Moving Object Databases for Near Earth Object Tracking

Countless celestial bodies, asteroids, comets, and more, whirl around the Sun. Some of them, which we call Near-Earth Objects (NEOs), occasionally come close to our planet. Tracking their movements is crucial for Earth’s safety. Recently, I started my PhD on Moving Object Database and naturally, a question occured to me, can the techniques and approaches in this field of study be applied to track these objects? Are we already doing so? This post is a short survey on the latest state of the art in NEO tracking I conductued to answer these questions.

1. Introduction

Traditional databases are like snapshots; they capture data at a specific moment. But what if the data is constantly changing, like the position of a moving asteroid? That’s where Moving Object Databases (MODs) come into play. They are designed to handle data that changes over time, making them perfect for tracking objects in motion.

MODs can store and manage the trajectories of moving objects, allowing for real-time updates and predictions. This capability is essential when monitoring NEOs, whose paths can be influenced by various gravitational forces.

2. Moving Object Databases

2.1 Data Models

Think of spatio-temporal databases as an evolution of traditional databases, but with a special focus on movement. They introduce two key concepts: moving points and moving regions. These aren’t just static dots on a map - they’re dynamic entities that change position over time. For instance, a moving point can be described mathematically as: $$ M(t) = (x_0 + v_x (t - t_0),\ y_0 + v_y (t - t_0)) $$ This elegant formula lets us pinpoint an object’s exact location at any given moment [2].

2.2 Indexing Techniques

The challenge of tracking moving objects led to some clever solutions. Early researchers adapted R*-trees to handle continuous movement, making it possible to quickly find objects within a certain area or identify the nearest neighbors [1]. Later innovations brought us kd-trees, which excel at connecting observations made on different nights [7]. These aren’t just theoretical improvements - they’re practical solutions that keep up with the rapid pace of astronomical surveys.

2.3 Query Types

What can we actually do with these databases? Quite a lot! We can:

Take a snapshot of where everything is at a specific time
See where objects have been over a period
Find the closest objects at any moment
Discover when and where objects might interact

3. Application to NEO Tracking

3.1 Pipeline Overview

Let’s look at how this all comes together in practice. Astronomical surveys are like giant cameras taking pictures of the sky. They spot moving points of light and connect the dots - first into tracklets (what we see in one night) and then into tracks (what we see over multiple nights). This is how we figure out an object’s orbit.

3.2 Pan-STARRS MOPS

The Pan-STARRS system is a real success story. It’s like having an automated astronomer that never sleeps, finding new asteroids and calculating their orbits. The numbers speak for themselves: it catches over 99.5% of simulated objects and successfully links observations across multiple nights about 80% of the time [6].

3.3 LSST MOPS

The Large Synoptic Survey Telescope (LSST) is taking this even further. Its system splits the work between DayMOPS for batch processing and NightMOPS for real-time tracking. The results are impressive - it’s expected to link 93.6% of NEOs brighter than magnitude 22, with very few false alarms [8].

4. Discussion

So, can MOD techniques help track NEOs? Absolutely! The evidence is clear in systems like Pan-STARRS and LSST. These systems handle millions of observations every night, using the very principles we’ve discussed. They’re not just theoretical - they’re actively protecting our planet by providing accurate predictions of NEO movements.

5. Conclusion

To answer the questions we started with: Yes, MOD techniques are not just applicable to NEO tracking - they’re already being used! Systems like Pan-STARRS and LSST are built on these principles, showing how theoretical database concepts can have real-world impact in protecting Earth. The future looks promising too - as we continue to improve these systems, we’re getting better at spotting and tracking potential threats to our planet.