Introduction

   When we look up at the night sky, we see through all the layers of our atmosphere into outer space. We see across millions of lightyears to the shining, beckoning pinpoints of light that make us stare up with wonder and curiosity. But what may not be so apparent is all the atmospheric layers we’re actually looking through.

   Using this graphic from the University Corporation for Atmospheric Research (UCAR), the various layers of the ionosphere are mapped based on their rough vertical altitudes from the surface. The graphic further points out the interesting division of ionospheric layers. The lowest layer — the D layer — sets between 40-50 miles above the Earth’s surface, while the next highest layer — the E layer — lies another 20-30 miles higher into the atmosphere. Finally, the F layer lies at the top of the atmosphere, starting at a staggering altitude of 140  miles above the surface! 

   Although like many aspects of the atmosphere, these numbers are not set in stone, and vary widely based on the time of year, the time of day, solar activity, and many other parameters. But we know they exist and we’ve been able to empirically demonstrate their presence via radio frequency (RF) experiments.

Ionograms

   The data collected by RF receiving stations can be converted from phase and quadrature (I/Q) components into plots like those shown below. In each image, the X- and Y-axis are frequency [MHz] and Time of Flight (TOF) [s], respectively. Conversely, TOF can be used to calculate Distance [km]. But what do all these lines tell us?

   Let’s start with the most uninteresting feature, the noise. In each plot, noise is represented by vertical lines. Based on the horizontal and vertical axes, the vertical lines represent strong signal at specific frequencies that are present across all altitudes (or, similarly, are present at all times). These are “vertical interferers”, and can be easily removed through image cleaning processes.

   If the vertical lines were eliminated, only the “swooshes” would be left. Physically, each swoosh represents an RF signal reflected from some surface, and the vertical distance between each swoop indicates the time delay between one reflection and the next.

   Reflections from what? This is a great question, and one that is difficult to pick apart. With the right conditions, RF frequencies in the 2-20 MHz range can bounce off of most anything. For instance, one reflected wave may be the result of a reflection from a D or E layer of the ionosphere, but other reflections can occur between the D layer and the Earth’s oceans. In other phenomenon known as atmospheric ducting can cause an RF signal to reflected repeatedly between two layers of the ionosphere before escaping back towards the Earth to be collected by a ground station, or escaping through the E layer and into the vastness of space.

Interpretation

   But wait, there’s more! Many parameters in the ionogram have a highly useful physical interpretation. For instance, assume the third ionogram in the previous image (far right) can be cleaned, segmented, and each signal identified. In this case, the bottom “swoop” (identified with green dots below) lies at some distance (ex. 2700 km) from the sensor. Although the direction remains ambiguous, the distance identifies a radius of valid solutions surrounding the detector from which the detected signal must have come. Consequently, ambiguity in direction can be eliminated through triangulation and geolocation.

   Of course, there are other signals that were collected by the same receiver at the same moment in time. Since the bottom swoop can be correlated with one circle with radius equal to the detected range, the second-highest swoop (identified in the previous image by salmon-colored dots) can also be correlated with a range value, and therefore another circle of direction-ambiguous solutions. Additionally, a second sensor spatially offset from the first sensor may also pick up similar signals with different ranges. All of these solutions can be plotted on a world map to indicate the rough proximity of sensors and their “nearby” sources. This scenario is illustrated in the map below, where two sensors (one in the United States, and one to the southeast) each collect one or more RF signals. Each signal is correlated with a range, and the range solutions are plotted as direction-ambiguous solution circles (dark blue, light blue, and green).

   Additionally, the shape of each swoop tells us something about the layer from which the RF signal reflected. For instance, the right-most edge of each trace typically forms some kind of bend, or “nose” where the signal reverses in direction with an increase in range. Physically, this is pretty neat — it says that the same frequency was detected at two ranges values, and both of those range values are related based on a physical phenomenon: atmospheric penetration. The frequency of the nose indicates the frequency at which a given signal no longer reflects from that particular layer of the ionosphere, and escapes into space without reflecting off of another, higher layer.

   Finally, a “split” can be seen on some traces similar to the split in a snake’s forked tongue. Each “fork” is also correlated with a physical parameter: polarization. At lower frequencies, both polarizations give a similar (but slightly different) response as they reflect off each ionospheric layer. At higher frequencies, though, each polarization interacts much more differently with the refractive index of the ionosphere and atmospheric layers, and so cause a split to be observed in the ionogram. With careful diligence, these splits can be identified, segregated, tracked, and predicted via image processing techniques.

Conclusion

   Ionogram interpretation — and the entire RF domain, for that matter — is a stunningly beautiful field. The results are pretty, and their interpretation across the spectral, temporal, and spatial domains heavily impact many aspects of our daily lives in ways we never think of. In a similar sort of beauty, all of this confusion and information can be tamed and understood by processing (and correlating!) three-dimensional slices of the data (frequency, range, strength) one bite at a time.

   It’s worth mentioning that the technology and research into the field of RF analysis is far from dead. In fact, it is the basis for over-the-horizon radar (OTHR), a fundamental pillar of many country’s military defense systems.