# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 1 Introduction  
The security of global navigation satellite system (GNSS) has caught more and more public attention because of the ever-increasing reliance on GNSS in our lives. GNSS receivers are vulnerable to jamming and spoofing attacks because the power of received GNSS signals is very low and the details of civilian signals are open to the public [1; 2]. Spoofing signals can lead to wrong position, velocity and time (PVT) results. A victim receiver may output results preassigned by a spoofer so that the motion of the vehicle equipped with the receiver will be misled [3]. Many spoofing tests have been carried out over the last few years. The first GNSS spoofing experiment was presented by [PERSON] and [PERSON] using a GS720 satellite simulator [4]. In the experiment, a spoofer was mounted on a truck, and the counterfeit signals were effective when the distance between the spoofer and the victim receiver did not exceed 30 feet. A portable Global Positioning System (GPS) civilian spoofer was developed by researchers at the University of Texas at Austin [3]. Spoofing threat was assessed with the device, and a successful spoofing experiment was carried out on a super yacht [5]. The researchers also did a successful spoofing test on phasor measurement units (PMU), which are employed in time synchronization of the power system [6]. It is obvious that the GNSS spoofing attack is not far from reality. Hence, efficient spoofing countermeasures need to be developed. Many anti-spoofing techniques have been developed because of the importance of GNSS security. Some of them require additional hardware or changes to the interface specification. Techniques based on multiple antennas, absolute power measurements and inertial navigation system (INS) perform spoofing detection with measurements from additional sensors [7; 8; 9]. Cross-correlation techniques detect spoofing attack by performing cross-correlation of encrypted signals between secure and defended receivers [10]. Cryptographic methods, such as spread spectrum security code (SSSC) [11] and navigation message authentication (NMA) [12], attempt to prevent spoofing attacks by signal encryption or designing new GNSS signals, which are difficult to simulate. However, these methods are, to some extent, vulnerable to the replay attacks, in which the spoofer estimates, manipulates and replays the cryptographically-secured GNSS signals in real time [13; 14]. The above-mentioned countermeasures require additional hardware, which increases the expense. Hence, their applications are limited. Other countermeasures are applied in single antenna receivers by detecting the abnormality and inconsistency of measurements. For example, signal quality monitoring (SQM) techniques detect the distortion of correlation results in the tracking stage [15]. Receiver autonomous integrity monitoring (RAIM) checks the consistency between pseudorange measurements of different satellites [16]. It fails when all channels are taken over by spoofing signals [17]. Moving antenna techniques detect and identify spoofing signals with the correlation between measurements of pairwise signals. It is desirable to be implemented in a moving single antenna handset [18; 19; 20]. The multiple tracking technique tracks all of the signals that are over the threshold in the acquisition stage and attempts to distinguish the authentic measurements from spoofing ones and recover the navigation solution [21]. It only works when the code phase difference between the authentic and spoofing signals is more than one code chip [22]. Most of the spoofing countermeasures mentioned above process signals separately, which limits their performance since the associated information between different signals is ignored. The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing [23].