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. On-board 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. Firstly, it significantly reduces the data volume to be downlinked, optimizing the usage of limited bandwidth and minimizing transmission delays. Secondly, on-board 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 on-board 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 on-board processing, such as the integration of artificial intelligence and machine learning techniques to enhance data analysis and decision-making capabilities. The advancements in on-board 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 on-board 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.

Recent advances in Synthetic Aperture Radar (SAR) sensors and innovative advanced imagery techniques have enabled SAR systems to acquire very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. The improvements of the SAR system capabilities also imply a significant increase in SAR data acquisition rates, such that efficient and effective compression methods become necessary. The compression of SAR raw data plays a crucial role in addressing the challenges posed by downlink and memory limitations onboard the SAR satellites and directly affects the quality of the generated SAR image. Neural data compression techniques using deep models have attracted many interests for natural image compression tasks and demonstrated promising results. In this study, neural data compression is extended into the complex domain to develop a Complex-Valued (CV) autoencoder-based data compression for SAR raw data. To this end, the basic fundamentals of data compression and Rate-Distortion (RD) theory are reviewed, well known data compression methods, Block Adaptive Quantization (BAQ) and JPEG2000 methods, are implemented and tested for SAR raw data compression, and a neural data compression based on CV autoencoders is developed for SAR raw data. Furthermore, since the available Sentinel-1 SAR raw products are already compressed with Flexible Dynamic BAQ (FDBAQ), an adaptation procedure applied to the decoded SAR raw data to generate SAR raw data with quasi-uniform quantization that resemble the statistics of the uncompressed SAR raw data onboard the satellites.

Next-generation SAR systems will be capable of performing high-resolution, wide-swath acquisitions at frequent revisit times. The overcoming of conventional SAR limitations will also lead to the generation of very large volumes of onboard data which need to be stored and managed by the system and downlinked to the ground. This poses severe constraints in terms of onboard memory requirements and downlink capacity and, in this challenging scenario, the onboard quantization of SAR raw data represents a crucial aspect, acting as a trade-off between the achievable product quality and the resulting onboard volume of data. State-of-the-art quantization schemes allow for enhanced data rate allocation, however, the optimization is directly performed on raw data, without targeting a desired performance on the final higher-level SAR/InSAR product. In this paper, we investigate the use of artificial intelligence (AI), and in particular of deep learning (DL), for developing a flexible onboard SAR raw data quantization method, with the aim of deriving an optimized and fully adaptive data rate allocation given a set of desired performance metrics and requirements in the resulting focused SAR and InSAR products, without relying on a priori information on the acquired scene. Different performance parameters are considered, such as the signal-to-quantization noise ratio (SQNR), the phase errors, the InSAR coherence loss as well as the resulting noise equivalent sigma zero (NESZ), extending the capabilities of the architecture to provide multiple bitrate estimations for a single input scene at the same time, depending on the desired application case. We use experimental TanDEM-X bistatic SAR data, both for the training of the DL model as well as for the validation and demonstration of the suitability of the proposed method. In view of a potential onboard implementation, a possible hardware architecture for the proposed compression scheme is investigated as well.

This paper analyzes the advantages and the drawbacks of using the Synthetic Aperture Radar (SAR) azimuth multichannel technique known as Multi-Aperture Processing Scheme (MAPS), in a set of relevant application cases that are far from the canonical ones. In the scientific literature on this topic, equally distributed azimuth channels with the quasi-monostatic deployment are assumed. With this research, we aim at extending the models from the current literature to (i) a generic bistatic acquisition geometry, (ii) a set of cases where the number of receiving tiles is not the same for each channel, or (iii) the tiles are shared between adjacent channels thus creating an overlapping configuration. The paper introduces the mathematical models for the listed non-conventional MAPS cases. Dealing with the bistatic MAPS, we first solve the problem by interpreting multichannel acquisition as a bank of Linear Time Invariant (LTI) filters. Then, a more physical approach, based on discrimination of the direction of arrivals (DoAs) is pursued. The effectiveness of the two methods and the advantages of the second approach on the first are proved by using a simplified 1D end-to-end simulation. Even limiting to the monostatic configuration, the azimuth antenna tiles have always been supposed equally partitioned among the RX channels. Overcoming this limit has two advantages: (i) more MAPS possible solutions in case few azimuth tiles are available, as in the ROSE-L mission; (ii) the number of channels can be designed independently of the number of tiles, also allowing asymmetric solutions, useful for a phase array antenna with an odd number of tiles such as in the SAOCOM-1 mission. Conversely, sharing one or more receiving tiles in different receiving channels makes the input noise partially correlated. The drawback is an increase in the noise level. A trade-off is determined for the different solutions obtained using simulations with real mission parameters. The theoretical performance and the end-to-end simulations are compared.

Multi-channel synthetic aperture radar (MC-SAR) allows for the high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared to conventional SAR systems. In this letter, we discuss the potential of efficient data volume reduction for MC-SAR. Specifically, we focus on methods based on transform coding (TC) and linear predictive coding (LPC), which exploit the redundancy introduced in the raw data by the finer azimuth sampling peculiar to the multi-channel system. The proposed approaches, in combination with a variable-bit quantization, allow for the optimization of the resulting performance and data rate. We consider three exemplary yet realistic MC-SAR systems, and we conduct simulations as well analyses on synthetic SAR data considering different radar backscatter distributions, which demonstrate the effectiveness of the proposed methods.

The results of the study of the large deployable reflector antenna for a SAR study project being conducted by Airbus for the European Space Agency are presented. The antenna is fed by a large feedarray, and the effect on the antenna patterns of the different feeders are shown. Comparison between the ideal antenna surface and its deployable configuration are presented. An initial study of the rms errors of the deployable antenna configuration is presented.