Positioning, Navigation, and Timing (PNT) technologies form the backbone of many of today's most critical applications, spanning various sectors and impacting everyday life. Modern PNT solutions rely on a sophisticated interplay of diverse sensors and systems to deliver precise and reliable information about position, navigation, and time (Dardari, et al., 2015). In this introduction, we discuss the fundamental components that constitute these advanced PNT systems and explore their wide array of applications.

One of the cornerstones of PNT technology is Global Navigation Satellite Systems (GNSS) (Teunissen and Montenbruck, 2017; Morton, et al., 2021). GNSS involves satellite constellations that provide timing and positioning signals to receivers on Earth. These signals are pivotal in numerous domains, including personal navigation, geolocation services, agriculture, and search and rescue operations. GNSS enables users to determine their exact location anywhere on the globe with remarkable accuracy and reliability. Complementing GNSS are Inertial Navigation Systems (INS) (Britting, 2010), which consist of accelerometers, gyroscopes, and magnetometers. These systems measure inertial forces to provide orientation, velocity, and position data. This technology is particularly essential for applications such as aircraft navigation, autonomous vehicles, marine navigation, and robotics, where continuous and precise navigation information is critical, even in the absence of external signals.

Another key technology in modern PNT solutions is Light Detection and Ranging (LiDAR) (Royo and Ballesta-Garcia, 2019). LiDAR utilizes laser sensors to emit laser light and process the reflections to measure ranges. LiDAR is invaluable in autonomous vehicles, environmental monitoring, mapping, and geospatial analysis, providing highresolution, three-dimensional information about the surroundings. Computer vision and camera systems (Hartley and Zisserman, 2004) also play a crucial role in PNT. These systems are based on image sensors and advanced processing algorithms, which analyze the environment. This technology finds applications in augmented reality, robotics, autonomous vehicles, and surveillance, enabling machines to interpret and respond to visual information with high precision.

Additionally, wired and wireless communication networks (Monge and Contractor, 2003) are integral to modern PNT solutions. Technologies such as 5G, Wi-Fi, and Bluetooth enable data exchange between devices and systems. While some of these networks are not explicitly designed for PNT, they can be utilized as Signals of Opportunity (SoO) to enhance dedicated PNT systems. Typical applications include the Internet of Things (IoT), smart cities, real-time traffic management, and remote sensing.

In summary, modern PNT solutions represent an intricate blend of various advanced technologies. Each component-whether GNSS, INS, LiDAR, computer vision, or communication networks-plays a vital role in delivering the precise and reliable PNT information that underpins countless applications in our daily lives and across numerous industries. As these technologies continue to evolve, their integration and capabilities will expand, driving further innovation and transforming how we navigate and understand the world.

Traditional PNT methods, while revolutionary, have inherent limitations that necessitate the integration of diverse sensors and systems to achieve continuous and reliable information. Each of these traditional technologies, -such as GNSS, INS, LiDAR, computer vision, and communication networks -has its unique strengths and weaknesses, shaped by their inherent characteristics and environmental interactions. Understanding these limitations underscores the need for a multifaceted approach to PNT solutions.

GNSS is susceptible to several limitations that can impair its accuracy and reliability. Signal obstructions and multipath effects, common in urban environments, forests, and indoor settings, lead to signal loss or degradation resulting in reduced positional accuracy and reliability. Additionally, GNSS signals are vulnerable to interference and jamming from other electronic devices or malicious users, which can lead to the loss or corruption of useful signals. Atmospheric conditions, particularly ionospheric and tropospheric delays, can also affect signal propagation, causing positioning errors, which are aggravated near the equator or during solar activity. Moreover, GNSS relies on the visibility of a sufficient number of satellites. In environments such as tunnels or urban canyons, the number of visible satellites can drastically reduce, hindering the system's ability to resolve the navigation problem.

INS, while powerful, face challenges primarily related to drift over time. The principle of INS involves integrating acceleration and angular rates over time, leading to cumulative errors known as drifts. Without periodic calibration or correction from external sources, these errors can accumulate, reducing the system's accuracy. Furthermore, high-precision INS is complex and expensive, limiting their suitability for low-cost applications.

LiDAR systems, though effective, encounter limitations due to environmental conditions and intrinsic characteristics. Weather conditions such as rain, fog, or smog can degrade LiDAR performance, reducing its accuracy. LiDAR also has limitations in range and resolution, making it difficult to detect distant or small objects. Additionally, high-quality LiDAR systems are often expensive and require significant power, posing challenges for low-cost or low-consumption applications.

Computer vision and camera-based systems are dependent on the lighting conditions of the scene they capture. Low light or highly dynamic lighting conditions can lead to poor quality images, affecting the system's performance. Occlusion and field of view limitations can result in incomplete images, hindering accurate analysis. Moreover, image processing algorithms are computationally intensive, requiring substantial power and memory, which can be challenging for real-time or low-cost applications.

Communication networks also face several limitations that impact PNT solutions. Latency and bandwidth constraints can affect real-time data transmission and processing, introducing delays and bottlenecks, especially in data-intensive applications. Network coverage can be inconsistent, particularly in underserved areas, reducing the availability and reliability of PNT solutions.

The limitations of traditional PNT methods highlight the necessity of integrating a diverse array of sensors and systems. Each technology brings unique capabilities and constraints. By combining these technologies, we can mitigate individual weaknesses and enhance overall PNT performance. This multifaceted approach is essential to meet the growing demand for precise, reliable, and continuous PNT information across various applications and environments.