Improved Geolocation Opens Up New Wave of Use Cases


AI and machine learning, combined with important technical developments, are enabling a vast number of new geolocation use cases.

Geolocation is maturing as new use cases are enabled by the emergence of cost-effective systems that harness the power of connectivity alongside adjacent advances in data handling and artificial intelligence (AI). From adhesive parcel labels to automated vehicles, new verticals for geolocation to support are opening up and enabling innovative business opportunities. 

Some important technical developments that support increased uptake of geolocation are the convergence of geo-databases and the addition of rich metadata to GNSS data streams. This powerful data environment enables applications such as temperatures of frozen goods to be tagged to their GPS position and to close the loop on assured cold chain logistics, for example.

At the same time, AI and machine learning are being deployed and enabling cluster pattern analysis. Many human activities create distinct geographical patterns that can be analyzed to improve processes. Cluster patterns of road accidents sites, for example, can be derived from ambulance back door openings that are geotagged to position information from the global navigation satellite system (GNSS).

This data can be used to improve bad road intersection design or to help build ambulance dispatch centers strategically, so they are close to areas of regular accidents. In addition, geographical bottlenecks in transport hubs can easily be identified and earmarked for process improvement.

See also: Artificial intelligence is Changing Logistics Automation

Improved accuracy

Innovations in adjacent technologies are facilitating new geolocation use cases, but there have also been significant improvements in geolocation itself, and these are opening up new verticals. The sub-one meter accuracy that is now available with L1 and L5 GNSS plus the adoption of real-time kinematic (RTK) positioning in GNSS modules are routinely achieving accuracy down to centimeter levels. Previously, this level of accuracy was a highly specialized domain and would have required reference base stations and very expensive receivers that would have made many use cases unviable.

Some cellular carriers have realized the mass market appeal of RTK and are adding network transport of radio technical commission for maritime services (RTCM) via IP (NTRIP) to their infrastructure and selling access to these over-the-top (OTT) network layers as a low-cost service. This provides an excellent enabler for RTK GNSS without the expense and complexity of reference base stations. Highly accurate GNSS at low cost enables new possibilities and novel applications and is a step change in GNSS performance.

Low-cost opportunities for geolocation

The low-end markets are typically focused on cost and simplicity. Low-cost devices are aimed at everything from vehicle breakdown hazard triangles that may or may not ever get used to synchronized aviation lighting where the position information is not used, but the GNSS device is simply used as an accurate timing source. In low-cost applications, absolute precision is seldom required, and the main driver is cost.

The main issues for these deployments are bound by the rules of physics. With low cost comes pressure for small batteries, small housings, and small antennas. All of these have challenges in the real world, where the sizes of these elements are directly proportional to performance.

The smaller the antenna, the longer the device has to be on to collect enough information to get a fix, and the less likely it will be able to receive the very weak signals from satellites. The smaller the battery, the shorter the time a device can operate in an active mode. While many customers push for smaller and smaller modules, the main limitation in overall product size is usually determined by the required battery life and expected sensitivity, plus the required antenna size.

To keep up with evolving demands, companies must develop novel techniques to help achieve very low power operation of devices in this category which are ideal for self-contained container trackers and disposable tracking devices.  It is important to balance the best performance for their products at the lowest cost.

Ultra-low power consumption

For disposable devices or devices that cannot be installed or connected to a reliable power source, low power is absolutely critical. Modules must have low power modes, and we have also enabled some of our devices to operate in an ultra-low-power mode where the position information is calculated on a server instead of the GNSS device. This allows the device to operate in a very low power mode and only switch on long enough to receive some critical raw unprocessed data before switching off again.

A small data package is sent to the server typically by a low power wide area (LPWA) network transmitter device, and the heavy current consuming calculations of the position is transferred to the server. This allows for one-tenth of the power requirements that would typically have been required to calculate the position information on the GNSS device itself and allows for smaller, lower-cost devices to be utilized.

High-performance challenges

High-end systems are used to complement autonomous driving applications or provide accurate timing sources for cellular networks. There is also growing demand for RTK-capable GNSS for use in precision agriculture.

Accuracy and integrity of the information is key to high-end performance, so adding inertial measurement units (IMUs) internally or catering for connection to external companion IMU chips allows for continued tracking in areas such as urban canyons where GNSS signal outages may be experienced. This fusion of technologies allows for seamless, uninterrupted tracking under bridges, beneath overpasses, and through tunnels.

Many high-end applications are involved in highly sensitive activities such as assisted or even autonomous driving or important medical use cases. This means certified modules are mandated by law in a growing number of markets, and certification has therefore become a priority. For example, high-end automotive applications require certification to the automotive standards IATF 16949:2016 and, in some cases, ASIL-B.

In markets like India, modules need to conform to AIS-140 for any applications for use in government fleets or public transport. For most standard markets, CE or FCC level certification relating to spurious radiation are required.

A full 360-degree design review service can save customers considerable expense, reducing the number of prototype runs or failed certification lab tests because GNSS and radiofrequency (RF) experts can see a problem on a customer’s schematic or layout, and help the customer avoid expensive re-runs or failed lab tests. This type of service can significantly speed up the time-to-market and expedite and evaluate products prior to certification lab tests.

With geolocation maturing to enable cost-efficient capabilities that have very low power requirements as well as complex yet highly accurate abilities, it’s important to select certified modules that integrate efficiently with antennas so that you can add geolocation to your project and build a smarter world.

Mark Winton

About Mark Winton

Mark Winton is a Product Development Manager in the GNSS Department at Quectel Wireless Solutions, a leading global supplier of cellular and GNSS modules for wireless technologies like 5G. He is an experienced Technology Leader with a long history of innovating in the GNSS domain. Mark has a strong background in RF Design and the development of PMR Radio. He spent four years as a Senior Field Application Engineer supporting Quectel's cellular and Smart Modules. He has previously founded, incubated, and spun off multiple GNSS-related businesses. His interests lie in finding unique uses for GNSS and combining GNSS with other technologies to create new synergies. The companies he founded sponsored, collaborated, and managed PhD level research into building tools using machine learning (AI) to find unique Geographical patterns in GNSS data.

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