Algorithm Enhancements for the SS-411 Digital Sun Sensor

Transcription

1 Algorithm Enhancements for the SS-411 Digital Sun Sensor John Enright Space Avionics & Instrumentation Laboratory Dept. of Aerospace Engineering Ryerson University Doug Sinclair Sinclair Interplanetary AIAA/USU Conference on Small Satellites August 2007 Logan, Utah Dept. of Aerospace Engineering Ryerson University 1

2 Sensor History SS-256 SS-330 SS-411 First production Number on-orbit Height 22 mm 17 mm 21 mm Front window Glass Sapphire Sapphire # of optical elements CPU clock 8 MHz 24.5 MHz 49 MHz RAM kb 0.75 kb 2.25 kb ROM 7.5 kb 7.5 kb 31.5 kb Digital two-axis sun sensor Inherent albedo rejection Established design heritage Design features High-end COTS components Integrated microcontroller Dept. of Aerospace Engineering Ryerson University 2

3 Theory of Operation z y h Embedded algorithms used for estimation x 2D x 2D y Pixel Array Sensor CPU Sun Vector Estimate ~ s0 Sensor Electronics Optical Mask & Filter Array Readout (2 sample images shown) Dept. of Aerospace Engineering Ryerson University 3

4 Algorithms & Optimizations Processing Steps NLSQ Algorithm Embedded Migration Image Segmentation Matlab Image NLSQ Fitting Geometry Model Sun Vector Standalone C C-Style Matlab Coarse peak localization & sorting Heuristic based Non-Linear Least Squares Fit (Iterative) Calculate sun-vector from calibrated parameters Each iteration, must solve this system: 256 x 6 Matrix ( T ) T Q Q dλ = Q dβ C w/ MEX Wrapper C8051 Dev-Kit SS411 Sensor General principles Exploit fixed-point where possible No transcendental functions Hand-optimized floating point library NLSQ optimizations Exploit sparseness Reorder operations In-place computation Clever exponential model for peak fitting LU-decomposition Embedded migration Exploit Matlab heritage Careful regression-testing Dept. of Aerospace Engineering Ryerson University 4 Sun Vector

5 Lab Facilities & Calibration Space Avionics and Instrumentation Laboratory (SAIL) equipped to calibrate and test ACS sensors. Sun simulator lamp Motorized test platform (.002, 1-σ) Calibration procedure used to determine model constants ~400 sample points in concentric rings, evenly spaced Sensor constants (e.g. manufacturing, component tolerances) 5 - Parameters Lab constants (e.g. mounting geometry) 7 Parameters Simplex search to find parameter assignments minimizing MSE. Dept. of Aerospace Engineering Ryerson University 5

6 Initial Performance Results Sensor Error Computational Benchmarks Algorithm Step Time (ms) ROM (Bytes) Image Segmentation Pretty good calibration fit Residual Error: 0.12 (RMS) Computational resources remain modest Memory acceptable (7 kb ROM, 434 Bytes RAM) CPU time fast enough for >5 Hz operation Evidence of uncorrected systematic error. Horizontal banding NLSQ (1 Iteration) Geometric Model Total* *Assumes 10 NLSQ iterations Dept. of Aerospace Engineering Ryerson University 6

7 Systematic Errors Structure clearly present in error: Ideal Detector (initial model) Estimation Error (pixels) Revised Data Sheet Array Position (pixels) Actual Array Evaluated alternative models Pixel Width Refractive index Multi-layer refraction Platform-induced distortion Wedge effects Then we looked at the detector. Two, 128 element arrays Longitudinal and transverse misalignment Dept. of Aerospace Engineering Ryerson University 7

8 Final Performance Augmented model corrects for X/Y apparent gap, refractive index (3 parameters) Before Gap Correction After Gap Correction Substantial error reduction From 0.12 to 0.08 (RMS) Reduction in structure, but solution is not perfect Some implications for calibration fit PPE step is now part of the cost function evaluation. Increased (offline) computation time (now about 0.5 hr) Dept. of Aerospace Engineering Ryerson University 8

9 Conclusions Excellent Sensor Performance Accuracy (0.08 RMS) Some residual systematic error, but effect are not pronounced Further refinements to SS-411 possible, but additional accuracy of questionable utility. Low-accuracy regions near outer edge of FOV. Speed ( > 5 Hz) Can be increased by changing maximum NLSQ iterations Smart sensors add extra capabilities Plug-in replaceable (internal calibration constants) Leverage capabilities of COTS processors Automatic calibration exceedingly helpful Data collection Simplifies manufacturing Simplifies lab-setup Good models are essential Fitting process can obscure exactly what s going on Dept. of Aerospace Engineering Ryerson University 9