This alternative to time-of-flight LiDAR has unique complexities and distinct attributes.
Part one begins our look at what FMCW LiDAR is, the architecture and components it uses, and its performance capabilities.
Electronic and optical components build a system
Q: What hardware components are needed to create a basic FMCW LiDAR system?
A: One perspective is shown in Figure 1. Note that this is just one block-diagram component perspective. There are many other ways to draw a system block diagram, with emphasis on electronic components, optical components, signal processing, and more. However you look at it, an FMCW LiDAR is a complicated electro-optical system (as is the ToF version).
A: There are many: extremely low returned signal levels (just a few photons!); solar light (noise); signal dynamic range; rain and fog; deliberate spoofing and interference; target objects in proximity to each other; range resolution…it’s a long list. Many of these issues are not unique to FMCW but manifest themselves in different ways compared to ToF designs.
Q: How does FMCW deal with the obvious issue of sunlight and glare?
A: Unlike ToF, FMCW LiDAR is based on the measurement principle of using a coherent superposition of the return light and of its local copy. This enhances the immunity to random optical signals that may interfere, such as sunlight and other sources that are not coherent with the emitted light.
The coherent amplification of the detection path “amplifies” the return signal from the target, which is, by nature, very weak and consists only of a few photons. It also significantly reduces the impact of the noise floor from other components in the system, such as the detectors, and improves signal-to-noise ratio (SNR), a critical performance factor in any signal-processing system.
Q: What about the wavelength and power of an FMCW LiDAR?
A: The typical optical output power of FMCW LiDAR is below 100 milliwatts per FMCW channel, with continuous transmission at a wavelength of 1500 to 1600 nanometers. These values of power and wavelength have two important implications for the development of automotive and robotic vehicle systems.
First, the preferred wavelength and the absence of pulses of high peak power enable the use of Laser Class 1 eye-safe long-range sensors. Second, the low-power continuous-wave operation enables the integration of many of the optical components, including lasers, optical amplifiers, and low-cost detectors, in photonic integrated circuits (PICs), which leads to lower cost and smaller package size in the final system.
Seeing more and better
Q: How does an FMCW LiDAR “look at” a wide field of view?
A: In some ways, this problem is as challenging as the basic FMCW LiDAR system itself. Long-range LiDAR (FMCW or ToF) requires a minimum field-of-view (FoV) of about 100° × 20° (horizontal × vertical), combined with a resolution of about 0.05⁰ × 0.05⁰ (H × V) in the region of interest and a frame rate of at least 10 Hz. These combined requirements are very difficult to meet.
Mechanical scanning using a precision galvanometer, widely used in close-in laser systems, does not provide the needed performance, and has reliability issues in an application such as moving vehicles. Another approach is to use an optical phased array with multiple emitters, but this is costly and complicated to manage.
A more-attractive option is to use solid-state beam-steering technology in an optical array, using MEMS-based micro-mirrors. This offers the opportunity to develop a combination of a photonic integrated chip and an advanced optical system. The photonic integrated chip integrates a set of optical structures on silicon and processes light in a similar way to what electronic chips do with electrical signals.
Q: What about the processing of the reflected signals to create useful image information?
A: Not surprisingly, that aspect of either LiDAR approach is a major task, with sophisticated algorithms that must deal with the returned photon information, noise, artifacts, distortion, component imperfections, and more.
The next part looks at some of the modeling and simulation of these complicated and multifaceted systems.
References
Frequency-Modulated Continuous Wave (FMCW) LiDAR, Bridger Photonics
The battle of LiDAR sensor technologies: FMCW vs. ToF, Laser Focus World
FMCW LiDAR is the future of high-performance sensing, Laser Focus World
Time of Flight vs. FMCW LiDAR: A Side-by-Side Comparison, AEye, Inc.
SCANTINEL FMCW LiDAR, Scantinel Photonics
Scantinel Technology Overview, Scantinel Photonics
Understanding the magnificent FMCW LiDAR, Think Autonomous
How the Solid-State LiDAR works (and why everyone bets on it), Think Autonomous
LiDAR vs RADAR: How 4D Imaging RADARs and FMCW LiDARs disrupt the Autonomous Tech Industry, Think Autonomous
Performance analysis of the coherent FMCW photonic radar system under the influence of solar noise, Frontier Media
FMCW Radar Part 1 – Ranging, Wireless Pi
Secure FMCW LiDAR Systems with Frequency Encryption, University of Washington
An Overview of FMCW Systems in MATLAB, Texas Instruments
An Extended Simulink Model of Single-Chip Automotive FMCW Radar, Semantic Scholar
Aeva Atlas Long-Range Automotive-Grade 4D LiDAR, Aeva Inc
Aeva Introduces AevaScenes, the First Open-Access FMCW 4D LiDAR and Camera Dataset for Autonomous Vehicle Research, Aeva Inc
Related EEWorld Online content
LiDAR and Time of Flight, Part 1: introduction
LiDAR and Time of Flight, Part 2: Operation
LiDAR and Time of Flight, Part 3: Emitters, sensors, and scanners
LiDAR and Time of Flight, Part 4: Circuitry and advances
Tiny, all-in-one direct Time-of-Flight module targeted at advanced imaging applications
Laser driver IC targets lidar time-of-flight apps
Reference platform simplifies development of direct Time-of-Flight, LiDAR-based systems
The Doppler effect: From highly ridiculed to absolutely indispensable, Part 1
The Doppler effect: From highly ridiculed to absolutely indispensable, Part 2
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