Efficient SAR Raw Data Compression in Frequency Domain

SAR raw data compression is necessary to reduce huge amounts of SAR data for a memory on board a satellite, space shuttle or aircraft and for later downlink to a ground station. In view of interferometric and polarimetric applications for SAR data, it becomes more and more important to pay attention to phase errors caused by data compression. Herein, a detailed comparison of block adaptive quantization in time domain (BAQ) and in frequency domain (FFT-BAQ) is given. Inclusion of raw data compression in the processing chain allows an efficient use of the FFT-BAQ and makes implementation for on-board data compression feasible. The FFT-BAQ outperforms the BAQ in terms of signal-to-quantization noise ratio and phase error and allows a direct decimation of the oversampled data equivalent to FIR-filtering in time domain. Impacts on interferometric phase and coherency are also given

Some practical aspects of lossless and nearly-lossless compression of AVHRR imagery

Compression of Advanced Very high Resolution Radiometers (AVHRR) imagery operating in a lossless or nearly-lossless mode is evaluated. Several practical issues are analyzed including: variability of compression over time and among channels, rate-smoothing buffer size, multi-spectral preprocessing of data, day/night handling, and impact on key operational data applications. This analysis is based on a DPCM algorithm employing the Universal Noiseless Coder, which is a candidate for inclusion in many future remote sensing systems. It is shown that compression rates of about 2:1 (daytime) can be achieved with modest buffer sizes (less than or equal to 2.5 Mbytes) and a relatively simple multi-spectral preprocessing step

Advanced techniques and technology for efficient data storage, access, and transfer

Advanced techniques for efficiently representing most forms of data are being implemented in practical hardware and software form through the joint efforts of three NASA centers. These techniques adapt to local statistical variations to continually provide near optimum code efficiency when representing data without error. Demonstrated in several earlier space applications, these techniques are the basis of initial NASA data compression standards specifications. Since the techniques clearly apply to most NASA science data, NASA invested in the development of both hardware and software implementations for general use. This investment includes high-speed single-chip very large scale integration (VLSI) coding and decoding modules as well as machine-transferrable software routines. The hardware chips were tested in the laboratory at data rates as high as 700 Mbits/s. A coding module's definition includes a predictive preprocessing stage and a powerful adaptive coding stage. The function of the preprocessor is to optimally process incoming data into a standard form data source that the second stage can handle.The built-in preprocessor of the VLSI coder chips is ideal for high-speed sampled data applications such as imaging and high-quality audio, but additionally, the second stage adaptive coder can be used separately with any source that can be externally preprocessed into the 'standard form'. This generic functionality assures that the applicability of these techniques and their recent high-speed implementations should be equally broad outside of NASA

Image and radio-frequency data compression for OPS-SAT using FAPEC

OPS-SAT is an ESA technology demonstration cubesat which includes a colour camera, a Software Defined Radio (SDR) receiver and a powerful ARM processor. One of the experiments executed there is FAPEC, a high-performance and versatile data compression software. Among others, it features image compression and linear prediction coding algorithms, suitable for multi-band and baseband radio-frequency (RF) samples, respectively. Since its deployment on-board OPS-SAT in late 2020, FAPEC has allowed for downloading a large set of Earth Observation images. Recently, thanks to ESA Open Space Innovation Platform funds, these two algorithms from FAPEC are being improved to get better compression ratios and speeds, add video compression capabilities, and higher quality levels in case of lossy compression. A smart lossy approach is being developed for radio-frequency data, identifying the time segments with signal presence and quantizing noise-only samples to further reduce the size. In order to identify the really useful files to be downloaded from the satellite, on-board data analysis capabilities are being developed as well. In this work we present in-orbit results, recent developments and preliminary results obtained with the new algorithms on real data.Peer ReviewedPostprint (published version

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

In recent years the growth in quantity, diversity and capability of Earth Observation (EO) satellites, has enabled increase’s in the achievable payload data dimensionality and volume. However, the lack of equivalent advancement in downlink technology has resulted in the development of an onboard data bottleneck. This bottleneck must be alleviated in order for EO satellites to continue to efficiently provide high quality and increasing quantities of payload data. This research explores the selection and implementation of state-of-the-art multidimensional image compression algorithms and proposes a new onboard data processing architecture, to help alleviate the bottleneck and increase the data throughput of the platform. The proposed new system is based upon a backplane architecture to provide scalability with different satellite platform sizes and varying mission’s objectives. The heterogeneous nature of the architecture allows benefits of both Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU) hardware to be leveraged for maximised data processing throughput

Proceedings of the Scientific Data Compression Workshop

Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms

Context-aware lossless and lossy compression of radio frequency signals

We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson–Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions. We test the lossless algorithm using two different datasets. The first one was obtained from OPS-SAT, an ESA CubeSat, while the second one was obtained using a SDRplay RSPdx in Barcelona, Spain. The results show that our approach achieves compression ratios that are 23% better than gzip (on average) and very similar to those of FLAC, but at higher speeds. We also assess the performance of our signal detectors using the second dataset. We show that high ratios can be achieved thanks to the lossy compression of the segments without any relevant signal.This work was (partially) funded by the European Space Agency (ESA) Contract No. 4000137290, the Spanish Ministry of Science and Innovation projects PID2019-105717RB-C22 (RODIN) and PID2021-122842OB-C21, the ERDF (a way of making Europe) by the European Union, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia María de Maeztu) through grant CEX2019-000918-M, grant 2021 SGR 1033 by Generalitat de Catalunya (AGAUR), and fellowship FPI-UPC 2022 by Universitat Politècnica de Catalunya and Banc de Santander.Postprint (published version

The ARIEL Instrument Control Unit design for the M4 Mission Selection Review of the ESA's Cosmic Vision Program

The Atmospheric Remote-sensing Infrared Exoplanet Large-survey mission (ARIEL) is one of the three present candidates for the ESA M4 (the fourth medium mission) launch opportunity. The proposed Payload will perform a large unbiased spectroscopic survey from space concerning the nature of exoplanets atmospheres and their interiors to determine the key factors affecting the formation and evolution of planetary systems. ARIEL will observe a large number (>500) of warm and hot transiting gas giants, Neptunes and super-Earths around a wide range of host star types, targeting planets hotter than 600 K to take advantage of their well-mixed atmospheres. It will exploit primary and secondary transits spectroscopy in the 1.2-8 um spectral range and broad-band photometry in the optical and Near IR (NIR). The main instrument of the ARIEL Payload is the IR Spectrometer (AIRS) providing low-resolution spectroscopy in two IR channels: Channel 0 (CH0) for the 1.95-3.90 um band and Channel 1 (CH1) for the 3.90-7.80 um range. It is located at the intermediate focal plane of the telescope and common optical system and it hosts two IR sensors and two cold front-end electronics (CFEE) for detectors readout, a well defined process calibrated for the selected target brightness and driven by the Payload's Instrument Control Unit (ICU).Comment: Experimental Astronomy, Special Issue on ARIEL, (2017

High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

The capacity of the downlink channel is a major bottleneck for applications based on remotesensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amountof HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraintsof spaceborne devices impose limitations on the complexity of practical compression algorithms.To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in thisstudy. This work aims at finding the most advantageous compression-complexity trade-off withinthe state of the art in HSI compression. To do so, a novel comparison of the most competitive spectraldecorrelation approaches combined with the best performing low-complexity compressors of thestate is presented. Compression performance and execution time results are obtained for a set of47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming onlya limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields thebest trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster andits compressed data rates are on average within 16% of the CCSDS standard. In scenarios whereenergy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of allevaluated methods

A technology assessment of alternative communications systems for the space exploration initiative

Telecommunications, Navigation, and Information Management (TNIM) services are vital to accomplish the ambitious goals of the Space Exploration Initiative (SEI). A technology assessment is provided for four alternative lunar and Mars operational TNIM systems based on detailed communications link analyses. The four alternative systems range from a minimum to a fully enhanced capability and use frequencies from S-band, through Ka-band, and up to optical wavelengths. Included are technology development schedules as they relate to present SEI mission architecture time frames