## 1 Introduction  
The positioning-based applications available today that rely on satellite navigation systems, including the widely used Global Positioning System (GPS), Russian GLONASS, European GALILEO, Chinese BEIDOU, Japanese QZSS and Indian IRNSS, have become integral parts of various aspects of modern life, including transportation, agriculture, telecommunications, and emergency services. These systems depend on a collection of satellites orbiting the globe to provide accurate positioning, navigation, and timing information to users worldwide. However, they are susceptible to interference, which can disrupt the signals and compromise their reliability. Satellite navigation interference refers to deliberate or unintentional disruptions of signals between satellites and ground-based receivers. Interference can take various forms, including jamming, which is the deliberate transmission of strong radio signals on the same frequencies used by satellite navigation systems to overpower and disrupt genuine satellite signals. The jamming devices are sometimes used for illegal purposes, for example, the usage of cheap commercial hardware jammers, such as cigarette lighters, available on the market. Other illegitimate GNSScreations, like signals erupting from high-power transmitters, also degrade the quality of the signal by completely blocking the signal reception and not allowing the GNSS satellites to compute the user position. Another kind of attack is spoofing, which is a more sophisticated form of interference where attackers generate fake satellite signals that mimic authentic ones. Spoofing can deceive receivers into calculating inaccurate positions. Signal blockage may be natural or deliberate; for example, obstructions caused by human intervention and obstacles created by the construction of tall buildings or terrain can block or reflect satellite signals, causing multipath errors that lead to inaccuracies in positioning. Multipath interference signals bouncing off nearby surfaces before reaching the receiver can create timing errors and inaccuracies. In the event of GNSS catastrophic failures, continuous monitoring and taking precautionary measures to detect unauthorized jamming and spoofing are required to prevent failures in GNSS data transmission at ground stations [1; 2; 3]. Early detection and rapid responses to counteract the impact of disruptions are required for seamless data reception and transmission [4; 5]. Recently, some solutions have involved implementing security protocols and GNSS signal encryption techniques in navigation data to resist these kinds of interruptions, which could make it more difficult to disrupt or manipulate the signals. In the GNSS-denied situation, alternative navigation techniques, like the use of inertial measurement units (IMUs), a mixed-mode operation that involves using accelerometers and gyroscope fusion, may help to measure the acceleration and angular velocity, which enhances the resilience and redundancy. Subsequently, computed features from the data serve as input to the ML and deep learning (DL) techniques for classifying and detecting the types of interruption. However, there is no proper explanation in the model output that describes how the model is responding to the changes in the signal and what factors influence the identification of key discriminative factors in distinguishing different classes of disruptions. Taking into consideration all the above shortcomings in signal detection using traditional ML techniques, this study proposes a novel framework for analyzing the GNSS signal characteristics. The proposed approach not only enables the accurate classification of GNSS signal disruptions but also provides valuable insights into the underlying factors driving these disruptions. The main contributions of the work are summarized as follows:  
1. In this paper, we recorded a synthetic GNSS dataset under various disruptions at different Jamming-to-Signal Ratio (JSR) levels using the Skydel simulator.