Accurate flight trajectory knowledge is crucial for airborne and drone-borne repeat-pass Synthetic Aperture Radar (SAR) interferometry. Given that Global Navigation Satellite System (GNSS) inaccuracies are comparable to typical radar wavelengths, especially at high-frequency bands (X-band and higher), estimating the Residual Motion Error (RME) is essential to ensure precise coregistration of Single-Look Complex (SLC) stacks and to generate high-quality repeat-pass interferograms. In this framework, traditional RME estimation techniques, such as the multisquint algorithm, often suffer performance degradation under large errors and high-decorrelation conditions, as they rely heavily on the quality of uncoregistered interferograms. To address this limitation, this paper introduces an enhanced version of the multisquint technique that solves the estimation of RME using a minimization strategy in the complex domain and uses the retrieved RMEs of the entire interferometric dataset to refine the estimates. This approach improves RME estimation robustness against large errors and decorrelation artifacts, particularly in high-frequency airborne and drone-borne SAR datasets. The proposed method is validated using a real dataset acquired with a Ku-band drone-borne SAR system, demonstrating significant improvements in coregistration accuracy. Quantitative evaluations based on interferometric coherence and phase fringe frequency metrics confirm its effectiveness, establishing the proposed refinement as a robust solution for high-precision interferometric applications.

Airborne Synthetic Aperture Radar (SAR) systems have been widely used in the last decades, especially for research purposes, offering higher revisiting time, better spatial resolution, and flexible acquisition geometry compared to satellite radar systems. In recent years, the rapid technological evolution of Remotely Piloted Aircraft (RPA) has opened up the possibility of integrating SAR systems into these agile platforms, allowing the community to explore the new possibilities that they can offer. The present paper introduces the interferometric capabilities of a new Ku-Band SAR system, called SARDrone, that has been completely developed and integrated into an RPA by DARES Technology. The document aims to present the first results obtained with the SARDrone system that demonstrate the inherent potential of multicopter platforms applied to SAR interferometry.

Dynamic Channel Bonding (DCB) allows for the dynamic selection and use of multiple contiguous basic channels in Wireless Local Area Networks (WLANs). A WLAN operating under DCB can enjoy a larger bandwidth, when available, and therefore achieve a higher throughput. However, the use of larger bandwidths also increases the contention with adjacent WLANs, which can result in longer delays in accessing the channel and consequently, a lower throughput. In this paper, a scenario consisting of multiple WLANs using DCB and operating within carrier-sensing range of one another is considered. An analytical framework for evaluating the performance of such networks is presented. The analysis is carried out using a Markov chain model that characterizes the interactions between adjacent WLANs with overlapping channels. An algorithm is proposed for systematically constructing the Markov chain corresponding to any given scenario. The analytical model is then used to highlight and explain the key properties that differentiate DCB networks of WLANs from those operating on a single shared channel. Furthermore, the analysis is applied to networks of IEEE 802.11ac WLANs operating under DCB-which do not fully comply with some of the simplifying assumptions in our analysis-to show that the analytical model can give accurate results in more realistic scenarios.

This paper presents a new phase-alignment algorithm for distributed beamforming [1].1This scheme is more energy-efficient than existing solutions, since it requires each node to transmit only once, but it converges at least as fast as all other previously proposed algorithms. In contrast with all other studies of distributed beamforming, where noise had been neglected, here it is explicitly included in the signal model and shown to have a considerable impact on synchronization that cannot be ignored. With and without noise, analytical expressions that characterize the performance of the new algorithm are provided, with emphasis on various limiting regimes of interest.

This paper presents a new deterministic closed-loop phase-alignment algorithm based on quantized feedback from the receiver for distributed beamforming. In contrast with previously proposed methods, which entailed repeated transmissions from all the nodes in the network, this new algorithm requires each node to transmit only once during the synchronization cycle. This drastically reduces the amount of power consumed to achieve phase alignment, yet the new algorithm converges at least as fast as all other existing schemes. In contrast with previous analyses of distributed beamforming based on random phase updates, where noise had been disregarded, here it is explicitly included in the models and shown to have a considerable effect that cannot be ignored. With and without noise, analytical expressions that characterize the performance of the new algorithm are provided, with emphasis on various limiting regimes of interest.

We investigate the problem of finding minimum-distortion policies for streaming delay-sensitive but distortion-tolerant data. We consider cross-layer approaches which exploit the coupling between presentation and transport layers. We make the natural assumption that the distortion function is convex and decreasing. We focus on a single source-destination pair and analytically find the optimum transmission policy when the transmission is done over an error-free channel. This optimum policy turns out to be independent of the exact form of the convex and decreasing distortion function. Then, for a packet-erasure channel, we analytically find the optimum open-loop transmission policy, which is also independent of the form of the convex distortion function. We then find computationally efficient closed-loop heuristic policies and show, through numerical evaluation, that they outperform the open-loop policy and have near optimal performance.

We investigate the optimum transmission strategy that minimizes the overall distortion for delay-sensitive but distortion-tolerant data. We consider a set of source symbols residing at the transmitter that are encoded into a set of packets using multiresolution source coding. Each packet has a given deadline after which its transmission will be useless. Since multiresolution source codes are being used, the packet lengths can be adjusted by dropping less significant bits in order to allow for the more significant bits of a larger number of packets to be transmitted before the deadline. We find the optimum number of bits that must be transmitted of every packet to minimize the overall distortion when transmissions are error-free. We show that for strictly convex distortion functions the solution is unique and independent of the form of the function, and extend this result to the case where transmitted bits can be affected by noise and find the optimum strategy that leads to the minimum expected distortion. Finally we look at the case where packets arrive according to a given deterministic arrival schedule and present an algorithm that finds the optimum transmission strategy.