scholarly journals The development of non-coherent passive radar techniques for space situational awareness with the Murchison Widefield Array

Author(s):  
Steve Prabu ◽  
Paul J. Hancock ◽  
Xiang Zhang ◽  
Steven J. Tingay

Abstract The number of active and non active satellites in Earth orbit has dramatically increased in recent decades, requiring the development of novel surveillance techniques to monitor and track them. In this paper, we build upon previous non-coherent passive radar space surveillance demonstrations undertaken using the Murchison Widefield Array (MWA). We develop the concept of the Dynamic Signal to Noise Ratio Spectrum (DSNRS) in order to isolate signals of interest (reflections of FM transmissions of objects in orbit) and efficiently differentiate them from direct path reception events. We detect and track Alouette-2, ALOS, UKube-1, the International Space Station, and Duchifat-1 in this manner. We also identified out-of-band transmissions from Duchifat-1 and UKube-1 using these techniques, demonstrating the MWA’s capability to look for spurious transmissions from satellites. We identify an offset from the locations predicted by the cataloged orbital parameters for some of the satellites, demonstrating the potential of using MWA for satellite catalog maintenance. These results demonstrate the capability of the MWA for Space Situational Awareness and we describe future work in this area.

2011 ◽  
Author(s):  
John McInroy ◽  
Suresh Muknahallipatna ◽  
Margareta Stefanovic ◽  
Farhad Jafari

2019 ◽  
Vol 32 (23) ◽  
pp. 8087-8109 ◽  
Author(s):  
Mark D. Risser ◽  
Christopher J. Paciorek ◽  
Travis A. O’Brien ◽  
Michael F. Wehner ◽  
William D. Collins

Abstract The gridding of daily accumulated precipitation—especially extremes—from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates of long period return values and their changes based on such gridded daily datasets are generally underestimated. In this paper, we characterize high-resolution changes in observed extreme precipitation from 1950 to 2017 for the contiguous United States (CONUS) based on in situ measurements only. Our analysis utilizes spatial statistical methods that allow us to derive gridded estimates that do not smooth extreme daily measurements and are consistent with statistics from the original station data while increasing the resulting signal-to-noise ratio. Furthermore, we use a robust statistical technique to identify significant pointwise changes in the climatology of extreme precipitation while carefully controlling the rate of false positives. We present and discuss seasonal changes in the statistics of extreme precipitation: the largest and most spatially coherent pointwise changes are in fall (SON), with approximately 33% of CONUS exhibiting significant changes (in an absolute sense). Other seasons display very few meaningful pointwise changes (in either a relative or absolute sense), illustrating the difficulty in detecting pointwise changes in extreme precipitation based on in situ measurements. While our main result involves seasonal changes, we also present and discuss annual changes in the statistics of extreme precipitation. In this paper we only seek to detect changes over time and leave attribution of the underlying causes of these changes for future work.


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