# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
###### Abstract
One example is a non-intentional interference from an out-of-band communication signal that could exhibit broadband pulsed orthogonal frequency division multiplexing (OFDM) signals, which should not transmit in the GNSS frequency bands. Therefore, this system should be found, and the manufacturer should implement adequate out-of-band suppression. A second example is an intentional interference (i.e., a jammer) such as a privacy protection device (PPD), which typically emits chirp signals [33]. In this case, it should be found and eliminated as it is illegal in most countries. Lastly, a third example is a distance measurement equipment (DME) signal from an airport, a narrow-band Gaussian pulsed signal that legally coexists in the GNSS bands [34]. These examples highlight the need to detect and have a rudimentary signal classification. Traditionally, signal classification is more complex and resource-intensive than detection, requiring more expensive equipment. This article showcases a low-cost commercial-off-the-shelf (COTS) interference monitoring receiver with a hardware cost below 200 E, which is at least an order of magnitude cheaper than state-of-the-art interference monitoring systems. Using COTS equipment is not new [35]. However, unlike [PERSON] et al. [35], the proposed system is significantly cheaper than previous systems. Hence, more receiver nodes are justified in the network as it is considerably more cost-effective. Further, the presented system uses self-calibrating and machine learning (ML) techniques to generalize and limit maintenance and calibration costs. Therefore, the current system improves on several limitations of classical interference  
Figure 1: A qualitative example comparison of the symbolic coverage (yellow colored area) of a single high-performance monitoring receiver to a distributed network of low-performance receivers. In both cases, the POI is too low to detect the interference signal (red-colored interference signal), indicating that either the performance must be increased (higher coverage per receiver) or more receivers are required. (**a**) A high-performance interference monitoring system with great sensitivity and a single large coverage area. (**b**) Multiple low-performance interference monitoring systems, each with low sensitivity and small coverage. monitoring systems. Lastly, the initial results show that the system can accurately and in real-time detect and classify interference signals with performance comparable with traditional methods. This article extends the conference paper [36] in four ways. First, it presents an extended hardware description of the system, including an analysis of the power consumption, methods to reduce the power consumption, and an investigation of alternative hardware designs. Second, the software design is more detailed, most notably the architectural change of using a Redis database for efficient logging and control. Third, the ML algorithms are explored more deeply to reveal the novel pipeline for interference detection and classification, which facilitates unsupervised label generation to detect and classify unknown interference signals reliably. Fourth, the results of [36] are extended by incorporating more trials to fortify the previous results. Most interesting is the inclusion of tests in a large anechoic chamber with a PPD mounted inside a car for realistic wave propagation effects. The remainder of this article is structured as follows. Section 2 provides a background to interference monitoring. Section 3 describes the hardware, Section 4 describes the software, and Section 5 describes the algorithms. Section 6 illustrates the test setup, and Section 7 shows the results of the system. Finally, the conclusions are drawn in Section 8.