A Slowly Varying Spoofing Algorithm Avoiding Tightly-Coupled GNSS/IMU With Multiple Anti-Spoofing Techniques

Abstract

Vehicle and airborne navigation users are facing more and more complex signal interference and even spoofing. If vehicles and aircraft do not strengthen their anti-spoofing ability, their navigation performance is bound to be adversely affected. global navigation satellite system (GNSS) spoofing technology has gradually become a preferred interference method for spoofer because of its high concealment and great harm. For spoofer, user terminal increasingly adopts GNSS with inertial measurement unit (IMU): tightly-coupled GNSS/IMU, on this basis, user also configures a variety of anti-spoofing techniques to effectively deal with spoofing. Even if spoofer slowly changes user's positioning, if spoofing strategy is unreasonable, it will lead to the violation of parameter rationality of coupled filter output parameters and spoofing observation, which greatly increases the difficulty of spoofing. In view of the above problems, from the perspective of spoofer, in order to effectively counter the non cooperative target of assembling tightly-coupled GNSS/IMU by using GNSS spoofing, this paper establishes GNSS spoofing mathematical model, and proposes a slowly varying spoofing algorithm to avoid tightlycoupled GNSS/IMU with multiple anti-spoofing techniques based on the analysis of the influence mechanism of spoofing on the positioning of tightly-coupled GNSS/IMU, the algorithm proposes a measurement deviation determination method to avoid a variety of anti-spoofing techniques, which can gradually pull the positioning results of coupled system, and successfully avoid anti-spoofing techniques detection of least squares residual receiver autonomous integrity monitoring (RAIM) and parameter rationality check. The experimental results show that the algorithm can gradually change positioning of tightly-coupled GNSS/IMU within 30 s, and the north, east and down displacements basically achieve the spoofing effect, the errors with the expected offset are −0.5 m, 1.9 m and 12.7 m respectively. At the same time, the detection of the above anti-spoofing techniques is avoided. The mean value of test statistics for tightly-coupled system is reduced by 75.4% and does not exceed the alarm threshold, so as to achieve the purpose of spoofing, the effectiveness and high concealment of the spoofing algorithm are proved.

A Comprehensive Evaluation on the Impact of Various Spoofing Scenarios on GPS Sensors in a Low-Cost UAV

“…Gao et al [6] have developed a forward spoofing-based mathematical model for tightly coupled GNSS receivers. The authors have considered the parameters from UAV's current state to deviate it gradually.…”

“…2: System Architecture for GPS Spoofing generate spoof signals for location manipulation through generative spoofing. Generative spoofing involves spoof signal generation with the same structure as authentic GPS signals [6]. Additionally, various physical settings, like indoor and outdoor conditions, are considered to understand the spoof signal efficiency.…”

“…UAVs use path planning algorithms for autonomous operations, which rely on Global Navigation Satellite System (GNSS) for functionality. The real-time location enables UAVs to update their path plans and avoid obstacles, enabling navigation through a complex environment [6] [7]. Typically, low-cost UAVs utilize the L1 frequency band of the Global Positioning System (GPS) for navigation and location ascertainment [3].…”

A Novel Robust Iterated CKF for GNSS/SINS Integrated Navigation Applications

“…( 1), the detailed representation of solution in Eqs. (2)(3)(4) is difficult to build via the conventional method. To solve the above issue, the posterior PDF is approximated using MAP approach and linear minimization approach in references [10,20], which can obtain effective estimation.…”

“…GNSS signal is vulnerable to instantaneous interference and attenuation, and inertial instruments of SINS are susceptible to drift and noise, which can accumulate errors rapidly over time [1]. The complementing qualities of the two systems have led to a widespread adoption of their integrated system in the navigation field [2,3]. For the tightly coupled GNSS/SINS integrated navigation applications, the parameter estimation is usually specified as a nonlinear filtering process considering multiple information fusion owing to the nonlinearity of error equations For the above nonlinear model, Extended Kalman Filter (EKF) extends the original Kalman filter to support non-linear models by linearizing the system model at the latest estimation, which has a wide range of applications in the navigation field [4][5][6].…”