LiDAR uses electromagnetic (EM) waves in the optical and infrared spectrum and functions as an active sensor, emitting and receiving its own signals. Operating at much shorter wavelengths than microwave radar, LiDAR offers higher angular resolution but is limited by environmental factors like fog or clouds. Unlike passive electro-optical (EO) sensors, which rely on external radiation, LiDAR generates its own, enabling it to function effectively at night using near-infrared wavelengths. Passive EO sensors struggle with nighttime performance in the infrared due to insufficient available radiation.
LiDAR’s use of shorter wavelengths provides superior resolution and allows continuous 24-hour operation. The EM spectrum shows that LiDAR’s visible and infrared wavelengths are shorter than radio waves and microwaves but longer than X-rays and gamma rays. On a logarithmic scale, this difference in wavelength is substantial.
For example, a typical tracking microwave radar operates at 10 GHz with a 3 cm wavelength (X-band radar), while an eye-safe LiDAR operates at 200 THz with a wavelength of 1.5 μm—about 20,000 times smaller than X-band radar. This results in a much higher carrier frequency. X-rays and gamma rays, in comparison, have even shorter wavelengths and higher frequencies than visible and infrared EM radiation.
Microwave radar engineers use frequency, while optical engineers focus on wavelength, both linked by the formula c = λν. Fog particles are typically 1 to 100 µm, and raindrops range from 0.5 to 5 mm in diameter. Microwave signals (30 cm or 3 cm wavelengths) are not heavily affected by fog or rain. However, LiDAR, with a 1.5 µm wavelength, is highly scattered by clouds, fog, and rain. Millimeter-wave radar (95 GHz, 3.16 mm wavelength) can penetrate fog but is partially blocked by rain. LiDAR struggles to penetrate through clouds and fog due to signal scattering.
LiDAR, particularly a 10-µm variant, faces significant challenges in penetrating clouds or fog, as particle sizes often exceed its wavelength, causing signal attenuation. When compared to passive electro-optical (EO) sensors, which rely on blackbody radiation for signal input, LiDAR benefits from generating its own radiation, thus avoiding reliance on background noise. Blackbody radiation remains a primary noise source for passive EO systems and will be addressed in more detail when discussing receivers.
LiDAR serves as a powerful imaging tool, capable of both 2D and 3D imaging. In 3D imaging, voxels (3D pixels) help measure intensity and color variations in reflected light, while velocity can be assessed via Doppler shift. Coherent LiDAR systems offer improved precision by comparing the return signal against a local oscillator, providing critical phase information.
Speckle, a common issue with coherent LiDAR, results from light interference and produces patterns of dark and bright spots, although speckle can be averaged out or used to enhance data interpretation.
LiDAR has extensive applications in both military and civilian sectors due to its ability to detect various observable features. These features fall into five categories: 1) geometry, 2) surface character, 3) plant noise, 4) effluents, and 5) gross motion. Geometry involves measuring the shape and intensity distribution of an object in one to three dimensions, while surface character includes details like roughness, scattered energy distribution, and polarization properties. Plant noise refers to vibrations and cyclical motions from moving parts, such as engines, and effluents include emissions like exhaust gases. Gross motion covers movements like translation, rotation, or articulation.
LiDAR’s ability to sense at wavelengths similar to human vision makes it an effective tool for identifying objects. Unlike radar, which suffers from specular reflection and bright spot patterns, LiDAR provides clear imaging in visible wavelengths, helping with easy object identification. Passive sensors are limited by available radiation, but LiDAR’s active illumination allows for imaging in low-light or nighttime conditions. This capability also enhances resolution due to shorter wavelengths, giving LiDAR better diffraction-limited resolution than passive systems, especially in poor lighting. LiDAR’s capacity to provide detailed, real-time images makes it invaluable for diverse applications across industries.
LiDAR offers advantages over passive EO sensors due to its ability to control illumination, producing 2D and 3D images with high detail. It can measure range in each pixel by controlling light emission timing, allowing it to generate both grayscale and color images. Coherent LiDAR accurately measures velocity and detects vibration modes through laser vibrometry. Reflective properties and speckle effects can influence the LiDAR return. Advanced techniques like synthetic-aperture and multiple-input, multiple-output (MIMO) allow for the creation of high-resolution images by assembling larger pupil plane images through multiple measurements. LiDAR’s versatility makes it a powerful sensing tool.
