Sensors and Algorithms Can Accomplish Great Things Together

We are hearing a lot about sensors and algorithms these days. More often than not, the two topics are discussed independently. But it turns out that they are actually related in many ways. Combine the right sensors with the right algorithms and you can accomplish great things.

A case in point comes out of a project conducted at the University of Illinois by an undergraduate student with a passion for aerospace technologies. The student designed and created an algorithm that uses data collected by a number of on-board sensors to calculate air density outside of a descending space vehicle.

An explanation of why that is important is forthcoming. But before getting to that, it would help to understand the problem the student is trying to solve. For that, we need to know a little bit more about sensors.

Sensor Basics

A sensor is any piece of electronic equipment that collects data and either stores or transmits it to a remote receiver. The GPS device in your smartphone is a sensor. So is the fuel gauge in your car.

Spacecraft are fitted with all sorts of sensors designed to collect data and send it back to mission control. There are sensors for navigation, climate control, spacecraft maintenance, and so forth. Sensors like these are designed and manufactured by aerospace sensor design companies like Rock West Solutions in California.

One particular type of sensor that should be on every space vehicle is the air density sensor. More often than not, it is missing.

Hard to Come By

The issue for spacecraft designers is not that density sensors do not exist. They do. It is just that there are not many that have been designed to withstand the extreme conditions of re-entry from space. That’s where the University of Illinois undergraduate student comes in. He discovered a way to calculate air density without needing a special sensor mounted on a space vehicle’s hull.

His algorithm collects data from accelerometers and gyroscopes, two kinds of sensors that were never intended to measure air density properties. The algorithm combines that data with previously collected data relating to maximal rate of acceleration and calculates air density from there.

The algorithm ostensibly learns as it goes. Every piece of data it collects and compares gets stored for later retrieval and analysis. Every subsequent calculation of air density is made using anever-larger data set.

More Accurate Landings

Finally, we get to the question of why all of this matters. Without getting too complicated, air density is an important factor when attempting to determine where a descending spacecraft will land. It is not the only factor but knowing air density makes landings more accurate.

Engineers typically set a static course when they don’t have air density readings to work with. This method for landing a vehicle isn’t as accurate as it might seem due to the fact that spacecraft undergo minor fluctuations in size and shape during re-entry. Those fluctuations throw off the eventual course of the spacecraft regardless of its programming.

Knowing air density can overcome some of the problems presented by re-entry. Engineers don’t have to make such broad guesses about where spacecraft will land. Instead, they can better pinpoint actual landing spots and, in some cases, even get the spacecraft to land exactly where they want it to.

Space travel itself would not be possible without advanced sensor technology. But even the sensors of the 1960s pale in comparison to what is available today. Combining modern sensors with computer algorithms is just the icing on the cake. You can do incredible things when you put the two together in unique applications no one thought of before.

Peter