# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
###### Abstract  
In order to effectively reduce the impact of Global Navigation Satellite System (GNSS) attacks while providing mobile terminals with credible navigation and positioning results, this paper proposes a credible navigation algorithm for GNSS attack detection using an auxiliary sensor system. Based on a credible Kalman filter and measurement information provided by the auxiliary sensor system on mobile terminals, the proposed algorithm can verify the credibility of the GNSS positioning result and determine whether it has suffered from a GNSS attack using the credible verification window and the credible verification threshold. According to the verification results, the algorithm can adaptively select an updated model for measurement correction and achieve a credible navigation result. The algorithm proposed in this paper has been verified on a self-developed mobile terminal, and the experimental results show that the algorithm can provide credible navigation and positioning services for mobile terminals in the context of GNSS attacks. c +
Footnote †: journal:easily detected. The repeater type of spoofing copies the received real signal, adds a delay, and forwards it again as a spoofing signal. This spoofing is mainly used for navigation signals whose interface files are not disclosed. However, in order to seize control, the power of the spoofing signal still needs to be further increased; moreover, this method also requires the interruption of the receiver to make it re-enter the capture state, which can be detected using the power detection method. Inducting spoofing is the only spoofing method that can seize control without changing the tracking state of the receiver. It is concealed and difficult to detect with conventional methods, such as power detection. In order to improve the credibility of mobile terminal navigation and positioning, different detection technologies are used for different types of GNSS attacks [9; 10; 11]. Today, the detection technologies for GNSS attacks can be divided into the following categories [12]: signal encryption authentication, signal feature detection and consistency verification with other navigation sensors. The signal encryption authentication technology [13; 14; 15] is used to encrypt the civil navigation signal or spreading code to deal with spoofing interference. However, for the method to work, it is required to change the signal system, add a reference receiver, and rely on other networks for collaborative detection. Signal feature detection [16; 17; 18; 19; 20] detects spoofing based on the amplitude, arrival time and arrival angle during the GNSS receiver's capture and tracking phase in order to determine the credibility of the GNSS signal. Compared to the previous one, this method does not need to change the signal system, but it requires upgrades to the baseband signal processing algorithm related to the GNSS receiver, or installation of anti-jamming modules, which are very expensive. The consistency verification of the mobile terminal combined with the auxiliary navigation sensors already installed on the mobile terminal can also effectively realize the detection of GNSS jamming and spoofing. Currently, the commonly seen mobile terminals that use GNSS signals, such as automobiles, aircraft, individual soldiers and unmanned terminals, are usually equipped with auxiliary navigation sensors [21] such as inertial measurement units (IMUs), odometers, etc. These auxiliary navigation sensors are non-radio sensors and will not be attacked by GNSS jamming signals or GNSS spoofing signals. These sensors can provide navigation and positioning information with good accuracy in a short time with high update rate. The position coordinates calculated by the auxiliary navigation sensors will be continuous, with no sudden interruption or signal jump. By effectively using the measurement information provided by the auxiliary navigation sensors to check the consistency of the GNSS signal, the mobile terminal is capable of detecting the involving jamming and spoofing attacks.