This paper presents an empirical analysis of GNSS timing spoofing detection using data from Jammertest 2024. While positioning spoofing has been extensively studied, timing spoofing presents unique threats to critical infrastructure including power grids, telecommunications, and financial systems. Our analysis reveals that position parameters become compromised at lower spoofing power levels (20-25 dBm) compared to timing parameters (30 dBm), creating potential for early warning systems. Traditional carrier-to-noise ratio methods proved unreliable, while validity flags in NMEA messages showed strong but incomplete detection capability. To address detection gaps, we implemented an unsupervised Isolation Forest algorithm achieving perfect recall (100%) and high specificity (99.96%) without requiring extensive labeled examples. A combined approach leveraging both validity flags and machine learning is able to offer robust protection with minimal computational overhead. Our findings demonstrate practical robustness measures for timing-dependent critical infrastructure and how it can be implemented.

Designing a globally ubiquitous positioning system that works seamlessly in all environments remains a critical area of ongoing research. While Global Navigation Satellite Systems (GNSS), such as GPS, are the predominant technology for global outdoor positioning, they fail in areas with physical obstructions (e.g., dense urban regions and indoors) and are prone to jamming. These limitations significantly restrict their global accessibility and dependability, highlighting the need for supplementary positioning technologies.

Indoor positioning systems (IPSs) are the foundations for all indoor location-based services and applications. In this article, we present a precise and robust IPS using ultra wide-band (UWB) technology fused with an inertial measurement unit (IMU). Both technologies are integrated to account for the non-line-of-sight (NLOS) problems arising in a dense challenging environment found within an industrial laboratory in Finland. Besides the conventional estimation techniques e.g. extended Kalman filter (EKF), we employ the Rauch-Tung-Striebel (RTS) smoothing algorithm in addition to a multivariate regression-based offset compensation method to improve the overall positioning accuracy of the system. The recommended number of distributed UWB anchors versus the coverage area is also discussed and tested in this article. The experiments were held by a patrolling mobile robot with millimeter accuracy, which acted as a ground truth reference to all used algorithms. The positioning estimation results showed a superior performance by the proposed method (UWB/IMU EKF-RTS-LR) with mean accuracy of 4.7 cm, and 9.6 cm for more than 95% of the time.

Jamming is the act of intentionally directing powerful electromagnetic waves toward a victim receiver with the ultimate goal of denying its operations. This paper describes the main types of Global Navigation Satellite System (GNSS) jammers and reviews their impact on GNSS receivers. A survey of state-of-the-art methods for jamming detection is also provided. Different detection approaches are investigated with respect to the receiver stage where they can be implemented.

Tactical situational awareness for military applications should be based on infrastructure-free systems and should be able to form knowledge of the previously unknown environment. Simultaneous Localization and Mapping (SLAM) is a key technology for providing an accurate and reliable infrastructure-free solution for indoor situational awareness. However, indoor environments and the requirements , especially the size and weight limits of the system, make the implementation of SLAM using existing algorithms challenging. In particular, we aim to implement SLAM using a monocular camera, due to size limitations, whereas most existing algorithms use stereo images. The two major obstacles to be overcome are the unknown scale of translation observed using a monocular camera and the shortage of features indoors, complicating visual perception. Herein, a Kalman filter based SLAM solution is discussed, utilizing a concept called visual odometry that provides absolute translation information with a reduced number of features. The results show that our solution is feasible for performing SLAM indoors using a monocular camera.

Microphone arrays, also known as acoustic antennas, have been extensively used for sound localization. Small-scale microphone arrays have especially been used in teleconferences and game consoles due to their small dimension and easy deployment. In this article, we present an approach to locating a sound source using a small linear microphone array. We describe the fundamentals of linear microphone arrays and analyze the impact of geometry in terms of positioning accuracy using the dilution of precision (DOP) concept. The generalized cross-correlation (GCC) based on the phase transform (PHAT) weighting function is used to estimate the time difference of arrivals in a microphone array. Given the time differences, we use both closed-form and iterative optimization solutions to calculate the coordinates of the sound source. In order to evaluate the performances of the solutions applied in this paper, simulations and field tests were conducted. Simulation results show that the closed-form algorithm gives a positioning error of less than 5 cm in a 10-by-10 meter room when the geometry of a microphone array is good and the signal to noise ratio (SNR) is high. Linear small microphone arrays have lower performances compared to a non-linear distributed array. When the scale of a linear array is reduced, the positioning accuracy decreases dramatically. With a small linear array, the iterative optimization algorithm gives much better performance compared to the closed-form algorithm. Field tests were conducted in an 11-by-5.6 meter room using a linear array with a length of 0.23 meters. Positioning results show an average error of 0.25 meters along the axis parallel to the linear array and 0.53 meters error along the axis which is perpendicular to the linear array.

Pseudolites provide a means for bridging the gap between outdoors and indoors when GNSS (Global Navigation Satellite System) positioning is concerned. This paper presents a ubiquitous outdoor/indoor GNSS navigation platform that utilizes GPS (Global Positioning System), GLONASS, and pulsed pseudolite (PL) signals for seamless positioning. When a pseudolite signal is pulsed to efficiently transmit the GNSS-like signal only at particular time instants, interference problems between the terrestrial pseudo-satellite signals and the space-based satellite signals are significantly reduced. Pulsed pseudolites are strategically placed indoors at known locations at the ends of building corridors to assist high-sensitivity GPS and GLONASS positioning. A particle filtering solution is implemented to combine the high-sensitivity GNSS and the pseudolite proximity information in order to provide a seamless outdoor/indoor positioning platform. As demonstrated with real-life experiments, pseudolites provide a convenient navigation aid indoors for a GNSS receiver without the need for using additional hardware.

A comfortable and useful pedestrian navigation system is accurate, reasonably priced, easy to use, and light to carry. Smartphones are attractive platforms for the navigation systems due to their small size, low cost, and the fact that they are already carried routinely by many pedestrians. Pedestrian navigation is mostly needed in GNSS degraded and denied areas such as indoors and in urban canyons. Positioning in these environments is still a challenging task. Aiding from other location sensors is typically needed. Obtaining a reliable heading e.g. from a digital compass is one of the most challenging tasks indoors because of the environmental disturbances to the sensor measurements. Visual-aiding has been considered for solving this problem for the last few years. Most algorithms are however often too massive for a Smartphone with limited computing power and storage space. This paper presents a lightweight algorithm for solving the problem. It is based on vanishing points calculated from lines in consecutive images. Results from a field test with a Nokia Smartphone have demonstrated that the performance of the algorithm in calculating the heading change is much better than that of the built-in digital compass. The heading change can be detected with the proposed algorithm at about 1 Hz frequency under the PC environment. It has potential to be adopted for a Smartphone platform.