Week 3: It's All in the Angles: Finding the Radar's Best Seat in the House

Last week ended with a plan to test three radar placement angles and find which one gives the classifier the most to work with. This week, I ran those tests, and the answer turned out to be both intuitive and satisfying once I saw it in the data.

The three angles I tested were 0°, 45°, and 90° relative to the swing plane. I chose not to include angles from 90° through 180° because the second half of the swing, the follow-through, has fewer nuances than the first half, the backswing. The backswing is what a player actively controls; the follow-through is largely a byproduct of it. Since the goal is a single radar setup that can capture enough information to predict swing faults, I needed to determine which of these three positions would produce the most feature-rich spectrograms.

To make the comparison meaningful, I varied both the club and the golfer. Different club lengths produce different swing mechanics and body orientations at different points during the swing, so I collected data across multiple clubs at each angle. I also collected data from both my brother and me because the Radar Cross Section (RCS), the amount of energy reflected back to the radar from a target, is heavily influenced by the golfer’s body shape and posture. Flatter surfaces oriented toward the radar and corner-like geometries produce high RCS through direct reflections back to the radar source, whereas curved or angled surfaces scatter energy away from the source and yield weaker returns. Using two golfers with different builds helped ensure the signatures I identified were properties of the swing itself rather than artifacts of one person’s body geometry.

From the spectrograms and range-Doppler maps, I could identify distinct signatures present in both of our swings, particularly the sharp Doppler transition when the club changes direction from backswing to follow-through and the concentrated burst at ball impact. What was not immediately clear, however, was which body parts were responsible for which features in the data. To answer that, I attached angle reflectors, devices composed of three mutually perpendicular surfaces that reflect waves directly back to their source regardless of incident angle, to specific body parts and recaptured the data. Because these reflectors produce strong returns in the spectrogram, I could isolate and label the contributions of the hands, elbows, shoulders, torso, hips, and knees. In total, I collected data on over 1,500 swings, each accompanied by a spectrogram, a range-Doppler map, a range profile, and a slow-motion video for reference.

Across all three angles, 45° produced the richest data. The 45° radar placement captures both the vertical component of motion, the forward-and-backward movement of the club and body, and the horizontal component, the left-to-right motion through the swing arc. At 0°, the vertical motion was largely absent from the spectrograms, and at 90°, the horizontal motion dropped out. The 45° angle preserved both, yielding spectrograms with the optimal signal strength and the most visually distinct features. However, while these spectrograms reveal clear signatures of the overall swing, dissecting them for features that are specifically indicative of individual swing faults will require data from a much wider range of golfers. Going into next week, I am working on recruiting players of varying skill levels and swing styles to expand and validate the dataset.