NOAA-Passive Radiometer Interference Management for Earth-Observing Systems (N-PRIME)

Anthropogenic radio frequency interference (RFI) has rapidly increased over the past decade, threatening the quality of geophysical data retrieved from microwave radiometry missions. The transmitting sources that produce RFI in microwave radiometry have distinct modulations, temporal characteristics, frequency extents, polarization, and many other properties, making a “one size fits all” solution for RFI management impractical.

The NOAA DMiPs program (“Examining Approaches and Methodologies for the Detection, Characterizing, and Mitigation of Passive Sensor Data Corrupting Emissions”) has a primary objective of “identification of passive band interfering or corrupting emissions, characterizations of those interfering or corrupting emissions, reduction of the impact of those interfering or corrupting emissions, and associated risks, processes and modifications needed to implement this capability on an international basis.”

We will present the NOAA-Passive Radiometer Interference Management for Earth-Observing Systems (N-PRIME) study under the DMiPS program, in which we apply an end-to-end requirement-based system engineering process first developed for NASA’s Aquarius and Soil Moisture Active Passive (SMAP) missions to design robust RFI management strategies for future radiometry missions. End-to-end (Phase A to E) implementation of RFI management strategies for space-borne radiometry is critical to completely tackle the growing challenge of RFI. Insufficient planning and implementation early on can degrade post-launch RFI management significantly. Similarly, lack of a post-launch mission structure for RFI management can make any early planning activities ineffective. The SMAP mission clearly demonstrates the need for, and beneficial impact of, robust end-to-end RFI management.

Key aspects of RFI management include tailoring and enhancing machine learning RFI filtering algorithmic approaches and creating a mission structure for reporting RFI to appropriate national and international regulatory and enforcement authorities.

The study’s activities are divided into two tasks:

(1) RFI management approach development: Expanded machine-learning methods for detecting the presence of corrupting RFI and for removing its contributions will be presented. Due to the range of source properties expected, we compare and contrast the performance of multiple pre- and post-detection training algorithms for a variety of known and expected RFI source types that will be simulated. The performance will be standardized to offer a qualitative assessment of the ML algorithms compared to traditional algorithms.

(2) Improving the RFI environment: Recommendations for the structure of an RFI operations team that will actively identify and report RFI post-launch will be presented and discussed.

The aim of this study is to address and summarize future RFI management needs for passive radiometry missions. We will assess the practicality, performance, usability, and resource utilization of the algorithmic and management approaches, as well as their impact on science products of interest. We will present the goals of the project and initial work undertaken to achieve those goals.

This research is partially carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. © 2024. California Institute of Technology. Government sponsorship acknowledged.