Improving Radio Frequency Interference Mitigation Strategies at the National Weather Radar Testbed
J. Lake1, C. Curtis2, and M. Yeary1 1Advanced Radar Research Center, Univ. of Oklahoma, Norman, OK 2National Severe Storms Lab, NOAA, Norman, OK
Mitigation strategies for radio frequency interference (RFI) will be needed for the Multi-function Phased Array Radar (MPAR) to successfully carry out its missions, which include meteorological surveillance and target detection and identification. The presidentially mandated sale of portions of the electromagnetic spectrum could subject radars to more stringent bandwidth requirements. Even without changes to bandwidth requirements, RFI has already presented itself at the National Weather Radar Testbed (NWRT) phased array radar; more stringent bandwidth requirements will only exacerbate the problem, jeopardizing the MPAR's ability to carry out its missions and thus its cost-effectiveness.
Fig. 1: The NWRT already experiences RFI, seen here as nonmeteorological blips. With stricter bandwidth requirements, RFI could become a ubiquitous problem.
Several strategies designed to cope with RFI have been developed. The three RFI mitigation algorithms outlined in the Vaisala User's Manual are all similar in form: they take sets of three consecutive pulses, compare the power (in decibels) of these pulses to user defined constraints, and use the result to determine if a pulse is corrupted by RFI. Upon detection of RFI, the corrupted entry is replaced by data from the preceding pulse. Another method, the Interference Spike Detection Algorithm (ISDA), allows users to specify both what data will be used to determine the presence of RFI and the threshold at which data is considered RFI. Tests on Gaussian noise with added artificial RFI indicate that ISDA outperforms the Vaisala algorithms under these conditions. Further evaluation of ISDA is needed.
RFI corrupted data available from the NWRT will be used to analyze and characterize pulsed interference in the weather radar spectrum, permitting more accurate simulation of RFI and a better understanding of its effects on weather radar. Changing which data is used by ISDA to find RFI could improve its efficacy and will give a better assessment of its performance relative to the Vaisala algorithms and other RFI mitigation techniques. The impact of introducing more sophisticated data substitution methods, rather than simply replacing corrupted data with data from previous pulses, will be explored. Inferior data substitution choices could have negative effects on clutter filtering algorithms or the phase coding algorithms, including Sachidananda-Zrnic phase coding (SZ2), an operationally used algorithm that mitigates range and velocity ambiguities. Other challenges, such as distinguishing RFI from ground clutter and expanding the algorithms to other radar platforms, will be addressed as needed through the research period.