This paper introduces a new waveform design approach called COSMIC (Connectivity-Oriented Sensing Method for Imaging and Communication). The method enables the creation of radio images of the environment by applying an extended orthogonality condition to the waveforms. Unlike conventional systems that use time, frequency, or space multiplexing, COSMIC achieves orthogonality through algebraic precoding of the signals from all antennas. Additionally, COSMIC takes advantage of the fact that the imaging field of view is much smaller than the length of the transmitted signals, allowing the waveforms to carry communication data without disrupting the sensing function. Simulations show that COSMIC waveforms enable precise environmental imaging while maintaining good communication performance in terms of error rates.

This paper illustrates the potential of an Integrated Sensing and Communication (ISAC) system, operating in the sub-6 GHz frequency range, for Synthetic Aperture Radar (SAR) imaging via an Unmanned Aerial Vehicle (UAV) employed as an aerial base station. The primary aim is to validate the system's ability to generate SAR imagery within the confines of modern communication standards, including considerations like power limits, carrier frequency, bandwidth, and other relevant parameters. The paper presents two methods for processing the signal reflected by the scene. Additionally, we analyze two key performance indicators for their respective fields, the Noise Equivalent Sigma Zero (NESZ) and the Bit Error Rate (BER), using the QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa), demonstrating the system's capability to image buried targets in challenging scenarios. The paper shows simulated Impulse Response Functions (IRF) as possible pulse compression techniques under different assumptions. An experimental campaign is conducted to validate the proposed setup by producing a SAR image of the environment captured using a UAV flying with a Software-Defined Radio (SDR) as a payload.

Developing algorithms to detect temporal and spatial changes in radar targets is paramount. This paper specifically addresses the temporal change detection aspect, introducing a rapid non-parametric Coherent Change Detection (CCD) algorithm named Fast-Permutational Change Detection (F-PCD). The F-PCD identifies temporal Change Points (CPs) in a radar target by recognizing block structures in the coherence matrix, showing great robustness against non-stationary noise sources that generally affect the performance of the standard approaches. Moreover, the F-PCD is characterized by an accelerated inference process, ensuring efficiency without substantial performance loss. The F-PCD algorithm can be applied to different scenarios, for example, where DEM changes happen, e.g., mining sites, volcano eruptions, and earthquakes. For this reason, an example of the F-PCD application on an active open-pit mining site is presented to validate its effectiveness. Moreover, its generalization capability is demonstrated by a multi frequency-geometry analysis conducted on the same mining site. Finally, fully exploiting the F-PCD outcomes contributes to a broader understanding of temporal changes in SAR data and introduces new perspectives for interpreting InSAR datasets.

This paper aims to showcase the potential of an Integrated Communication and Sensing (ISAC) system, operating within the sub-6 GHz frequency range, for Synthetic Aperture Radar (SAR) imaging through an Unmanned Aerial Vehicle (UAV). Our primary goal is to validate the system’s ability to generate SAR imagery under practical constraints dictated by contemporary communication standards, including factors like maximum transmitted power, carrier frequency, occupied bandwidth, Pulse Repetition Frequency, and the number of sub-carriers. The paper provides a detailed description of the Orthogonal Frequency Division Multiplexing (OFDM) signal transmitted by the base station. We compare two methods for range-compressing the signal backscattered by the scene and analyze the Noise Equivalent Sigma Zero (NESZ) under classical line-of-sight conditions and in challenging environments, demonstrating the system’s capability to detect targets under snow. It also showcases simulated Impulse Response Functions (IRF) under various assumptions, as well as real SAR images of the environment obtained using a UAV with a software-defined radar (SDR) integrated as a payload.

This work provides the signal processing workflow to focus Unmanned Aerial Vehicles (UAV) Synthetic Aperture Radar (SAR) images with partially unknown or corrupted trajectories. The processing chain is divided into two modular blocks. The former involves a novel and low-complexity autofocusing technique. This method applies geometric corrections directly to the nominal trajectory, getting rid of the assumption of a constant phase correction, that does not hold with highly variable squint and off-nadir angles, as for the case of UAV-borne SAR. The latter modular block concerns a Fast Factorised Back Projection (FFBP) based focusing scheme. In particular, for a given scenario the processor computes a priori the computational burden in order to define the most appropriate reference system and the degree of hierarchical merging to focus the scene at the minimum computational cost. In light of this, the proposed focusing algorithm is able to deal with a complex trajectory typical of UAVs and to focus a large image at high resolution. Two scenarios are considered. The former is a UAV-borne SAR experiment with a small aperture, a long range, and a wide area covered by a large antenna aperture. The latter is a proper UAV-borne SAR stripmap scenario. Here, the proposed focusing scheme performs better than the traditional FFBP. Finally, the results are supported by a numerical simulation to prove the effectiveness of the whole processing scheme.

Coherent multistatic radio imaging represents a pivotal opportunity for forthcoming wireless networks, which involves distributed nodes cooperating to achieve accurate sensing resolution and robustness. This paper delves into cooperative coherent imaging for vehicular radar networks. Herein, multiple radar-equipped vehicles cooperate to improve collective sensing capabilities and address the fundamental issue of distinguishing weak targets in close proximity to strong ones, a critical challenge for vulnerable road users’ protection. We prove the significant benefits of cooperative coherent imaging in the considered automotive scenario in terms of both probability of correct detection, evaluated considering several system parameters, as well as resolution capabilities, showcased by a dedicated experimental campaign wherein the collaboration between two vehicles enables the detection of the legs of a pedestrian close to a parked car. Moreover, as coherent processing of several sensors’ data requires very tight accuracy on clock synchronization and sensor’s positioning—referred to as phase synchronization—(such that to predict sensor-target distances up to a fraction of the carrier wavelength), we present a general three-step cooperative multistatic phase synchronization procedure, detailing the required information exchange among vehicles in the specific automotive radar context and assessing its feasibility and performance by hybrid Cramér-Rao bound.

Nowadays, Synthetic Aperture Radar (SAR) is widely used in heterogeneous fields with aims strictly dependent on the objectives of the application. One of the most common is the exploitation of the Interferometric-SAR (InSAR) to measure millimeter movements on the Earth’s surface, aiming to monitor failures (e.g. landslides) or to measure the health state of infrastructures (e.g. mining assets, bridges, buildings, etc). In this context, developing algorithms to detect temporal and spatial changes in the radar targets becomes very important. This paper focuses on the temporal change detection framework, proposing a non-parametric Coherent Change Detection (CCD) algorithm called Permutational Change Detection (PCD), a purely statistical algorithm whose core is the Permutation Test. The PCD estimates the temporal Change Points (CPs) of a radar target recognizingblocks structurein the coherence matrix, namely new radar objects. The algorithm has been fine-tuned for small SAR datasets, with the specific aim of prioritizing the analysis of the latest changes. A rigorous mathematical derivation of the algorithm is carried out, explaining how some limits have been addressed. Then, the performance analysis on simulated data is deeply accomplished, carried out for the stand-alone PCD and for the PCD compared with a parametric CCD algorithm based on the Generalized Likelihood Ratio Test (GLRT), and with the Omnibus and REACTIV detectors. The comparison with these other algorithms and the stand-alone performance analysis point out the robustness of the PCD in dealing with very noisy environments, even in the case of a single block. Finally, the PCD is validated by processing two Sentinel I data stacks, ascending and descending geometry, of the 2016 Central Italy earthquake.

This work investigates a new concept to finely resolve the vertical structure of natural media, like snow, ice, vegetation, by using a formation of spaceborne Synthetic Aperture Radars (SAR) mounted onboard different satellites. The formation is assumed to operate in Multiple Input Multiple Output (MIMO) mode by implementing a Frequency Division Multiplexing (FDM) access scheme, where all satellites transmit simultaneously on different frequency bands and receive the echoes scattered by the Earth’s surface in all transmitted bands. In so-doing, a formation onNsatellites is used to produceN2SAR images. By the principle of Diffraction Tomography, each of these images represents a distinct set of wavenumbers, i.e. a distinct region of the spatial spectrum of the observed scene. The vertical separation between any two sets of wavenumbers defines the interferometric differential wavenumber, which determines the sensitivity of that particular pair to the vertial structure of the observed scene. Fine vertical resolution is achieved by developing a novel approach to set the satellite positions in such a way that the resulting interferometric differential wavenumbers form an almost uniformly-spaced array of maximum length under the constraint of a given height of ambiguity and interferometric coherence magnitude. As a result, we show two examples where formations of 4 or 5 satellites are deployed to provide the equivalent of 17 and 26 monostatic acquisitions, respectively. Such figures are comparable to the best airborne and ground-based systems available as of today, and indicate the concrete possibility to image the vertical structure of natural targets from space at fine resolution. The concept here developed to deploy the formation is referred to as Minimum Redundancy Wavenumber Illumination (MRWI), as it is shown to be a generalization to distributed targets of the principle of Minimum Redundancy Virtual Array (MRVA) used in array theory. The analysis is supported by results from synthetic data generated by numerical simulations.

This article evaluates the recently introduced phase histogram (PH) technique for estimating forest height and vertical structure using theoretical modeling and experimental synthetic aperture radar (SAR) data. The PH technique assigns each pixel in an SAR interferogram to a specific height bin based on the value of the corresponding interferometric phase, thus allowing for the estimation of the forest’s vertical structure by accumulating pixels magnitudes within a given spatial window. This approach is radically different from the one employed by SAR tomography (TomoSAR), which allows for direct imaging of the 3-D structure of the vegetation by jointly focusing on SAR data from multiple trajectories. Importantly, PHs can be built using as few as two images (a single interferogram), whereas TomoSAR is well-known to perform best when many images area available. Accordingly, the main question we intend to address in this article is to what extent and in which conditions single-baseline PHs can be used as a surrogate of TomoSAR (in the absence of multibaseline data). Experimental analyses are conducted using L-band tomographic SAR data from the ESA campaign TomoSense, flown in 2020 at Eifel Park in North West Germany. TomoSense data include 30 + 30 monostatic overpasses acquired along two opposite flight headings, and are complemented by airborne, terrestrial, and unmanned aerial vehicle (UAV) Lidar surveys. Lidar data are used to generate a forest canopy height model (CHM) and vertical profiles of leaf area density (LAD), taken as the main reference in the evaluation of PHs. Multibaseline tomographic data are produced and investigated to assess the actual sensitivity of radar data to forest structure at this site, as well as to provide indications about the performance of a radar instrument when multiple baselines are available. Experimental results indicate that the PH technique can only loosely approximate the vertical structure produced by TomoSAR. Still, it can produce a reasonably good estimate of forest height. In particular, TomoSAR and the PH technique are observed to have an average root mean square error (RMSE) with respect to Lidar estimate of 2.8 and 4.45 m in North-West heading data, and 1.84 and 5.46 m in South-East heading data, respectively. The observed results are interpreted in light of a simple physical model to characterize PHs depending on the number of scatterers within the SAR resolution cell, on which basis we derive analytical expressions to predict height dispersion in PHs. The proposed model indicates that the concept of PH is inherently based on the assumption of a single dominant scatterer within any single SAR resolution cell. If this is not the case, PHs produce an intrinsic dispersion that does not represent the actual vertical distribution of scatterers within the vegetation. Consistently, we conclude that the PH technique is inherently best suited for the analysis of high- or very-high resolution data, which suggests its use in the context of higher frequency SAR missions (e.g., Tandem-X) and when there are few acquisitions available.

This paper tackles two open problems concerning automotive Synthetic Aperture Radar (SAR): whether it is possible to develop a fast and efficient focusing routine and assess the effect of multipath on MIMO-SAR images. First, we present a processing technique that enables real-time imaging of the scene under observation. The idea is that the SAR image is already present in the Range-Angle-Velocity (RAV) data cube and must be extracted with a simple 3D interpolation. Unlike standard Doppler beam sharpening techniques, this processor is accurate since it handles range migration and phase curvature. For what concerns the multipath, instead, we introduce a simple yet effective approach to mitigate this issue, which significantly improves the robustness of SAR systems concerning multipath. To showcase the capabilities and potential of our proposed method, we conducted a comprehensive set of experiments utilizing simulated data. The results were not only able to demonstrate the enhancement of robustness of the SAR system in dealing with multipath but also highlighted the ability of our approach to deliver highly accurate and high-quality real-time imaging of the scene being surveyed.

This article discusses the effect of multipath in automotive radar imaging under different sensor configurations. The study is motivated by the fact that radar technologies are becoming indispensable in the automotive scenario. Many applications such as collision avoidance systems, assisted parking, and driving assistance systems take advantage of radar technologies to accomplish their task. However, one of the main concerns about automotive radars is the possibility of detecting false targets due to multiple signal reflections. In this article, we show how different sensor layouts experience multipath differently. In particular, we demonstrate that with multiple-input multiple-output (MIMO) radars, what really matters is the physical positions of the transmitting and receiving antennas. The monostatic/bistatic equivalent configurations cannot be used to design a system and to simulate an acquisition in the presence of a multipath. We also demonstrate how vehicle-based MIMO-synthetic aperture radar (MIMO-SAR) imaging can generate a bi-dimensional aperture which significantly reduces multipath effects in the focused image, avoiding the detection of false targets. All the theoretical analyses are supported by several simulations where different sensor layouts are tested, and the capability of MIMO-SAR to reject multipath is validated.

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle’s ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-calledautofocusalgorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocusrequirementsin typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets’ localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.

Differential SAR interferometry (DInSAR) is a useful technique used to measure small movements and surface deformation. However, ionospheric phase screens are a major error source in multipass terrain observation by progressive scans sar (TOPSAR) interferograms. In this letter, an improved split-spectrum method is proposed. First, the burst used for ionospheric phase estimation is selected through coherence, and then, the ionospheric phase of the burst is estimated based on the split-spectrum method. Finally, the TOPSAR ionospheric space-variable phase in a large scene is obtained through 2-D space-variable fitting, which avoids the complicated processing of splicing between bursts of different periods and reduces the number of unwrapping calculations for large scenes after splicing. The method can ensure that the number of calculations is reduced without loss of accuracy. Sentinel-1 TOPSAR real data processing verifies the correctness of the proposed method.

We discuss a coherent synthetic aperture radar (SAR) formation where$N$identical sensors transmit at the same time, code, and frequency. This is a particular multiple-input–multiple-output (MIMO) configuration, where the transmitted waveforms interfere together, resulting in an illumination pattern that randomly changes in space and time. Similar to the single-input–multiple-output (SIMO) formations, the diversity provided by the$N$receiver phase centers can be used to mitigate this interference and reduce the pulse repetition frequency (PRF) for achieving large swath coverage. The good point, in the MIMO case, is that the signal-to-noise ratio (SNR) gain of the system increases, theoretically, with the square of the number of elements. However, residual spurious sidelobes may appear as ghosts of the multiple illuminators. In practice, the power gain is to be optimized, together with ambiguity rejection, sidelobes, and azimuth resolution. The actual performances achievable by these formations in terms of impulse response function (IRF), SNR, and sensitivity to the precise positioning of the sensors are discussed theoretically and based on simulations.

We propose a full Space and Time Coherent Change Detection (ST-CCD) that takes advantage of Sentinel-1 (S1) short interferometric revisit to enhance sensitivity in detecting changes at a fine space resolution. It extends the Generalized Likelihood Ratio Test (GLRT) used for change detection by single baseline images, to the multi-pass, multi-polarimetric stacks of S1 data. Applications are shown for damage mapping after hazards and for agriculture. A preliminary validation is performed by processing data referred to central Italy earthquake in 2016.

This paper focuses on the detection, from a stack of repeated-pass interferometric synthetic aperture radar (SAR) images, of such changes causing a target to completely lose the correlation between one epoch and another. This can be the consequence of human activities, such as construction, destruction, and agricultural activities, and also be the consequence of hazards, such as earthquake, landslides, or flooding, to buildings or terrains. The millimetric sensitivity of SAR makes it valuable for detecting such changes. This paper approaches two coherent change detection methods: a space coherent, time incoherent one and a full space and time coherent one, both based on the generalized likelihood ratiob (LR) test. A preliminary validation of the method is provided by processing two Sentinel-1 data stacks of 2016 Central Italy earthquake and by comparing the results with the map of damaged buildings in Amatrice and Accumoli made by Copernicus Emergency Management Service.