# Spoofing Detection Algorithm Based on Pseudorange Differences
## 1 Introduction
Spoofing interference can be simple or complex:  
Simplistic spoofing attacks and meaconing can be regarded as \"simple spoofing\". This \"simple spoofing\" is defined as a non-overlapped spoofing scenario. The main characteristic of this kind of spoofing is that the correlation peak of the spoofing pseudo-random noises (PRNs) is not overlapped with that of the authentic ones. This attack is usually generated by a hardware simulator or replayed by a signal transponder. An effective way to spoof a receiver in a non-overlapped scenario is to first jam and make the receiver lose its lock on the real signal and instead capture the spoofing signal [9]. In this scenario, the spoofing signal appears as a noise and only affects the effective carrier noise power ratio (C/No). For example, in the simulation scenario of the \"Beidou Open Laboratory test\" in Section 3, the signal is switched from the real to the spoofing signal with an absolute power advantage. Another effective way to spoof a receiver in a non-overlapped scenario is that the receiver enters an area that the satellite signal cannot cover, then the signal is generated through the simulator or replayed through the transponder to achieve spoofing. In this case, even if the value of C/No is lower than that of the real signal, the spoofing signal can still be captured by the receiver. In this scenario, the value of C/No is mainly affected by the transmitting power. The simulation scenario of the \"university test\" in Section 3 is an example of this case. The simulation scenarios of \"the Texas spoofing test battery\" used in this paper are more complex and can be regarded as \"complex spoofing\". The \"complex spoofing\" is defined as overlapped spoofing scenarios. In an overlapped spoofing attack, the correlation peak of the spoofing signals and the real signals overlap, and this interaction misshapes the correlation peak. This kind of spoofing attack is generated by a receiver-based spoofing generator where the spoofer knows the current time, the observable satellites and the location and signal parameters of the target receiver. The real signal is separated from the composite signal (which is the overlap between the real signal and the spoofing signal) by a small power advantage to realize the signal switch. This kind of spoofing is harder to detect. For simple spoofing, some related methods in [1] are valid. However, for complex spoofing, these methods become invalid. Current research focuses on using the antenna array method [10], the receiver pseudorange or carrier phase difference [11; 12], the correlation method [13; 14], the inertial aided method [15; 16] or the hypothesis testing method [17; 18]. The antenna array method needs more than one antenna. Its detection performance is affected by the baseline length between the antennas. When there is only one single receiver or one single antenna, such a difference method cannot be used. The inertial aided method has some disadvantages. First, inertial devices are needed, and this increases the cost of the whole navigation system. Second, inertial navigation involves error accumulation over time. This makes it effective over a short period, but invalid over longer periods. There are also some studies on the hardware of the receiver needed to detect the signal [19; 20]. All hardware-based methods require a change of the structure of existing receivers. Signal encryption is also a scheme to avoid spoofing interference, which is analyzed in [21]. However, the implementation of this solution requires a comprehensive and systematic modification from satellite to receiver, which is not feasible in a short time. If users want to use the signal, permission is needed from the operator. Furthermore, as meaconing does not change the signal structure, the scheme of signal encryption cannot effectively suppress meaconing. In addition, some crossing methods are proposed. In Ref. [22], the authors proposed a method to monitor the spoofing signal based on machine learning and signal processing.