KEYWORDS: Synthetic aperture radar, Field programmable gate arrays, Image processing, Image quality, Signal processing, Satellites, Data communications, MATLAB, Data storage, Space operations
Synthetic aperture radar (SAR) imagery requires image reproduction through the successive signal processing of received data before browsing images and extracting information. The received signal data records of ALOS-2/PALSAR-2 are stored in the onboard mission data storage and transmitted to the ground. To compensate for storage usage and transmission data capacity through the mission data communication network, the operation duty of PALSAR-2 is limited. This balance strongly relies on network availability. The observation operations of current spaceborne SAR systems are rigorously planned by simulating the mission data balance, given conflicting user demands. This problem should be solved so that we need not compromise the operations and potential of next-generation spaceborne SAR systems. One of the solutions is to compress the SAR data through onboard image reproduction and information extraction from the reproduced images. This is also beneficial for fast delivery of information products and event-driven observations by constellation. The Emergence Studio (Sōhatsu kōbō in Japanese) of the Japan Aerospace Exploration Agency has been developing evaluation models of the image processing system with the field-programmable gate array for onboard SAR image reproduction. The first model called the “Fast L1 Processor (FLIP)” can reproduce a 10-m resolution, single-look complex image (level 1.1) from ALOS/PALSAR raw signal data (level 1.0). FLIP processing speed at 200 MHz results in processing about five times faster than CPU-based computing at 3.7 GHz, corresponding to 50% real-time processing capability for ALOS/PALSAR. The FLIP’s image is almost identical to the image reproduced by 32-bit simulation in MATLAB.
KEYWORDS: Synthetic aperture radar, Image processing, Signal processing, Field programmable gate arrays, Image acquisition, Image resolution, Data storage, Remote sensing, Data processing, Data modeling
Synthetic Aperture Radar (SAR) imagery requires image reproduction through successive signal processing of received data before browsing images and extracting information. The received signal data records of the ALOS-2/PALSAR-2 are stored in the onboard mission data storage and transmitted to the ground. In order to compensate the storage usage and the capacity of transmission data through the mission date communication networks, the operation duty of the PALSAR-2 is limited. This balance strongly relies on the network availability. The observation operations of the present spaceborne SAR systems are rigorously planned by simulating the mission data balance, given conflicting user demands. This problem should be solved such that we do not have to compromise the operations and the potential of the next-generation spaceborne SAR systems. One of the solutions is to compress the SAR data through onboard image reproduction and information extraction from the reproduced images. This is also beneficial for fast delivery of information products and event-driven observations by constellation. The Emergence Studio (Sōhatsu kōbō in Japanese) with Japan Aerospace Exploration Agency is developing evaluation models of FPGA-based signal processing system for onboard SAR image reproduction. The model, namely, “Fast L1 Processor (FLIP)” developed in 2016 can reproduce a 10m-resolution single look complex image (Level 1.1) from ALOS/PALSAR raw signal data (Level 1.0). The processing speed of the FLIP at 200 MHz results in twice faster than CPU-based computing at 3.7 GHz. The image processed by the FLIP is no way inferior to the image processed with 32-bit computing in MATLAB.
Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.