Domain Analysis of Radio Frequency Interference Detection Techniques for SMOS -Sidharth Misra and Christopher Ruf University of Michigan, Ann Arbor College of Engineering Department of Atmospheric, Oceanic & Space Sciences
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
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
Spatially localized RFI has a lower signal amplitude in the visibility domain than it does in the Tb domain
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
Signal-to-Noise level increases relative to Visibility domain
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
Visibility Tb spatial comparison
Samples in the Tb domain are N 1/2 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 N 1/2
Minimum detectable RFI level (assuming SNR = 3)
L1a domain can detect RFI above V 0 = 0.6K
Corresponds to Tb = 1560K
Tb domain can detect RFI above Tb = 15K
Corresponds to V 0 = 0.005K
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
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
Tb Angular Domain Detection Algorithm, cont.
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
Cubic fit can be corrupted if most samples (vs. incidence angle) are corrupted by RFI
Tb Spatial vs. Tb Angular Domain RFI Detection Example #2 – Positive RFI Detection in Angular Domain
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
Tb Spatial vs. Tb Angular Domain RFI Detection Example #4 – Tb Angular domain can differentiate between RFI positives and RFI false alarms Spatial domain
Tb Spatial vs. Tb Angular Domain RFI Detection Example #5 – Relative insensitivity to False Alarms over land in the angular domain Lake not confused as RFI by Tb Angular algorithm Spatial domain Angular domain
Tb Spatial vs. Tb Angular Domain RFI Detection Example #6 – “Negative” RFI 235K RFI
Tb Angular Domain: RFI snapshot
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
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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 Visibility vs. Tb Spatial Domain RFI Detection Example #1 SM_OPER_MIR_SCLF1C_20100708T101059_20100708T110500_344_001_1 – snapshot id = 35740243
A 450K RFI point source = 0.17K in the L1a domain, which is below the detection threshold of the L1a algorithm Visibility vs. Tb Spatial Domain RFI Detection Example #2 SM_OPER_MIR_SCLF1C_20100708T101059_20100708T110500_344_001_1 – snapshot id = 35735541