This paper presents an algorithm of improving the range resolution of passive synthetic aperture radar (SAR) images by combining multiple terrestrial digital video broadcasting (DVB-T) channels while also suppressing artefacts arising from the direct transmit-receive signal. The proposed algorithm has two parts: spectral estimation techniques for combining DVB-T channels and direct signal suppression (DSS) techniques based on modified CLEAN and sub-aperture imagery-based minimum pixel selection (SA-MIPS). Those capabilities can be achieved with a single-channel, wideband receiver and at shorter ranges and are thus consistent with drone-based operation. The performance of the techniques is demonstrated through both simulation and real-world experiments with a drone-based demonstrator.

This paper deals with the exploitation of shadow regions in high-resolution drone-borne synthetic aperture radar (SAR) images and utilizing them for accurate target height and shape estimation. While prior research has shown the potential of SAR shadows for height estimation, these efforts have primarily focused on airborne and spaceborne platforms and lack a theoretical analysis for assessing achievable performance. In contrast, this work introduces a processing chain for target height and shape extraction and proposes a theoretical framework to evaluate system performance. The study demonstrates that the geometric flexibility and maneuverability of drones offer significant advantages in height estimation accuracy compared to conventional SAR platforms. The proposed method is validated using experimental data from a 24 GHz INRAS radar-equipped drone-borne SAR system, showing strong alignment between the extracted 2D shapes and height estimates and the theoretical predictions. Additionally, the study highlights how multi-perspective drone imagery enables the reconstruction of three-dimensional structures, further extending the potential of drone-borne SAR systems.

This article develops and examines methods for the production of real-world, very high-resolution imagery using a high-frequency drone-borne synthetic aperture radar (SAR) operating at short ranges. The significance of motion errors which lead to space-invariant/variant phase errors is discussed. Subsequently, an imaging algorithm capable of handling these errors is proposed and presented. The validity of the approach is tested through both simulation and experiment. We present novel short-range, fine-resolution imagery (less than 2 cm in cross-range) of an extended target area generated using a low-cost, drone-borne vehicular frequency-modulated continuous-wave (FMCW) radar operating at 77 GHz, without using a dedicated inertial navigation system (INS) or Global Positioning System (GPS).

This article explores the possibility to exploit global navigation satellite systems (GNSS) signals to obtain radar imagery of ships. This is a new application area for the GNSS remote sensing, which adds to a rich line of research about the alternative utilization of navigation satellites for remote sensing purposes, which currently includes reflectometry, passive radar, and synthetic aperture radar (SAR) systems. In the field of short-range maritime surveillance, GNSS-based passive radar has already proven to detect and localize ship targets of interest. The possibility to obtain meaningful radar images of observed vessels would represent an additional benefit, opening the doors to noncooperative ship classification capability with this technology. To this purpose, a proper processing chain is here conceived and developed, able to achieve well-focused images of ships while maximizing their signal-to-background ratio. Moreover, the scaling factors needed to map the backscatter energy in the range and cross-range domain are also analytically derived, enabling the estimation of the length of the target. The effectiveness of the proposed approach at obtaining radar images of ship targets and extracting relevant features is confirmed via an experimental campaign, comprising multiple Galileo satellites and a commercial ferry undergoing different kinds of motion.

This article investigates passive synthetic aperture radar (SAR) image formation using Terrestrial Digital Video Broadcast (DVB-T) transmitters of opportunity and an airborne receiver. The proposed airborne system does not use separate channels for direct and reflected signal reception. The proposed image formation algorithm suppresses artifacts due to the presence of the direct signal in radar echo data using modified CLEAN method and accounts for unknown irregularities in airborne platform motion using a map-drift autofocus (MDA) technique. The algorithms are theoretically derived and experimentally confirmed via an appropriate airborne campaign, and their experimental performance is measured.

This paper examines the potential in extracting the instantaneous location of maritime moving targets using a passive multistatic radar with Global Navigation Satellite Systems (GNSS) as illuminators of opportunity and a single receiver. This paper presents a theoretical framework for the localization of a moving target from a set of bistatic range measurements. The algorithm and its predicted accuracy are presented. The localization is achieved by what is essentially a multilateration technique, which can be applied while the transmitting platform is also in motion. The algorithms and the accuracy predictions, as a function of the number of transmitters, have been experimentally confirmed via a dedicated experimental campaign, where two different maritime targets were detected by up to 12 GNSS satellites belonging to different satellite constellations (GPS, GLONASS, and Galileo) simultaneously. To the best of the author's knowledge, these are the first results of their kind and on this scale not only for GNSS-based passive radar but also for multistatic radar in general.

Echolocation is the ability to use sound-echoes to infer spatial information about the environment. Some blind people have developed extraordinary proficiency in echolocation using mouth-clicks. The first step of human biosonar is the transmission (mouth click) and subsequent reception of the resultant sound through the ear. Existing head-related transfer function (HRTF) data bases provide descriptions of reception of the resultant sound. For the current report, we collected a large database of click emissions with three blind people expertly trained in echolocation, which allowed us to perform unprecedented analyses. Specifically, the current report provides the first ever description of the spatial distribution (i.e. beam pattern) of human expert echolocation transmissions, as well as spectro-temporal descriptions at a level of detail not available before. Our data show that transmission levels are fairly constant within a 60° cone emanating from the mouth, but levels drop gradually at further angles, more than for speech. In terms of spectro-temporal features, our data show that emissions are consistently very brief (~3ms duration) with peak frequencies 2-4kHz, but with energy also at 10kHz. This differs from previous reports of durations 3-15ms and peak frequencies 2-8kHz, which were based on less detailed measurements. Based on our measurements we propose to model transmissions as sum of monotones modulated by a decaying exponential, with angular attenuation by a modified cardioid. We provide model parameters for each echolocator. These results are a step towards developing computational models of human biosonar. For example, in bats, spatial and spectro-temporal features of emissions have been used to derive and test model based hypotheses about behaviour. The data we present here suggest similar research opportunities within the context of human echolocation. Relatedly, the data are a basis to develop synthetic models of human echolocation that could be virtual (i.e. simulated) or real (i.e. loudspeaker, microphones), and which will help understanding the link between physical principles and human behaviour.

This paper considers the exploitation of navigation satellite systems as opportunity transmitters for bistatic and multistatic synthetic aperture radar (SAR). The simultaneous availability of multiple satellites over a scene of interest at different viewing angles allows multistatic SAR acquisitions using a single receiver on or near the ground. The resulting spatial diversity could be used to drastically improve image resolution or to enhance image information space. To exploit the availability of multiple satellites, two data fusion approaches are here considered. In the former, point features of the single images obtained from different perspectives are extracted and then combined, whereas in the latter, a multistatic image is first obtained by combining the single channel data at the image level and then the point features are extracted. This is achieved by considering ad hoc CLEAN-like techniques. These techniques have been developed on both the analytical and simulation levels and experimentally verified with real GNSS-based SAR imagery. The techniques described here are not limited to GNSS-based SAR but may be applied to any multistatic SAR system.

This letter demonstrates the feasibility of improving range resolution in passive synthetic aperture radar with the Galileo navigation satellites as transmitters of opportunity. This can be done by an appropriate coherent combination of the Galileo E5a and E5b signals. The proposed approach is analytically derived and is confirmed both by simulation and experiments.

This letter presents an analysis of the multistatic point-spread function (MPSF) for passive synthetic aperture radar (SAR) with navigation satellites as opportunity transmitters and a stationary receiver. It is shown that a noncoherent combination of bistatic SAR images, obtained by multiple, spatially separated satellites, can yield multistatic imagery that may be essentially improved in terms of resolution when compared with a single bistatic SAR image. The MPSF is derived analytically, for an arbitrary number of bistatic acquisitions and for any bistatic topologies. Analytical results are confirmed using both simulated and experimental data. The obtained result could be applied for the analysis of spatial resolution in multistatic real time radar, thus enabling the adaptive selection of the more suitable opportunity transmitters.

This paper analyzes the spatial decorrelation between repeat-pass bistatic synthetic aperture radar (BSAR) images with Global Navigation Satellite Systems as transmitters and a fixed receiver. This study is needed in the development of such a system to monitor temporal changes in a scene. The main challenge is that, in this bistatic configuration, spatial coherence heavily depends on the data acquisition geometry. The appropriate theoretical framework to describe spatial coherence for this case is developed by extending well-established monostatic models and, in principle, can be applied to any fixed-receiver BSAR with a spaceborne transmitter. Theoretical results are initially supported by Monte Carlo simulations. Finally, the validity of the model is confirmed by comparing real images.

This letter conducts a point spread function (PSF) analysis for bistatic synthetic aperture radar (BSAR) systems where the transmitter is in medium Earth orbit, and the receiver is fixed on the ground. To achieve a reasonable azimuth resolution under such a configuration, the trajectory of the satellite can no longer be approximated as a straight line; therefore, current methods for PSF analysis are insufficient. The solution proposed involves extension of the generalized ambiguity function to accommodate satellite trajectory curvature. The theoretical analysis shows effects unlike those observed in monostatic or even the general BSAR and is verified by both simulation and experimental results using navigation satellites as the transmitting platforms.

This paper presents proof-of-concept methods and results on a passive synthetic aperture radar for surface change monitoring. The bistatic topology comprises navigation satellites (such as Global Positioning System, GLObal Navigation Satellite System (GLONASS), Galileo, or Beidou) as transmitters of opportunity and a fixed receiver on the ground. Surface monitoring is to be provided through coherent change detection. An experimental test bed built to confirm the concept of the system is described, and the appropriate experimental program is presented. Preliminary signal processing algorithms for change detection are derived. Experimentally measured changes are compared to the theoretical predictions, and the obtained results are discussed.

This letter demonstrates the feasibility of space-surface bistatic synthetic aperture radar using navigation satellites as transmitters of opportunity and a fixed ground-based receiver. Experiments with real satellite signals are described, and the obtained imagery is presented and discussed.

This paper presents algorithms designed for a subclass of bistatic synthetic aperture radar (BSAR) called space-surface BSAR (SS-BSAR). Two SS-BSAR configurations are considered. The first one assumes a stationary, spaceborne transmitter and a moving airborne receiver. The second case is the generalized SS-BSAR configuration, where the transmitter is nonstationary. The transmitter and receiver have essentially different flight paths and velocities. For each configuration under investigation, the characteristics of the corresponding SS-BSAR received signal are examined first. Then, each proposed algorithm is derived analytically, and verified via simulation.

This paper reports progress in the development of an image formation algorithm suitable for stripmap space-surface bistatic synthetic aperture radar. A description of the proposed algorithm, which is a modification of the standard range-Doppler algorithm, is provided for the case when the transmitter and the receiver have parallel flight paths and unequal velocities. Both simulation and initial experimental results are presented to verify our analysis.