# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
### Related Work  
Currently, numerous anti-spoofing technologies [8] are available for the detection, identification, and suppression of spoofing signals in GNSS systems. These include in-band power monitoring [9], signal quality monitoring [10], receiver timing monitoring [11], navigation message authentication [12], multi-antenna technology [13], and multi-receiver technology [14], among others. However, there have been limited studies on the localization of GNSS spoofers. The commonly employed methods for localizing GNSS interference sources include direction of arrival (DOA) methods [15; 16], time difference of arrival (TDOA) methods [17; 18], and received signal strength (RSS) methods [19; 20]. The signal strength of spoofing interference is equivalent to the real GNSS signal strength, and the signal-to-noise ratio (SNR) of the signal can be as low as \(-19\) dB, which makes it difficult to extract the DOA, TDOA, and RSS of the signal. DOA methods frequently use algorithms such as CAPON and MUSIC to estimate signal arrival angles, which cannot efficiently extract the DOA of spoofing interference signals in the aforementioned low-SNR conditions. The extraction of TDOA with weak spoofing interference requires long-term data correlation operations, which requires TDOA methods to collect and transmit a large amount of raw data. It is difficult to extract the RSS because the spoofing signal is submerged in noise. Importantly, due to the extreme similarity between the spoofing signal and the real signal, it is difficult to distinguish the signal features of the real and spoofing signals from the extracted ones, even if the above localization methods implement signal feature estimation. For example, the navigation receiver can compute the C/N0 of the spoofing signal to achieve the RSS measurement, but it cannot distinguish the C/N0 of the real satellite signal from that of the deception signal, and the DOA and TDOA methods face similar problems. The deception localization methods proposed by [21; 22] have shown relative effectiveness; however, their establishment relies on stringent restrictive assumptions, thereby impacting the practicality of these methods. Reference [21] is a low-cost deception interference localization method, but the establishment of the method requires accumulating observation data under the assumption that the deception interference source is stationary and the replay delay does not modify over time or has the same shift, which is a relatively strict assumption condition. In addition, reference [21] can only realize the repeater spoofing localization, while the generative deception source is more common. Reference [22] is a deception interference localization method based on TDOA, and the article utilizes the geometry and known positions of multiple static GPS receivers distributed within the power substation. The establishment of this method requires the construction of a GNSS monitoring sensor network and requires the receivers in the network to return relevant peak information, which brings certain limitations to the practical application of the method. This article adopts a direct localization method, while the traditional localization method adopts a two-step processing [23]. The two-step method first extracts measurement parameters (such as DOA and TDOA) and then estimates the source location. Direct localization achieves source localization without estimating signal parameters. This type of method can avoid correlation problems [24], and its localization accuracy has been proven to be significantly higher than traditional two-step methods, especially under low signal-to-noise ratio conditions [25]. In the application of this paper, due to the weak signal of the spoofing signal and the dynamic monitoring scenario, the direct localization method is used to localize the GNSS spoofing interference, which improves the localization accuracy and robustness of the method in this scenario.