated and used to correct the typical drift errors of a MEMS-based, low-cost inertial measurement unit. The initial heading angle is estimated with a Kalman filter. Then, in order to observe the performance reached a comparison between the compensated and uncompensated system is evaluated. The adoption of a compact and lightweight polarimetric camera can improve the navigation performance of autonomous systems, such as Drones and Remotely Operated Vehicles.