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Abstract Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced.

Satellite-borne synthetic aperture radar (SAR) technology has revolutionized remote sensing applications by providing high-resolution and all-weather imaging capabilities. With the increasing availability of SAR data, the need for efficient data processing has become crucial. Onboard processing has emerged as a promising solution to address the challenges associated with limited downlink capacity and final data products latency. Performing data processing and compression directly on the airborne platform reduces the raw data transmitted to ground stations, which offers several key benefits. First, it significantly reduces the data volume to be downlinked, optimizing the usage of limited bandwidth and minimizing transmission delays. Second, onboard processing enables faster access to processed data products, allowing for quicker decision-making and timely response to dynamic events, enhancing the real-time capabilities of the system that are particularly valuable in time-critical applications. This article discusses various onboard processing techniques employed in SAR systems and explores their challenges, like computational constraints, architectural impacts, power consumption, and algorithm optimization. Furthermore, it examines the potential future developments in onboard processing, such as the integration of artificial intelligence and machine learning techniques to enhance data analysis and decision-making capabilities. The advancements in onboard processing have the potential to revolutionize the way SAR missions are conducted. By leveraging these techniques, SAR systems can achieve improved operational efficiency, reduced data latency, and enhanced real-time capabilities. This article emphasizes the significance of onboard processing in meeting the growing demands of SAR applications and underscores its role in advancing remote sensing capabilities for various sectors, including environmental monitoring, disaster response, and surveillance.

The problem of not having persistent river levels monitoring via satellite remote sensing is of considerable importance because the lack of this capability could affect the safety of inhabited areas near rivers. It has been shown that synthetic aperture radar (SAR) interferometry is a valid tool to measure the water surface of the river. Unfortunately, rivers can swell dynamically in less time than between two observations with the same geometric SAR parameters. Therefore, interferometric time series could not be used for effective emergency prevention and continuous monitoring because of geometric constraints. In this article, we propose an improvement of previous research by considering arbitrary geometric SAR acquisitions. The proposed method allows us to increase the possibility of image acquisition in order to drastically reduce the revisit time with respect to an interferometric acquisition strategy. Experimental results are consistent with the water level measurements from the in situ stations. Finally, the proposed procedure can potentially use data from any satellite system positioned in any orbit, not only sun-synchronous, in order to reduce time for more continuous monitoring of river levels.

Abstract Coregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross‐correlations in turn computed from each couple of cross‐correlations (a.k.a. cross‐cross‐correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne‐measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart.

One of the greatest limitations of Synthetic Aperture Radar imagery is the capability to obtain an arbitrarily high spatial resolution. Indeed, despite optical sensors, this capability is not just limited by the sensor technology. Instead, improving the SAR spatial resolution requires large transmitted bandwidth and relatively long synthetic apertures that for regulatory and practical reasons are impossible to be met. This issue gets particularly relevant when dealing with Stripmap mode acquisitions and with low carrier frequency sensors (where relatively large bandwidth signals are more difficult to be transmitted). To overcome this limitation, in this paper a deep learning based framework is proposed to enhance the spatial resolution of low-resolution SAR images while retaining the complex image accuracy. Results on simulated and real SAR data demonstrate the effectiveness of the proposed framework.

Abstract Recent years have seen a steady convergence of Radar and Communications band Radio Frequency (RF) transceiver systems. Not only have Communications systems colonised large swathes of previously allocated Radar bands but there is also a convergence of technologies driven by the relatively low cost of software‐defined transceivers and solid‐sate RF sources. Thus, where conventional radar transmissions are characterised by short narrowband pulses with high peak power, new classes of ‘pulse‐compression’ radar are being developed to exploit this new technology. The resulting Low Probability of Intercept waveforms are designed to spread energy in both time and frequency, yielding a very low instantaneous power spectral density. Methods to detect, analyse and distinguish such sources require longer acquisition periods to collect more energy from the sources. Here, a novel solution is provided for detection and separation based on direction‐finding utilising polynomial matrix methods in conjunction with sparse array geometries. This approach provides enhanced detection, separation and direction finding while using relatively few antenna elements.

Nowadays, a worldwide database containing the historical and reliable data concerning the water surface speed of rivers is not available and would be highly desirable. In order to meet this requirement, the present work is aimed at the design of an estimation procedure for water flow velocity by means of synthetic aperture radar (SAR) data. The main technical aspect of the proposed procedure is that an along-track geometry is synthesized using a single antenna and a single image. This is achieved by exploiting a multichromatic analysis in the Doppler domain. The application of this approach allows us to obtain along-track interferometry equivalent virtual baselines much lower than the equivalent baseline corresponding to the decorrelation time of raw data preserving data coherence. The performance analysis, conducted on live airborne full-polarimetric SAR data, highlights the effectiveness of the proposed approach in providing reliable river surface velocity estimates without the need of multiple passes on the observed scene.

In this article, we propose a new procedure to monitor critical infrastructures. The proposed approach is applied to COSMO-SkyMed data, with the aim to monitor the destabilization of the Mosul dam. Such a dam represents the largest hydraulic facility of Iraq and is located on the Tigris river. The destructive potential of the wave that would be generated, in the event of the dam destruction, could have serious consequences. If the concern for human lives comes first, the concern for cultural heritage protection is not negligible, since several archaeological sites are located around the Mosul dam. The proposed procedure is an in-depth modal assessment based on the micromotion estimation, through a Doppler subapertures tracking and a multichromatic analysis. The method is based initially on the persistent scatterers interferometry that is also discussed for completeness and validation. The modal analysis has detected the presence of several areas of resonance that could mean the presence of cracks, and the results have shown that the dam is still in a strong destabilization. Moreover, the dam appears to be divided into two parts: the northern part is accelerating rapidly while the southern part is decelerating and a main crack in this north-south junction is found. The estimated velocities through the PS-InSAR technique show a good agreement with the GNSS in situ measurements, resulting in a very high correlation coefficient and showing how the proposed procedure works efficiently.

Communication over doubly selective channels (both time and frequency selective) suffers from significant intercarrier interference (ICI). This problem is severe in underwater acoustic communications. In this paper, a novel partial fractional (PFrFT)-MIMO-OFDM system is proposed and implemented to further mitigate ICI. A new iterative band minimum mean square error (BMMSE) weight combining based on LDLH factorization is used in a scenario of perfect knowledge of channel information. The proposed method is extended from SISO-OFDM configuration to MIMO-OFDM. Simulation results demonstrate that the proposed PFrFT-LDLH outperforms the other methods in the SISO-OFDM scenario and that its performance can be improved in MIMO-OFDM scenarios.

Modern radar applications, such as automotive, integration of radar and communications, have become an important requirement. Systems able to provide joint radar and communications would exploit the same hardware while providing enhanced capabilities to the carrying platform, such as road situation awareness and content sharing. In this article, a novel framework for automotive radars based on the fractional Fourier transform (FrFT) is introduced. The aim of the proposed approach is to preserve radar performance while integrating a communication link. Simulation results demonstrate the potential of the proposed framework for automotive applications.

Direction of arrival estimation is a crucial aspect of many active and passive systems, including radar and electronic warfare applications. Spread spectrum modulation schemes are becoming ever more common in both Radar and Communications systems; because such modulation spreads the signal energy in frequency and time, such sources prompt the need for new approaches for detection and location. Broadband antennas and their subsequent signal processing systems are expensive in terms of both cost and power consumption. This forces a limitation on the number of elements in a feasible real-world array. In this paper, a novel co-prime broadband MUSIC-based direction of arrival algorithm is presented. The main feature of the new method is that it aims to reduce the number of antenna elements for a given aperture by utilising a co-prime sensing scheme applied to the problem of broadband direction finding via the polynomial MUSIC algorithm. Comparative results using simulated data show that the proposed co-prime polynomial MUSIC has comparable performance to those obtained using a uniform linear array method with an equivalent physical aperture.

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.

Europe has become the first continent of attraction for migrants and is also the first continent as for incidence of the population emigrated to the total of population. In Italy, in particular, the foreign population has grown from 2000 to 2013 at an average rate of 10% per annum. This scenario, plus the emergence of immigrant landings on the Italian coast as a point of arrival but also as a point of departure to other European countries, leads to awareness of the need for answers, not only at the Italian institutional level but also within a European convergence framework, in the search of policies and programs most appropriate to a new social context, strongly characterized by the presence of the immigrant population

Large infrastructures need continuous maintenance because of materials degradation due to atmospheric agents and their persistent use. This problem makes it imperative to carry out persistent monitoring of infrastructure health conditions in order to guarantee maximum safety at all times. The main issue of early warning infrastructure fault detection is that expensive in-situ distributed monitoring sensor networks have to be installed. On the contrary, the use of satellite data has made it possible to use immediate and low-cost techniques in recent years. In this regard, the potential of spaceborne Synthetic Aperture Radar for the monitoring of critical infrastructures is demonstrated in geographically extended areas, even in the presence of clouds, and in really tough weather. A complete procedure for damage early-warning detection is designed, by using micro-motion (m-m) estimation of critical sites, based on modal proprieties analysis. Particularly, m-m is processed to extract modal features such as natural frequencies and mode shapes generated by vibrations of large infrastructures. Several study cases are here considered and the “Morandi” Bridge (Polcevera Viaduct) in Genoa (Italy) is analyzed in depth highlighting abnormal vibration modes during the period before the bridge collapsed.

Within certain cultural and social limits, some behaviours linked to the use of new technology, gaming, physical exercise or work are useful practices with a positive social value for both individuals and society. As these practices are commonly and socially accepted, the trend is to underestimate the risks and not to perceive them as deviances even when they start to be compulsive. This paper focuses on the concept of new non-substance addictions and on how some social factors influence, on the one side, this new interaction between man and technology and, on the other side, social relationships in the electronic environment.

The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow monitoring through a synthetic aperture radar (SAR) signal processing technique that has the capability to perform water flow elevation estimation. This paper proposes the application of a new method for the design of a robust procedure to track over the time double-bounce reflections from bridges crossing rivers to measure the gap space existing between the river surface and bridges. Specifically, the difference in position between the single and double bounce is suitably measured over the time. Simulated and satellite temporal series of SAR data from COSMO-SkyMed data are compared to the ground measurements recorded for three gauges sites over the Po and Tiber Rivers, Italy. The obtained performance indices confirm the effectiveness of the method in the estimation of water level also in narrow or ungauged rivers.

In this paper, we propose a novel strategy to estimate the micro-motion (m-m) of ships from synthetic aperture radar (SAR) images. To this end, observe that the problem of motion and m-m detection of targets is usually solved using synthetic aperture radar (SAR) along-track interferometry through two radars spatially separated by a baseline along the azimuth direction. The approach proposed in this paper for m-m estimation of ships, occupying thousands of pixels, processes the information generated during the coregistration of several re-synthesized time-domain and not overlapped Doppler sub-apertures. Specifically, the SAR products are generated by splitting the raw data according to a temporally small baseline using one single wide-band staring spotlight (ST) SAR image. The predominant vibrational modes of different ships are then estimated. The performance analysis is conducted on one ST SAR image recorded by COSMO-SkyMed satellite system. Finally, the newly proposed approach paves the way for application to the surveillance of land-based industry activities.