  It is widely known that spoofing is a major threat that adversely impacts the
reliability and accuracy of GNSS applications. In this study, a crowdsourcing
double differential pseudorange spatial (D2SP) random set is constructed and
the distribution of the set is derived.Based on the variance of the D2SP set, a
tri-level hypothesis detection algorithm is designed to classify spoofing-free,
fully-spoofed, and partially-spoofed cases in the region of interest (ROI).It
does not require the prior knowledge of the truth positions or relative
distances of the receivers.Simulation test results show that the proposed D2SP
spoofing detection method has the advantages of lower computational complexity
and higher tolerance for multipath errors compared with the generalized
likelihood ratio test (GLRT) method that is the current mainstream spoofing
detection algorithm based on multiple receivers' differential
pseudoranges.Moreover, it also shows better flexibility for different sizes of
ROI and numbers of the crowdsourcing receivers.
