Computationally Efficient Radio Frequency Source Localization for Radio Interferometric Arrays

Abstract

Radio frequency interference (RFI) is an ever‐increasing problem for remote sensing and radio astronomy, with radio telescope arrays especially vulnerable to RFI. Localizing the RFI source is the first step to dealing with the culprit system. In this paper, a new localization algorithm for interferometric arrays with low array beam sidelobes is presented. The algorithm has been adapted to work both in the near field and far field (only the direction of arrival can be recovered when the source is in the far field). In the near field the computational complexity of the algorithm is linear with search grid size compared to cubic scaling of the state‐of‐the‐art 3‐D MUltiple SIgnal Classification (MUSIC) method. The new method is as accurate as 3‐D MUSIC. The trade‐off is that the proposed algorithm requires a once‐off a priori calculation and storing of weighting matrices. The accuracy of the algorithm is validated using data generated by low‐frequency array while a hexacopter was flying around it and broadcasting a continuous‐wave signal. For the flight, the mean distance between the differential GPS positions and the corresponding estimated positions of the hexacopter is 2 m at a wavelength of 6.7 m.

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

“…Research in the fields of artificial intelligence, big data, and the Internet of Things has been widely used in human life and production, creating enormous wealth. In recent years, many positioning technologies developed as the IoT system have been proposed, such as Radio Frequency [1], [2], Infrared [3], Ultrasound [4]- [8], Optical, and Magnetic Field Strength [9]- [12] with the relevant positioning calculation mechanism. This kind of positioning mainly includes two application scenarios.…”