Physics of sub-millimeter detection

The appeal of 60 GHz radar (part of the millimeter-wave (mmWave) spectrum) is not just that it is contact-free. It is that the band supports high-resolution motion detection in a compact form factor. With a wavelength of roughly 5 mm, a 60 GHz system can resolve sub-millimeter movement through phase changes in the reflected signal. Specifically, a chest displacement of just 0.25 mm corresponds to a 36-degree phase rotation, yielding a high signal-to-noise ratio for physiological tracking.

This capability is particularly relevant because many traditional monitoring methods are still either intermittent or intrusive. Clinical systems provide accuracy but depend on wired sensors and patient compliance. Wearables improve convenience but require constant charging and user acceptance. Radar offers a third way: passive, continuous sensing that operates entirely in the background. This is especially valuable in sleep monitoring and elder care, where it can provide long-term data on breathing patterns and restfulness while preserving privacy better than camera-based systems.

Navigating signal processing hurdles

Despite its promise, 60 GHz radar is not a plug-and-play solution for medical monitoring. The engineering challenge lies in signal extraction, not basic detection. Respiration is relatively easy to sense because the motion amplitude is large (millimeter scale) and the frequency is low. Heartbeat is significantly harder. The chest displacement associated with cardiac motion is much smaller and easily obscured by respiratory harmonics and intermodulation products.

In a practical engineering environment, robust performance depends almost entirely on the signal-processing pipeline. Engineers must implement static clutter removal (to ignore room reflections), I/Q mismatch compensation, and adaptive filters to isolate the cardiac signal from the respiratory “noise.” Furthermore, random body movements, such as a patient rolling over, can momentarily swamp the micro-motion signatures. Modern systems handle this by “gating” the data, which involves temporarily pausing vital-sign extraction during high-motion intervals to prevent false readings.

As antenna integration and digital signal processing (DSP) continue to advance, 60 GHz systems are becoming more spatially selective. Compact arrays now enable beamforming, allowing the radar to isolate a specific subject even when multiple people or moving objects are present in the room. While 60 GHz radar won’t immediately replace gold-standard medical instruments, its role as a tool for continuous, unobtrusive monitoring, especially for respiratory rate and sleep-related trends, is set to expand across the digital health landscape.

Editor’s note: This is an updated version of Can 60 GHz radar sensing change healthcare monitoring?

The post Why 60 GHz radar is finding a place in health monitoring appeared first on Sensor Tips.

This FAQ examines how these factors affect sensing integrity and provides strategies for system design.

Q: How does high-voltage EMI filtering affect ac voltage sensing accuracy?
A: In 800 V systems, EMI filtering is necessary to meet regulatory standards. Designers typically implement large Y-capacitors, sometimes reaching 100 nF, between high-voltage lines and protective earth. These components introduce a trade-off regarding ac voltage sensing accuracy due to the time constant they insert into the measurement loop.

Data from Figure 1 identify two primary error mechanisms:

1. Transient-state settling errors: During a transient event or at the start of a measurement cycle, the Y-capacitor must charge through the sensing resistor. This results in a settling time delay. If the system relies on rapid insulation monitoring, this delay can prevent the sensor from reading the true voltage in a timely manner.
2. Steady-state phase displacement: Because Y-capacitors have frequency-dependent impedance, they introduce a phase shift. As shown in Figure 1, the sensed voltage waveform leads the true voltage, creating a phase delay that can compromise the accuracy of insulation resistance calculations.

To improve accuracy, the resistance in the measurement network should be reduced. Lowering the resistance shortens the charge and discharge periods, which minimizes phase deviation and settling time.

Q: How do high dv/dt nodes dictate the physical placement of sensors?
A: WBG devices, such as SiC MOSFETs, switch at high speeds, generating common-mode noise due to high dv/dt. In these systems, physical placement must account for electromagnetic coupling between power loops and sensitive signal traces.

Figure 2 maps specific high dv/dt switching nodes within bidirectional ac/dc and dc/dc converters. These nodes act as sources of capacitive noise coupling.

Placement guidelines:

• Separation: Sensitive sensing signals, gate loops, and input/output connectors should not be placed near high dv/dt switching nodes.
• Shielding: In high-density designs where physical separation is limited, grounded heat sinks can be utilized as physical shields. These shields intercept capacitive noise before it reaches the sensing circuitry.

Q: How can PCB layout geometry mitigate high di/dt magnetic coupling?
A: GaN devices exhibit fast turn-on transients, often exceeding 100 kV/μs, which generate high di/dt changes. These changes cause magnetic coupling that can penetrate sensor isolation barriers. This results in radio frequency interference that rectifies within operational amplifiers, manifesting as a dc shift in the output signal.

Figure 3 provides a comparison of layout techniques:

• Figure 3a shows significant current sensor distortion caused by a layout with large, horizontal signal and ground loops.
• Figure 3b demonstrates clean waveforms achieved through an optimized physical layout.

Designers should implement minimized, vertical multi-loop areas. By stacking VCC, signal, and ground traces vertically across the main power board and control board, the system maximizes negative mutual-partial inductance. This effect cancels out the external magnetic coupling from the power switching loop, ensuring measurement integrity despite high di/dt transients.

Summary

Designing for 800 V and WBG architectures requires balancing aggressive EMI filtering with sensor performance. Large Y-capacitors introduce phase displacement and lag that must be mitigated by lowering sensing resistance.

Physical layout is equally important, as designers must isolate sensors from high dv/dt switching nodes or deploy grounded heat sinks for shielding. For high di/dt environments, implementing vertical, tightly stacked PCB multi-loops maximizes negative mutual inductance to cancel magnetic noise.

References

Impacts of High Frequency, High di/dt, dv/dt Environment on Sensing Quality of GaN Based Converters and Their Mitigation, IEEE
A Comparative Analysis of Insulation Monitoring Device (IMD) Architectures in Bidirectional Onboard Chargers, Texas Instruments
Mitigating EMI with SiC Solutions in Renewable Energy & Grid-Connected Power Converters, Wolfspeed

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Power management

Smartwatches, earbuds, and health monitor wearables typically operate on 300 to 1500 mWh batteries. For these and other wearable products, power management ICs (PMICs) must address challenges such as inefficient power conversion, limited battery life, and space constraints. In addition, many wearables require multiple voltage regulators to provide a variety of voltages.

Using a design technique called Single-Inductor Multiple-Output (SIMO) technology, a PMIC can have one shared inductor generate multiple independent and independently regulated output voltages from a single input. For example, one company used this approach in a PMIC designed specifically for wearables and ultra-portable devices. With its single inductor, it delivers three outputs at 91% efficiency and replaces three traditional converters and inductors in a 19 mm² footprint that is 50% smaller than the alternative.

As part of power management, two subcategories deserve special consideration: charging and energy harvesting.

Charging/energy harvesting

Wearables have unique design requirements for charging based on their small size that pose problems for physical connectors. The solution is wireless charging, and available design techniques provide choices for implementing it. As shown in Table 1, a variety of factors enter into the choice of the right one. The wireless connectivity is provided by inductive, magnetic resonance, and RF (electromagnetic) techniques.

For wireless charging, industry standards have been developed by the Wireless Power Consortium and the NFC Forum.  Leveraging the existing Near Field Communication (NFC) antenna for both communication and power transfer, the NFC Wireless Charging Specification (NFC WLC) can deliver low-power charging—up to 1 watt—over distances of about 2 cm. NFC charging relies on a small antenna that already exists in billions of devices for connectivity and authentication. In contrast, inductive or resonance systems require relatively larger coils/antennas.

The Wireless Power Consortium (WPC) created its Qi Standard strictly for wireless power transfer (WPT).

Originally an exclusive feature on the 2020 iPhone 12 line, after five years, key parts of Apple’s MagSafe wireless charging are now also available on Android phones because Apple allowed their incorporation into WPC’s open Qi2.2 standard.

Far-field wireless charging, where wearables are charged from across the room using radio waves, infrared light, or lasers, is a possible future approach for wearables.  Some startups have already demonstrated functional prototypes.

In addition to these established wireless techniques, researchers continue to investigate new approaches for wireless power transfer. For example, one group of university researchers used self-capacitance technology to remotely transfer wearer-generated power to the wearable or devices installed in hard-to-reach areas of the body. As shown in Figure 1, their system utilizes the self-capacitance of the person’s body to wirelessly transfer power from portions of the body that generate higher power density to energy-constrained wearable devices. Its inventors envision implementation in millimeter-scale wearables in the power range of ~10mW, in end-user applications such as:

• Hard-to-reach wearable technology (including smart contact lenses and mouth guards)
• Smart textiles and stitched sensors (sports performance monitors, flexible electronics/wearables)

Energy harvesting from different conversion technologies continues to be the design goal of many companies and researchers. The ultimate solution could be a combination of the two companies’ expertise. For example, working together, one company’s RF-based wireless charging technology combined with the other company’s energy harvesting technology that captures RF power promises to open up the market for wireless charging up to two meters away. The combined solution can be used for a diverse array of connected products for retail, industrial, and consumer applications, including ultra-small, location-flexible, RF charging for wearables, hearables, and low-power electronics.

Algorithms

As shown in Figure 2, one company has developed algorithms for wearables that use an Inertial Measurement Unit (IMU) input through a 9-axis sensor fusion algorithm based on accelerometer, gyroscope, magnetometer, and strong artificial intelligence (AI). It offers these algorithms to consumers for wearable and other applications. Specifically, the algorithms address fall monitoring, activity recognition, step counting, head posture control (left and right turn of head, nod, head shake), and 3D head tracking for an immersive audio experience.

By operating on most of the world’s well-known low-power microprocessors, single-chip and Bluetooth SoCs, the algorithm can collect large amounts of data based on multiple sensors and constantly improve results through machine learning to achieve higher accuracy and more stable motion recognition. Figure 3 shows the small footprint that can be achieved.

Other systems aspects

Other system design aspects that impact or promise to impact wearable products include the display, simulation technology, including digital twins, multi-physics, and more, to optimize board layout and address thermal limitations, faults, and more.

For more complete power-consuming consideration, the screens on displays/optical systems can consume 10 to 100 mW, while augmented reality (AR) optics/glasses often require <320 mW, and Global Positioning System (GPS) sensors can require 70 mW+ of power.

With general-purpose simulation software used in all fields of engineering, manufacturing, and scientific research, multi-physics system-level analysis allows designers to gain insights into how physics couplings influence overall system performance.

References

Revolutionizing Power Management For Wearable Power Management With SIMO Technology
Extending the Battery Life of Electronic Wearable Devices
Self-capacitance Power Transfer for Efficient Wireless Charging of Small Wearables | Washington University Office of Technology Management
Powering the Future of Wearables: Battery Charging Strategies
Wireless Charging 2.0: NFC Power for Wearables in 2026
Energous and Atmosic Achieve Industry First Interoperability Energy Harvesting Advancing Development of Wireless Charging Applications
CyweeMotion
The COMSOL Product Suite
What is Multiphysics?
Digital twin for personalized medicine development

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Sensors

Depending on the type(s) of sensors, their operating time (activity per hour), and other factors, sensors in wearables can easily consume from a few to several mW or more. However, some sensors can consume as little as a few microwatts. For higher power-consuming sensors and to minimize the power consumption of any sensor, there are design tips that provide designers a means of achieving the desired operational objectives. These design tips include the choice of sensor technologies for the design, controlling the amount of time the chip is powered, how frequently readings are made, and other factors.

Data processing (plus instruction language)

The next power-consuming aspect of any wearable design is the computing or data processing portion. The microcontroller unit (MCU)/ system on chip (SoC) computing portion generally operates within 0.507 µW to 216 µW, is often optimized for lower power in sleep modes, and has other lower power operating modes. For operation with analog sensors, the data converters (ADC/DAC) portion of an MCU/SoC can require from 15 µW to 1.95 mW.

In addition to the computing hardware, the instruction language may play a key role in reducing power consumption and the ability to deliver more features. For example, one company’s line of digital signal processors (DSPs) features its Tensilica Instruction Extension (TIE) language that enables designers to move data significantly faster than conventional processors. This results in a more efficient and less expensive SoC implementation that consumes less energy. For wearable applications such as True Wireless Stereo (TWS) earbuds, hearing aids, Bluetooth headsets, smart watches, and more, a recently introduced DSP delivers an enhanced audio/voice experience for small battery-powered products.

However, computing can also be an integral part of devices dedicated to communicating the data.

Communications

Another essential design aspect is communicating or transferring the data from the wearable to another device for the user or others (such as a healthcare provider in medical applications or a safety/security analyst in industrial applications) to monitor. Wireless data transmission (using Wi-Fi, Bluetooth Low Energy (BLE), LoRa, or others) requires significant power in wearables because radios can consume several mWs while transmitting/receiving. As shown in Table 1, in addition to the choice of low-power wireless technology, the maximum operating distance and data rate are key factors to determine power consumption.

To improve the functionality of their low-power wireless connectivity products, semiconductor suppliers continually introduce new designs. For example, one supplier recently introduced the fifth addition to its next-generation series of wireless SoCs. It integrates a 2.4 GHz radio for low-voltage Bluetooth LE applications, a 128 MHz processor, a coprocessor, and essential peripherals to support a 1.2-1.7 V supply voltage range. In addition to providing a sub-50 nA system hibernation mode for shipping and storage, its power consumption is 30 to 50 percent lower in common Bluetooth LE use cases, compared to its predecessor.

References

Power Management in Wearable Electronics: Strategies for Longer Life and Smarter Designs
A Survey on Smart Wearable Devices for Healthcare Applications
Extending the Battery Life of Electronic Wearable Devices
Tensilica Instruction Extension (TIE) Language
New Tensilica HiFi 1 DSP delivers increased voice- and music-processing performance with optimal neural network capability in a compact footprint with ultra-low energy
Sensors, Batteries, and Low-Power Design for Wearable Devices in Medical Applications
Nordic Semiconductor unveils nRF54LV10A – a breakthrough low-voltage Bluetooth LE SoC for next-gen healthcare wearables

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The WSEN-PDMS from Würth Elektronik is a differential pressure sensor with ±1 mbar accuracy and 16-bit resolution, supporting 3.0 V to 5.5 V operation with I²C, SPI and analog interfaces. The sensor provides calibrated pressure data with optional temperature output, operates from −25°C to +85°C and includes CRC support for reliable communication. Available with multiple nozzle configurations, it is designed for HVAC, industrial automation, gas detection and medical devices such as inhalation systems.

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For more critical situations/procedures, such as intravenous (IV) insertions and more, near-infrared (NIR), ultrasound sensing techniques, or transillumination technology are frequently used. Design differences provide medical professionals with options for selecting one technology over the other.

NIR vein finder technology

A portable NIR vein viewer consists of one laser diode emitting infrared light and a second laser diode emitting only visible wavelengths. Blood vessels absorb a portion of the infrared light, reflect a contrasted infrared image, and transmit a corresponding signal through a pair of silicon PIN photodiodes.

With circuitry to amplify, sum, and filter the output signals, as well as the use of an image processing algorithm, the second laser diode projects the contrasted image onto the patient’s skin surface. The projected image provides vein location, depth, diameter, and the degree of certainty of vein locations. One company’s portable vein finder’s capabilities are shown in Table 1. It uses six different color lights to be able to detect the target vein through a wide range of patient skin colors.

Ultrasound vein finder technology

Ultrasound imaging or sonography that takes advantage of the Doppler effect uses a piezoelectric transducer element that converts electric signals into acoustic waves. High-frequency sound waves travel from the probe through a gel placed on the skin into the body. The probe also collects the sounds that bounce back for a system processor (an MPU or MPU+DSP) to create an image. Since ultrasound captures images in real-time, it can show the structure and movement of the body’s internal organs as well as blood flowing through blood vessels. As shown in Figure 1, other circuitry in a typical system includes a multi-channel analog front-end, FPGA transmit and receive beamformers, a DAC pulser, a transmit/receive (T/R) switch, and a multiplex/demultiplex (MUX/DEMUX) interface to the piezoelectric transducer.

Transillumination vein finder technology

For a transillumination vein finder, high-intensity LED lights illuminate tissue from below, as shown in Figure 2. Since blood absorbs the light, veins appear as dark lines within the illuminated area. One transillumination product designed primarily for vein access includes a series of 24 dual-colored light emitting-diodes (LEDs).

Another transillumination design includes at least two sets of LEDs of two or more different colors arranged in a light head placed that is against a patient’s skin. As shown in Figure 3, electronic circuitry coupled to the light head selectively operates the LEDs in two or more user-selected modes, providing the ability to adjust the relative intensities of the different colors to best suit the skin color and vein depth of the patient.

Choosing the right technology

Among the criteria for selecting the right vein finder, penetration depth, the ability to obtain vein access in patients with underlying problems such as developmental venous anomaly (DVA), and the ability to allow the clinician to customize the image to their environment appear to be critical. Transillumination, while less accurate than ultrasound or NIR, since it uses simple LEDs for illumination instead of lasers or a piezoelectric transducer, is easy to use and less costly as well.

References

Phlebotomist Technologists & Technicians
Patent No.: US 8,463,364
Near-infrared (NIR) vein finders
Lite 2.0 + Stand
Ultrasound transducer
VeinViewer illumination technology
Venous Ultrasound
US20120101343A1 – Medical imaging device – Google Patents

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Q: Can you buy FMCW components or a complete system today?
A: Absolutely. There are many companies offering one or both paths to a complete system. For example, the Aeva CoreVision Lidar-on-Chip Technology of Figure 1 with a 500-meter range has an integrated silicon photonics module. The module incorporates all key FMCW LiDAR elements, including transmitter, detector, and a new optical processing interface chip. Proprietary integrated laser and receiver electronics enable better integration and lower costs, and it is designed to strict automotive standards.

Q: What does the future hold for FMCW versus ToF LiDAR systems in cars and other applications?
A: In short, no one knows. Some say one system will win out within the next few years, others feel it’s too soon to say, and others believe the ultimate solution will be to use both to overcome their individual weaknesses. Adding one or both will affect car affordability, of course, but should ease the path to fully autonomous vehicles.

Q: What is the presumed path forward?
A: Not surprisingly, it’s largely about integration to reduce cost and size while enhancing reliability. Keys to cost reduction, miniaturization, and overall optimization are the integration of the FMCW engine. This engine is the heart of the system and must emit a narrow laser linewidth (resulting in long coherence length) with highly linear modulation of the laser wavelength to generate the chirp signal. There’s great progress in this area, but more to be done.

Much of the development work is focused on a hybrid integration of Group III/V materials on silicon, along with on-chip semiconductor optical amplifiers (SOAs). This would also allow the photonic integrated circuits (PICs) to use standard CMOS foundries, with all the advantages that brings.

Conclusion

The competition between ToF and FMCW approaches to LiDAR is busy and ongoing. The many advances in merging optical and electronic components will help define which has lower cost and more practicality, while component specifications and associated signal processing will determine which provides the necessary level of performance.

In either case, these LiDAR systems are complicated and must accommodate many uncontrollable aspects, variables, and corner cases. The rewards of being the winner in the automotive and robotic market for LiDAR are significant. The considerable research and development, much from innovative small-to-medium companies acting on their own or in partnership with larger companies, will be a great story for a technology historian to cover—but not yet.

There is much more that can be said about FMCW technology, components, electro-optical R&D, and the market situation. The References below cover these topics and more.

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

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Modeling and simulation

Q: What design analysis is done on an FMCW LiDAR system?
A: In a word: lots. Much of the analysis parallels or leverages what has been done for radar, as there are so many similarities in operating principles and the analytical equations.

One starting point is the link budget, as is done with any signal path or channel. A high-level simplified link budget is shown in Figure 1.

More highly detailed budgets are then worked out at each point in the link, as seen in Figure 2.

Q: What do more advanced and detailed models look like?
A: One MATLAB Simulink model is shown in Figure 3.

However, this model only tells part of the multifaceted story. The reason is that FMCW is a hybrid electro-optic system, so the modeling must accommodate electronic-only, optical-only, and integrated electro-optical components. This is a major challenge among many challenges in providing realistic and accurate modeling and simulation.

Which is better: FMCW or ToF LiDAR?

Q: What are some of the presumed advantages of FMCW over ToF LiDAR?
A: Proponents of FMCW make these points, but ToF advocates dispute them, or say the considerations are not clear-cut or still to be decided: FMCW proponents say it offers:

• Improved range resolution, enabling the measurement and separation of multiple closely spaced surfaces.
• Improved dynamic range, enabling the measurement of both bright and dim objects simultaneously.
• Single-photon sensitivity, enabling small apertures, long-range operation, and the ability to penetrate obscurants.
• Velocity sensitivity enables the ability to detect and quantify motion.

Q: What is a major point that ToF proponents claim is their strength and an FMCW weakness?
A: One of the claims is that FMCW does not work well at detecting lateral motion (across the image plane) since there is little or no Doppler shift. Note, there are many other legitimate performance points that ToF advocates raise as well, in addition to cost and complexity; some of the References call these out from the ToF-advocate side, and others do side-by-side comparisons – so you have a lot to consider and weigh!

The final part looks at today’s reality and the possible future(s).

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

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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).

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
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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 

The post FAQ on the basics of FMCW LiDAR: part 2 appeared first on Sensor Tips.

When engineers hear the phrase LiDAR, an acronym for Light Detection and Ranging, they generally think of time-of-flight (ToF) systems. ToF LiDAR is widely used in automotive and robotic applications to “see” surroundings and obstacles.

But there is another, radically different LiDAR approach: frequency modulated continuous wave or FMCW LiDAR. This approach is derived from many of the principles of conventional RF-based radar, but modified extensively to be used with light instead of RF.

“Which is better?” is a contentious question. Practical FMCW systems are relatively newer than ToF LiDAR, but their proponents maintain that their time has come, while adherents of ToF maintain that their approach is superior. As usual, it is not a clear-cut argument, as each has strong and weak points compared to the other.

The right answer depends to some extent on which characteristics are most important to the user in the intended application. In addition, the underlying technologies are rapidly evolving, so the relative merits at any given time are in flux.

This FAQ will not attempt to resolve the “which approach is better where and when?” question. Instead, it will look at what FMCW LiDAR is, the architecture and components it uses, and its performance capabilities.

Q: What is the operating principle of FMCW LiDAR?
A: In FMCW, a laser diode is used to send out a continuous frequency-modulated beam towards the target area, seen in Figure 1.  A receiving sensor co-located with the source captures the light-return signal (it’s often just a few photons) and uses the well-known Doppler shift to near-instantly determine where the objects are and how fast they are moving relative to the system.

A small fraction of the carrier is diverted to the receiver channel to enable coherent synchronous demodulation. Again, this is an optical expansion of well-known radar and demodulation principles.

Q: Does FMCW LiDAR create a 3-D image?
A: No, it creates what is called a 4-D image encompassing three-dimensional space along with velocity imaging of the scene.

Q: Why is it considered to be frequency modulated, as there doesn’t seem to be any conventional modulation here?
A: In traditional FM broadcast radio, a continuous signal, such as voice or music, is imposed on the carrier and modulates its frequency. In many radar systems, the frequency of the carrier is instead modulated by a “chirp” (compressed high-intensity radiated pulse), a pulse-compression signal where the frequency increases (up-chirp) or decreases (down-chirp) over the duration of the pulse, shown in Figure 2.

Instead of a single frequency, the chirp sweeps a range of frequencies to improve range resolution and target detection while keeping power usage low. Due to the relatively fast transitions of the modulating signal, it creates a wide range of frequencies as it modulates the carrier.

Q: What about the “continuous wave” aspect? The chirping does not seem continuous.
A: Again, in a parallel to radar and even broadcast FM, the carrier is on all the time and thus continuous. It’s the modulating waveform that is not continuous.

The next part looks at the electronic and optical components needed to build an FMCW LiDAR system.

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 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

The post FAQ on the basics of FMCW LiDAR: part 1 appeared first on Sensor Tips.

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