SAR Satellite On-Board Ship, Wind, and Sea State Detection

This paper describes a prototype implementation of ship, wind, and sea state detection algorithms for satellite on-board SAR processing designed for Maritime Situation Awareness. Existing algorithms were adapted to run on a Multi- Processor-System-On-Chip (MPSoC) combining an FPGA and an ARM CPU and further optimized for fast runtime on the system. The achieved processing times were 20 s for ship detection and 16 s for sea state detection on a 29Mpx SAR image. SAR processing is one component of a larger prototype system being developed in the frame of the H2020 project EO-ALERT, which further comprises an optical data chain, data compression/encryption, and delivery on multiple MPSoC boards. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Demonstrating a SAR Satellite Onboard Processing Chain

The EO-ALERT project, being implemented by a European consortium in the frame of the European Union’s Horizon 2020 programme, has developed, built, and tested a prototype demonstrator chain comprising onboard raw EO data (SAR and optical) Level 1 processing, Level 2 image processing and transfer of final EO alert products to the end user. The goal of the project is to demonstrate the feasibility of the concept aiming at overall latencies below 5 minutes even down to 1 minute. The task of DLR within the project was to implement a low latency SAR onboard Level 1 and Level 2 processing chain. SAR processing is well known for its high demand for computational resources. The targeted latencies together with low power and low mass constraints require the use of Field-Programmable Gate Array (FPGA) technology. Multi-Processor System on a Chip (MPSoC) devices were chosen for all onboard data chain developments. SAR image formation (IF) and SAR image processing (IP) are integrated in a single bitstream file and loaded into a single MPSoC device. The the paper provides an overview of the demonstrator chain built in the project, the SAR processing chain implementation and the achieved test result

Synthetic Aperture Radar Image Formation and Processing on an MPSoC

Satellite remote sensing acquisitions are usually processed after downlink to a ground station. The satellite travel time to the ground station adds to the total latency, increasing the time until a user can obtain the processing results. Performing the processing and information extraction onboard of the satellite can significantly reduce this time. In this study, synthetic aperture radar (SAR) image formation as well as ship detection and extreme weather detection were implemented in a multiprocessor system on a chip (MPSoC). Processing steps with high computational complexity were ported to run on the programmable logic (PL), achieving significant speed-up by implementing a high degree of parallelization and pipelining as well as efficient memory accesses. Steps with lower complexity run on the processing system (PS), allowing for higher flexibility and reducing the need for resources in the PL. The achieved processing times for an area covering 375 km2 were approximately 4 s for image formation, 16 s for ship detection, and 31 s for extreme weather detection. These evelopments combined with new downlink concepts for low-rate information data streams show that the provision of satellite remote sensing results to end users in less than 5 min after acquisition is possible using an adequately equipped satellite

Generation of Rapid Civil Alerts by Satellite On-Board SAR Processing

The concept and prototype implementation of a satellite on-board SAR processing chain designed for Maritime Situation Awareness is described. It aims to reduce the latency between data acquisition and product delivery to about 3-4 minutes. SAR processing is one component of a larger prototype system being developed in the frame of the H2020 EO-ALERT project. It further comprises an optical data chain, data compression/encryption, and delivery. The system employs multiple boards with Multi-Processor-System-On-Chip (MPSoC) combining FPGAs and ARM CPUs. Low latency data processing was a key development goal, hence, a tailored workflow and adapted L1 and L2 processing algorithms ensure that the requirements for latency and product quality are met. The SAR processor is designed to generate SAR imagery from TerraSAR-X stripmap data for subsequent ship detection and sea state determination. The achieved overall L1 and L2 processing times were 60 s for ship detection and 105 s for sea state determination on a 1125 km² SAR image. These results enable further work towards a new generation of Earth Observation satellites with similar processing capabilities on-board, providing users with products only a few minutes after acquisition

Advanced Data Chain Technologies for the Next Generation of Earth Observation Satellites Supporting On-Board Processing for Rapid Civil Alerts

The growing number of planned Earth Observation (EO) satellites, together with the increase in payload resolution and swath, brings to the fore the generation of unprecedented volumes of data that needs to be downloaded, processed and distributed with low latency. This creates a severe bottleneck problem, which overloads ground infrastructure, communications to ground, and hampers the provision of EO products to the End User with the required performances. The European H2020 EO-ALERT project (http://eo-alert-h2020.eu/), proposes the definition of next-generation EO missions by developing an on-board high speed EO data processing chain, based on a novel flight segment architecture that moves optimised key EO data processing elements from the ground segment to on-board the satellite. EO-ALERT achieves, globally, latencies below five minutes for EO products delivery, reaching latencies below 1 minute in some scenarios. The proposed architecture solves the above challenges through a combination of innovations in the on-board elements of the data chain and the communications link. Namely, the architecture introduces innovative technological solutions, including on-board reconfigurable data handling, on-board image generation and processing for generation of alerts (EO products) using Artificial Intelligence (AI), high-speed on-board avionics, on-board data compression and encryption using AI and reconfigurable high data rate communication links to ground including a separate chain for alerts with minimum latency and global coverage. Those key technologies have been studied, developed, implemented in software/hardware (SW/HW) and verified against previously established technologies requirements to meet the identified user needs. The paper presents an overview of the development of the innovative solutions defined during the project for each of the above mentioned technological areas and the results of the testing campaign of the individual SW/HW implementations within the context of two operational scenarios: ship detection and extreme weather observation (nowcasting), both requiring a high responsiveness to events to reduce the response time to few hours, or even to minutes, after an emergency situation arises. The technologies have been experimentally evaluated during the project using relevant EO historical sensor data. The results demonstrate the maturity of the technologies, having now reached TRL 4-5. Generally, the results show that, when implemented using COTS components and available communication links, the proposed architecture can generate and delivery globally EO products/alerts with a latency lower than five minutes, which demonstrates the viability of the EO-ALERT concept. The paper also discusses the implementation on an Avionic Test Bench (ATB) for the validation of the integrated technologies chain

A Novel Architecture for the Next Generation of Earth Observation Satellites Supporting Rapid Civil Alert

The EO-ALERT European Commission H2020 project proposes the definition, development, and verification and validation through ground hardware testing, of a next-generation Earth Observation (EO) data processing chain. The proposed data processing chain is based on a novel flight segment architecture that moves EO data processing elements traditionally executed in the ground segment to on-board the satellite, with the aim of delivering EO products to the end user with very low latency. EO-ALERT achieves, globally, latencies below five minutes for EO products delivery, and below one minute in realistic scenarios. The proposed EO-ALERT architecture is enabled by on-board processing, recent improvements in processing hardware using Commercial Off-The-Shelf (COTS) components, and persistent space-to-ground communications links. EO-ALERT combines innovations in the on-board elements of the data chain and the communications, namely: on-board reconfigurable data handling, on-board image generation and processing for the generation of alerts (EO products) using Machine Learning (ML) and Artificial Intelligence (AI), on-board AI-based compression and encryption, high-speed on-board avionics, and reconfigurable high data rate communication links to ground, including a separate chain for alerts with minimum latency and global coverage. This paper presents the proposed architecture, its hardware realization for the ground testing in a representative environment and its performance. The architecture’s performance is evaluated considering two different user scenarios where very low latency (almost-real-time) EO product delivery is required: ship detection and extreme weather monitoring/nowcasting. The hardware testing results show that, when implemented using COTS components and available communication links, the proposed architecture can deliver alerts to the end user with a latency below five minutes, for both SAR and Optical missions, demonstrating the viability of the EO-ALERT architecture. In particular, in several test scenarios, for both the TerraSAR-X SAR and DEIMOS-2 Optical Very High Resolution (VHR) missions, hardware testing of the proposed architecture has shown it can deliver EO products and alerts to the end user globally, with latency lower than one-point-five minutes

FPGA Implementation of a Scalable SAR Image Processor for CFAR Object Detection

Ship detection on satellite SAR imagery regularly relies on CFAR algorithms for bright pixel discrimination. However, these algorithms are computationally complex, resulting in long processing times especially for high-resolution images, which is in conflict with the time-criticality of ship detection products. To satisfy the requirement of high product resolution and rapid delivery, an FPGA-based implementation of cell-averaging CFAR is presented. For the first time, High Bandwidth Memory is employed to eliminate previous memory bottlenecks and make full use of a pipelined datapath. Processing of a full Sentinel-1 IW high-resolution scene finishes in less than 8 seconds, while even higher resolution products such as TerraSAR-X StripMap are supported as well and processed in under 15 seconds. At the same time, power efficiency improves by an order of magnitude compared to a conventional server-grade CPU

Maritime Sicherheit durch Datenprozessierung direkt im Satelliten

Erdbeobachtungssatelliten liefern rund um die Uhr aktuelle Aufnahmen der Erde. Bis diese aber beim Nutzer ankommen vergeht wertvolle Zeit, besonders in heiklen Situationen auf See. Das im Vortrag vorgestellte EU-Projekt EO-ALERT demonstriert, wie diese Zeit in zukünftigen Missionen deutlich reduziert werden könnte