Digital Payload Modeling for Space Applications

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Shona Floyd
5 years ago
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Up until even the recent past, these payloads have been fixed band plan, implemented with all analog electronics.

2 Overview In recent years in the commercial or government space businesses, there has been a movement away from RF analog payloads for pure bent-pipe, or transponder applications. Up until even the recent past, these payloads have been fixed band plan, implemented with all analog electronics. Today s customers are looking towards all digital payloads that utilize digital signal processing (DSP) to accomplish the same goals while providing enhanced mission capability and flexibility. Bent Pipe transponder satellite at Geostationary Orbit (35,786 km) Location A Location B 2

3 The Challenge Design a Modular Agile digital Payload (MAP) to take the place of the traditional analog communications satellite payload. The payload must have the following characteristics: Be spaceworthy; i.e., highly reliable, and highly tolerant of radiation effects. Be reconfigurable; that is, able to be altered on-orbit for different functions as business conditions change. Be highly flexible and agile with respect to bandplan and channelization, so that bandwidth for users can be assigned ondemand, and spectrum can be arbitrarily allocated anywhere within the full bandwidth of a beam. Be modular, and re-useable for many different programs, all with different requirements. 3

4 The Early Days Early on in the program, it was a complete unknown what this system would look like. Requirements from numerous programs were flying around, and they were often incompatible with each other, particular their bandplans: AMC: 4, 8, 36, and 72 wide channels. MMSI: 24 and 32 wide channels. JSAT: 27 wide channels. AsiaSAT: 8 and 36 MHZ wide channels. and many others These unknown requirements not only made it very difficult to design the hardware, but put a tremendous amount of work on the DSP engineer designing algorithms. As the lead DSP engineer, I found myself re-writing the algorithms almost daily, in order to demonstrate the various bandplans. 4

5 The Hardware Solution Digital Channelizer Unit (DCU): A modular, space-worthy payload composed of the following building blocks: CIO Channelizer I/O Module Composed of 5 Field Programmable Gate Arrays (FPGAs), three of which are voted for extremely high reliability. Handles all of the signal processing, real-time. DRM Data Routing Module Multi-gigabit, non-blocking data routing in rad tolerant FPGAs. RIO RF Input/Output Modules Power Converters 5

System Level DCU Architecture Each input port (RIO): 125 anti alias filter RF power optimized for ADC RF SPARE Beam 4 Beam 3 Beam 2 Beam 1 RF SPARE RF CIO modules: Digitize

to RF conversion SPARE Dig DAC RF RIO modules: Reconstruction Filtering SPARE Beam 4 Beam 3 Beam 2 Beam 1 RF BCA Control EPC & Intermediate Power SCB Controller Serial

6 System Level DCU Architecture Each input port (RIO): 125 anti alias filter RF power optimized for ADC RF SPARE Beam 4 Beam 3 Beam 2 Beam 1 RF SPARE RF CIO modules: Digitize Spectrum Divide Channels SPARE ADC Dig Dig DRM module: Multi-Gigabit Routing SPARE Dig CIO modules: Reconstruct Spectrum Dig. to RF conversion SPARE Dig DAC RF RIO modules: Reconstruction Filtering SPARE Beam 4 Beam 3 Beam 2 Beam 1 RF BCA Control EPC & Intermediate Power SCB Controller Serial Command Bus Frequency Generation 5 for 4 Processing Modules 2 For 1 Router/Controllers Spacecraft TT&C Bus Spacecraft RIU MIL-STD 1553 B RIO RF Input/Output Module CIO Channelizer Input/Output Module DRM Data Routing Module 6

7 DCU on MAP Spacecraft MAP type Spacecraft Active Arrays Channelization in CIO Digital Channelizer Unit (DCU) CIO DRM 7

8 DCU Algorithm Algorithm Overview The DCU algorithm is defined as the mathematical manipulation (via fixed point arithmetic implemented in the DCU FPGAs) of data samples to perform the following tasks: Process a total digitized analog frequency bandwidth of 5 (which may or may not be contiguous). Break up this bandwidth into narrowband subchannels (i.e., channelize ) Have the ability to re-arrange the subchannels in any arbitrary order. Have the ability to alter the subchannel gains arbitrarily, in multiple m modes. Have the ability to measure and report the average and peak signal power level at the input and output of the DCU, as well as at the subchannel level. Recombine the subchannels (i.e., "reconstruct" them) into a composite osite bandwidth of the same size as the input. Transform the recombined spectrum so that it can be converted back into the analog domain for further processing. Be arbitrarily reconfigurable in signal processing function. The functional data flow for a 4 port DCU is depicted here: Port Port 1 Port 2 Port 3 ADC Channelize ADC Channelize ADC Channelize ADC Channelize Route Reconstuct DAC Reconstuct DAC Reconstuct DAC Reconstuct DAC Port Port 1 Port 2 Port 3 8

9 subch, port -2 2 subch 6, port -2 2 subch 12, port -2 2 subch 18, port -2 2 subch 24, port subch 1, port -2 2 subch 7, port -2 2 subch 13, port -2 2 subch 19, port -2 2 subch 25, port subch 2, port -2 2 subch 8, port -2 2 subch 14, port -2 2 subch 2, port -2 2 subch 26, port subch 3, port -2 2 subch 9, port -2 2 subch 15, port -2 2 subch 21, port subch 4, port -2 2 subch 1, port -2 2 subch 16, port -2 2 subch 22, port subch 5, port -2 2 subch 11, port -2 2 subch 17, port -2 2 subch 23, port -2 2 DCU Algorithm Matlab Algorithmic Model A floating point model of the DCU algorithm has been developed in the Matlab environment. Amplitude () Input IF Two - Sided Input Signal Spectrum After Sampling (Possibly Aliased Spectrum) Amplitude () Preprocess Input Signal Spectrum After Quarter Band Filtering This model is designed to be very flexible, allowing the user to specify a wide range of arguments. This makes the Matlab model useful for functionally verifying various channelization schemes quickly and efficiently. The user simply needs to pick the right parameters (filter coefficients, FFT length, decimation/interpolation factors), etc Frequency () Channelize Frequency () Route, Route, Gain Gain routing_table, gain_table The DCU algorithm function can be called from the Matlab command line, or as part of a script file. Amplitude () Reconstruct Reconstructed Function Output Amplitude () Output IF Passband Output Spectrum This function is composed of lower level functions that do arbitrary channelization and reconstruction Frequency () Frequency () 9

DCU Algorithm Simulink Structural Model The next step in the development

assumptions about the DCU Algorithm and provide a model that is very

Once the Simulink model works properly, another Simulink model is developed

10 DCU Algorithm Simulink Structural Model The next step in the development was to build a structural model of the DCU algorithm in the Simulink environment. Simulink Block Diagram This model is designed to test implementation assumptions about the DCU Algorithm and provide a model that is very similar to what the real gateware will look like. Once the Simulink model works properly, another Simulink model is developed by replacing the blocks with components from another vendor s blockset in order to generate VHDL. Simulink to Hardware Development Simulink Model FPGA SynDSP Blockset Model HDL Synthesis 1

DCU Algorithm Model Verification Once VHDL for the various pieces of the

and stitched together in the Mentor HDL environment, the next step is

For this task, we choose the EDA Simulator Link MQ tool (formerly known

This tool allows us to make use of all of the signal generation and

still simulating the VHDL at the bit level.

verification that we used during the development phase.

11 DCU Algorithm Model Verification Once VHDL for the various pieces of the DCU algorithm have been developed, tested in the Simulink environment, and stitched together in the Mentor HDL environment, the next step is functional simulation and verification. For this task, we choose the EDA Simulator Link MQ tool (formerly known as Link for Modelsim ). This tool allows us to make use of all of the signal generation and visualization tools available to us in the Simulink environment, while still simulating the VHDL at the bit level. In fact, we can use the exact same test-benches for VHDL test and verification that we used during the development phase. This tool was immensely helpful in finding bugs in the code that were not apparent in the Simulink environment alone. Pull the generated code together with HDL Designer Bring into Simulink Environment Simulate with ModelSim 11

12 Conclusion Using advanced tools and methods, we took a program from concept through functional, space-worthy hardware in about 1½ years. Estimated reduction in development time 8 months over traditional (hand coding) methods. Process flow and tools: Concept Matlab (Signal Processing Toolbox) Structural Model Simulink (Signal Processing and Communications Blockset) VHDL Model SynDSP Blockset in Simulink Environment VHDL Code Integration HDL Designer Functional Verification and Degugging - EDA Simulator Link MQ tool in Simulink Environment 12

STRS COMPLIANT FPGA WAVEFORM DEVELOPMENT

STRS COMPLIANT FPGA WAVEFORM DEVELOPMENT Jennifer Nappier (Jennifer.M.Nappier@nasa.gov); Joseph Downey (Joseph.A.Downey@nasa.gov); NASA Glenn Research Center, Cleveland, Ohio, United States Dale Mortensen