SCROLLNavigation systems identify an object’s position, speed and direction using onboard computation or external signals. The document explains that navigation methods fall into two categories: physical model–based methods such as inertial navigation systems (INS) and dead-reckoning, and external data–based methods like the Global Navigation Satellite System (GNSS). INS and dead-reckoning use velocity and acceleration changes to estimate position, while GNSS provides satellite-derived location and timing data. This foundational split is essential for understanding how modern navigation layers blend sensor data with satellite inputs for accuracy.
GNSS is defined as a constellation of satellites transmitting precise timing and location signals to receivers, enabling global positioning. The file highlights major GNSS constellations including GPS (US), GLONASS (Russia), Galileo (EU), BeiDou (China), QZSS (Japan) and NavIC (India). Each constellation operates multiple satellites in different orbital planes to ensure positioning accuracy, integrity, continuity and availability. The comparison chart in the document shows variations in orbital altitude, inclination and satellite count across each system, which directly affects performance across environments.
The document clearly differentiates GPS from GNSS: GPS is a single satellite system operated by the United States, while GNSS refers to the combined use of multiple global constellations for improved performance. The visual comparison on page 8 shows that GPS uses about 31 satellites while GNSS receivers can access over 89 satellites across four systems. Because GNSS receivers combine signals from multiple constellations, they offer better accuracy, reduced blind spots and more consistent coverage, especially in urban canyons, forests or high-latency regions.
The file emphasizes that GNSS alone can suffer from signal obstruction, which is why advanced methods like INS, dead-reckoning and real-time kinematic (RTK) positioning are widely used. INS and dead-reckoning fill GNSS gaps by estimating position through motion sensors, helpful in tunnels or dense cities. RTK, explained on page 17, enhances GNSS precision by applying correction data from a fixed base station to achieve centimeter-level accuracy. These techniques are critical for automotive navigation, robotics, surveying and industrial IoT systems requiring stable high-precision positioning.
The document highlights how GNSS powers a wide range of IoT applications such as connected vehicles, asset tracking, wearables, smart street lighting, waste management and emergency response. GNSS supports real-time geofencing, navigation in challenging environments, synchronization of telecom and power grids, and secure anti-spoofing/anti-jamming capabilities. As shown in the GNSS applications section, positioning, navigation and timing (PNT) are now foundational for transportation, finance, agriculture, law enforcement and scientific research—making GNSS indispensable for the future of IoT development.
