KEYWORDS: Image compression, Chromium, Chemical species, Saturn, Wavelets, Data compression, Space operations, Distortion, Signal to noise ratio, Plasma
We investigated data compression algorithms to boost science data return from high-data-volume instruments on planetary missions, particularly outer solar system missions where every bit of data represents an engineering triumph of over severe constraints on mass (limiting antenna size) and power (limiting signal strength). We developed a methodology to (1) investigate algorithms to improve compression and (2) to work with the science teams to evaluate the effects on the science. Our algorithm for compressing the Cassini Radio Plasma Wave Science (RPWS) data achieved a factor of 5 improvement in data compression (relative to what the RPWS team was using), and our algorithm for the Cassini Ultraviolet Imaging Spectrograph (UVIS) Saturn data set achieved a much higher factor (∼70). In both cases, the investigators on the science teams who evaluated our results reported that the science goals were not compromised. Our compression algorithm for Imaging Science Subsystem images achieved on average a factor of ∼1.7 improvement in lossless compression compared to the original algorithm. We also evaluated the compression effectiveness of JPL’s Fast Lossless EXtended (FLEX) hyperspectral/multispectral image compressor on Cassini’s Visible and Infrared Mapping Spectrometer data. FLEX lossless compression provides a factor of 2 improvement over the original compression. We also explore a different range of lossy compression, which can achieve an additional factor 2 to 5 depending on the fidelity required. Our findings have implications for the design of future space missions, particularly with respect to antenna size and overall size, weight, and power budgets, by demonstrating strategies to implement better data compression. In addition to improved algorithms, we show that an iterative process involving real-time science team evaluation and feedback to update the onboard compression algorithm is both essential and feasible. We make the case that a spacecraft facility compressor hosting a toolbox of compression algorithms, available to all of the science instruments and supported by a team of compression experts, convey significant benefits. Beyond the obvious benefits of increased science return and faster playback, better data compression enables design trades between antenna size and number of science instruments on the payload.
The Deep Space Optical Communication (DSOC) project will demonstrate free-space optical communication at almost 3 AU, or 3 orders of magnitude further than any previous attempt. DSOC will utilize the 5m Palomar Hale Telescope to receive the downlink signal, which will couple the downlink light onto an optical table and into a superconducting nanowire single photon detector (SNSPD). The output of the SNSPD is digitized by the Ground Laser Receiver Signal Processing Assembly (GSPA) using a high throughput streaming time to digital converter (TDC). The GSPA is a scalable FPGA-based receiver which demodulates and decodes the DSOC downlink signal through novel signal processing algorithms implemented on Xilinx UltraScale+ FPGAs, as well as Python-based software monitor and control routines. Exploiting the unique TDC-based architecture, the GSPA supports over four orders of magnitude of downlink data rates across multiple orders of magnitude of signal and background powers. In this paper we present an overview of the hardware, firmware and software architectures to implement this system, as well as performance analysis for links ranging from near-Earth to 2.8 AU.
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