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Domain Analysis of Radio Frequency Interference Detection Techniques for SMOS

College of Engineering Department of Atmospheric, Oceanic & Space Sciences. Domain Analysis of Radio Frequency Interference Detection Techniques for SMOS. -Sidharth Misra and Christopher Ruf University of Michigan, Ann Arbor. Outline. Introduction Detection domains and algorithms

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Domain Analysis of Radio Frequency Interference Detection Techniques for SMOS

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  1. College of Engineering Department of Atmospheric, Oceanic & Space Sciences Domain Analysis of Radio Frequency Interference Detection Techniques for SMOS -Sidharth Misra and Christopher Ruf University of Michigan, Ann Arbor

  2. Outline • Introduction • Detection domains and algorithms • Visibility domain • Tb spatial domain • Visibility  Tb spatial comparison • Advantage • Examples • Tb angular domain • Algorithm description • Pro’s and Cons • Tb spatial  Tb angular comparison • Advantage • Examples • Summary

  3. Introduction • Motivation: Develop parameters for Aquarius RFI detection • The SMOS data goes through a number of processing steps and RFI detection can be attempted at any of the intermediate domains • Visibility domain: Visibility vs. time (L1a data ) • Spatial domain: Brightness Temperature (Tb) vs. position • Angular domain: Tb vs. incidence angle • Polarimetric domain: Stokes (Tb) vs. natural max (Anomalous Stokes behavior) • The first three RFI detection algorithms are briefly discussed and compared here in terms of signal-to-noise ratio, false-alarm etc.

  4. Visibility Domain Detection Algorithm • Algorithm developed by E. Anterrieu • Based on using the zeroth visibility data (other visibilities also considered) • Algorithm performs a spline-fit of consecutive visibility samples • Any outlier detected based on deviation from the spline is tagged as RFI • Pros: • Detects RFI early in the signal processing flow • Has a lower NEDT noise level (~0.2K) compared to other domains • Can immediately identify and discard large sources that might be outside the alias-free zone • Is well suited for detecting continuous RFI sources • Can utilize the positive definite L2 norm property of the zeroth visibility RFI perturbations • Cons: • Spatially localized RFI has a lower signal amplitude in the visibility domain than it does in the Tb domain

  5. Tb Spatial Domain Detection Algorithms • Algorithm identifies ‘outliers’ or ‘spikes’ in the SMOS snapshot image • Involves a moving spatial averaging window that compares the Tb in the pixel under test with the average of neighboring pixels • RFI is flagged if the pixel Tb deviates from its neighboring values by a threshold value related to the NEDT • Pros: • Signal-to-Noise level increases relative to Visibility domain • Cons: • Natural geophysical variability within the averaging window can cause RFI false alarms • RFI can have a negative bias as well as positive bias due to phenomenon such as ringing

  6. Visibility  Tb spatial comparison • Samples in the Tb domain are N1/2 times noisier than in the L1a domain, where N is the number of receiver pairs • The signal amplification factor from the L1a to the Tb spatial domain is N • Thus the overall increase in SNR from the L1a to the Tb domain is N1/2 • Minimum detectable RFI level (assuming SNR = 3) • L1a domain can detect RFI above V0 = 0.6K • Corresponds to Tb = 1560K • Tb domain can detect RFI above Tb = 15K • Corresponds to V0 = 0.005K

  7. Tb Angular Domain Algorithm • Takes advantage of the fact that SMOS measures a single grid point over multiple incidence angles • Tb has a characteristic dependence on incidence angle • Algorithm description: • Assemble Tb values at one location vs. incidence angles • If the number of samples is large enough (>10), then a cubic fit is performed on the incidence angle dependence • The cubic fit is only done for samples within a physically reasonable range (e.g. <320K) • The sample under test is compared to the best fit line • Deviations from the best fit line which are above or below a certain threshold (3sigma) are flagged as RFI

  8. Tb Angular Domain Detection Algorithm, cont. • Pros: • Takes advantage of the fact that there is a coherent relationship between the sample under test and it’s neighboring samples at other incidence angles • A more accurate prediction can be made of the sample under test (fit vs. spatial mean) in the angular domain • Is not influenced by spatial variability of neighboring pixels, since it is fixed at one grid location • Is well suited to identification of time varying RFI • Cons: • Cubic fit can be corrupted if most samples (vs. incidence angle) are corrupted by RFI

  9. Tb Spatial vs. Tb Angular Domain RFI Detection Example #2 – Positive RFI Detection in Angular Domain

  10. Tb Spatial vs. Tb Angular Domain RFI Detection Example #3 – Relative insensitivity to False Alarms over ocean in the angular domain Spatial domain Angular domain Island not falsely detected as RFI by Tb Angular algorithm

  11. Tb Spatial vs. Tb Angular Domain RFI Detection Example #4 – Tb Angular domain can differentiate between RFI positives and RFI false alarms Spatial domain

  12. Tb Spatial vs. Tb Angular Domain RFI Detection Example #5 – Relative insensitivity to False Alarms over land in the angular domain Angular domain Spatial domain Lake not confused as RFI by Tb Angular algorithm

  13. Tb Spatial vs. Tb Angular Domain RFI Detection Example #6 – “Negative” RFI 235K RFI

  14. Tb Angular Domain: RFI snapshot

  15. Summary • Algorithms in three domains were compared and contrasted • The visibility domain algorithm is very good at detecting RFI early in the processing pipeline, but at the cost of SNR (detectability) • The spatial domain algorithm though has a relatively superior SNR, can suffer from image heterogeneity for low-level RFI • The angular domain algorithm is independent of such heterogeneity and takes advantage of the smooth geophysical relationship between Tb and incidence angle • The angular domain algorithm fit can suffer due to a high amount of RFI present • Lack of “ground-truth” makes independent and comparative assessment of the performance of each algorithm difficult

  16. References • Y. H. Kerr, P. Waldteufel, J. P. Wigneron, S. Delwart, F. Cabot, J. Boutin, M. J. Escorihuela, J. Font, N. Reul, C. Gruhier, S. E. Juglea, M. R. Drinkwater, A. Hahne, M. Martin-Neira, and S. Mecklenburg, "The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle," Proceedings of the IEEE, vol. 98, pp. 666-687, 2010. • J. E. Balling, S. S. Sobjaerg, S. S. Kristensen, and N. Skou, "RFI and SMOS: Preparatory campaigns and first observations from space," 2010 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, pp. 282-287, 2010. • A. Camps, J. Gourrion, J. M. Tarongi, A. Gutierrez, J. Barbosa, and R. Castro, "RFI analysis in SMOS imagery," in IEEE Geoscience and Remote Sensing Symposium (IGARSS), Proceedings, 2010, pp. 2007-2010. • H. M. J. Barre, B. Duesmann, and Y. H. Kerr, "SMOS: The Mission and the System," Geoscience and Remote Sensing, IEEE Transactions on, vol. 46, pp. 587-593, 2008. • E. Anterrieu, "On the detection and quantification of RFI in L1a signals provided by SMOS," Geoscience and Remote Sensing, IEEE Transactions on, vol. PP, (99), pp. 1-7 (IEEE Early Access), 2011. • L. Li, E. G. Njoku, E. Im, P. S. Chang, and K. S. Germain, "A preliminary survey of radio-frequency interference over the U.S. in Aqua AMSR-E data," Geoscience and Remote Sensing, IEEE Transactions on, vol. 42, pp. 380-390, 2004. • N. Skou, J. E. Balling, S. S. Sobjarg, and S. S. Kristensen, "Surveys and analysis of RFI in the SMOS context," in IEEE Geoscience and Remote Sensing Symposium (IGARSS), Proceedings, 2010, pp. 2011-2014. • K. Huang and S. Aviyente, "Sparse representation for signal classification," Advances in Neural Information Processing Systems, 2006.

  17. Back-up Slides

  18. Visibility vs. Tb Spatial Domain RFI Detection Example #1 SM_OPER_MIR_SCLF1C_20100708T101059_20100708T110500_344_001_1 – snapshot id = 35740243 An 1800K RFI point source = 0.69K in the L1a domain, which is just above the detection threshold of the L1a algorithm and will be detected by both algorithms

  19. Visibility vs. Tb Spatial Domain RFI Detection Example #2 SM_OPER_MIR_SCLF1C_20100708T101059_20100708T110500_344_001_1 – snapshot id = 35735541 A 450K RFI point source = 0.17K in the L1a domain, which is below the detection threshold of the L1a algorithm

  20. Tb Spatial Domain Detection Algorithm Example #1: Indistinct RFI spot

  21. Tb Spatial vs. Tb Angular Domain RFI Detection Example #4 – Positive RFI Detection in Both Domains Angular domain Spatial domain 450 K RFI spot, detectable by both algorithms

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