In today’s blog post, we will discuss the Fourier Transform—what it is and why it is important for lidar.
The Fourier Transform is one of the most widely used mathematical tools across various disciplines. The more you look for the Fourier Transform, the more you see it. Applications include signal processing, quantum mechanics, data compression (e.g., JPEG and MP3), 5G technology, and—of course—lidar!
The Fourier Transform is a mathematical operation that decomposes a complex signal into its constituent frequencies, transforming it from the time domain (how a signal changes over time) to the frequency domain (the frequencies present and their amplitudes). This transformation represents the signal as a sum of sinusoidal waves (sines and cosines) of varying frequencies, amplitudes, and phases. It provides critical insights into the frequency content of a signal and forms the foundation of many technologies.
For a more visual understanding of the Fourier Transform, I highly recommend the 3Blue1Brown YouTube channel. Their video on the Fourier Transform provides one of the clearest and most engaging explanations available. Check it out below:
The Fourier Transform plays a vital role in Frequency-Modulated Continuous Wave (FMCW) lidar. FMCW systems emit a laser signal with a continuously varying frequency (a “chirp”) and measure the frequency shift of the reflected signal. This shift, known as the beat frequency, is directly proportional to the distance and velocity of the object. By applying the Fourier Transform to the mixed signal (a combination of the reflected and reference signals), lidar systems isolate the beat frequency and extract both range and Doppler velocity information. This makes FMCW lidar particularly powerful for applications requiring precise motion detection, such as autonomous vehicles and robotics. The Fourier Transform also reduces computational complexity, enabling efficient real-time data processing.
In Time-of-Flight (ToF) and full waveform lidar systems, the Fourier Transform is not central to the primary distance measurement process, which typically relies on time-domain analysis of laser pulses. However, it is occasionally used in specialized applications, such as analyzing the frequency content of returned signals in full waveform lidar to characterize target properties or filter noise. In post-processing lidar point clouds, Fourier techniques may help identify periodic patterns, analyze spatial frequencies, or extract features like surface textures. These are niche applications, as most ToF lidar and point cloud workflows rely on geometric or statistical methods. Still, in scenarios where frequency-domain analysis offers unique advantages, the Fourier Transform becomes a valuable tool.
In lidar systems, the Fast Fourier Transform (FFT) is the most widely used implementation of the Fourier Transform due to its computational efficiency. The FFT is an algorithm that computes the Discrete Fourier Transform (DFT) quickly, making it ideal for applications like FMCW lidar, where rapid conversion of time-domain signals to the frequency domain is critical for distance and velocity calculations.
I hope you enjoyed this discussion about how one of math’s most elegant and practical tools, the Fourier Transform, applies to lidar. Don’t forget to subscribe for more enjoyable reads!
We’ve written several articles recently about frequency-modulated continuous-wave lidar. For two good reads, check out FMCW vs. ToF Lidar Battle Ramping Up and What is Frequency-Modulated Continuous Wave (FMCW) LiDAR?
