GPS tracking - Localisation and its drawbacks

GPS (Global Positioning System) is one of the most transformative technologies enabling modern smart infrastructure solutions and IoT product engineering. It allows precise localization and tracking across industries — from navigation and logistics to smart pole technology and geospatial engineering services.

In this blog, let’s understand how GPS localization works, its key components, and its limitations — and how it fits into the broader ecosystem of AI-powered embedded systems and smart city solutions.

1. Satellite Signals -GPS relies on a constellation of satellites orbiting the Earth — typically at least 24 active satellites distributed across six orbital planes.Each satellite continuously broadcasts radio signals containing timing and positional data.

This satellite network forms the backbone of global geospatial infrastructure, supporting everything from smart city asset tracking to GIS mapping for utilities. These signals enable engineers to build intelligent systems that form the foundation of urban infrastructure digitalization and AI in GIS and geospatial analytics.

2. GPS Receiver

A GPS receiver — found in smartphones, vehicles, and industrial IoT devices — captures signals from multiple satellites simultaneously.For accurate localization, it needs a clear line-of-sight to at least four satellites.

In the context of embedded systems development, receivers are often integrated into Edge AI embedded devices such as smart sensors or IoT gateways, forming part of end-to-end embedded product design for location-aware applications.

3. Trilateration

GPS localization uses a geometric process called trilateration.By measuring the distance from at least three satellites (and ideally four for accuracy), the receiver determines its position in three-dimensional space.

This mathematical technique is foundational to GeoAI — combining GPS data with AI and analytics to enhance AI-enabled geospatial intelligence. Modern AI for smart infrastructure applications use trilateration data to optimize mobility, logistics, and asset tracking in smart cities.

4. Position Calculation

Once distances are determined, the GPS receiver calculates its position — latitude, longitude, and altitude — by intersecting spheres representing distances from satellites.This process is computationally intensive and typically handled by custom embedded software development optimized for real-time performance.

In IoT & embedded services India, these optimized algorithms are critical in designing lightweight, battery-efficient GPS modules used in industrial IoT and automation systems.

5. Additional Data for Precision

GPS accuracy improves further through data like:

• Ephemeris data (satellite orbital information)

• Ground-based corrections (e.g., DGPS)

• Real-time kinematics (RTK) for centimeter-level accuracy

For geospatial engineering services and GIS consulting companies, these corrections ensure reliable data for applications like smart pole IoT integration, AI for utilities and infrastructure management, and digital twin smart city simulations.

6. Localization Output

After processing, the GPS receiver outputs coordinates — typically latitude, longitude, and sometimes altitude.These data points are used for mapping, navigation, tracking, and AI-powered decision systems across industries.

In smart city technology partners’ solutions, such precise localization powers:

• Fleet management systems

• Smart lighting and energy monitoring

• Infrastructure maintenance tracking

• Public safety and surveillance using computer vision for smart city systems

Drawbacks of GPS Localization

While GPS localization is robust, it does have limitations that engineers must account for when developing IoT and embedded systems.

1. Interferences and Obstructions

GPS performance drops in environments with obstacles like tall buildings, dense forests, or tunnels.In urban infrastructure digitalization, this challenge often requires hybrid localization methods — combining AI GIS analytics with Wi-Fi, BLE, or LoRa positioning for continuous tracking.

2. Multipath Effects

Signal reflections from buildings or metallic surfaces cause multiple signal paths, introducing errors.AI-based smart infrastructure systems now use predictive models to compensate for these anomalies using AI engineering solutions and machine learning algorithms.

3. Limited Accuracy

GPS is typically more accurate in horizontal positioning than vertical (altitude) estimation.For engineering services for smart cities, integrating GPS with AI and IoT sensors provides higher vertical accuracy needed for drones, construction mapping, and 3D terrain modeling.

4. Time to First Fix (TTFF)

When a receiver starts or changes location, it takes time to lock onto satellites — known as TTFF.Embedded developers in AI product engineering companies in India optimize firmware to reduce TTFF using cached satellite data and predictive models.

5. Power Consumption

Continuous GPS use consumes significant power, a major consideration in embedded systems development.Edge AI embedded systems mitigate this by activating GPS modules only when required, extending device battery life in field applications.

6. Cost and Infrastructure

Maintaining the GPS satellite constellation is costly, though end-user access remains free.Organizations offering IoT & embedded services in India integrate low-cost GPS modules within custom embedded software to deliver affordable smart city solutions.

7. Indoor Localization Challenges

GPS signals degrade or disappear indoors.To overcome this, AI and IoT solutions for municipalities integrate complementary technologies like Wi-Fi RTT, BLE beacons, or UWB sensors, supported by AI-powered embedded systems for continuous, hybrid localization.

Conclusion

GPS is the foundation of modern smart infrastructure solutions, powering applications in navigation, mapping, and AI-driven smart city technologies.As industries adopt AI-enabled geospatial analytics and Edge AI embedded systems, GPS will evolve from a standalone localization tool into an integral part of AI for smart infrastructure and urban digital transformation.

At EurthTech, we continue to pioneer embedded AI solutions and IoT product engineering that leverage GPS and geospatial intelligence — creating a connected, intelligent, and data-driven urban future.

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