Lidar System Research Articles

Profiling of particulate matter transport flux based on dual-wavelength lidar and ensemble learning algorithm.

Transport flux (TF) is a significant particulate matter (PM) characteristic. This paper introduces an advanced dual-wavelength polarization aerosol and wind lidar (Wind Flux 3000) capable of independently observing the PM TF. The PM TF observation capability, which allows for simultaneous aerosol and wind measurements, was achieved by integrating a Mie-polarization particle lidar module and a coherent Doppler wind lidar module into a single lidar system. The primary measurement products of the Wind Flux 3000 include particulate extinction coefficient at 532 nm and 1550 nm, volume linear depolarization ratio at 532 nm (δp,532), wind speed (WS), wind direction (WD), vertical speed (VS), turbulence intensity (TI) and mixing layer height (MLH), retrieved by physical and proven algorithms. The PM concentration scales with the measured optical parameters and is also impacted by other environmental or meteorological parameters. Under the framework of the potential relationship between the PM concentration and the above parameters, the PM2.5 and PM10 concentration retrieval models were established using the stacking method of the ensemble learning technique; the models were trained using the in-situ data as true values, while the signal-to-noise ratio (SNR) at 1550 nm, δp,532, WS, WD, VS, the standard deviation of VS, TI, MLH provided by the Wind Flux 3000, as well as the relative humidity and temperature from ERA5, the hours of the day, and a "dust day" flag were used as inputs. The R2, RMSE, and MAE for the comparison between the predicted and true values of the PM2.5 test set are 0.857, 13.52 µg · m- 3, 9.16 µg · m- 3, and those of the PM10 test set are 0.926, 24.75 µg · m- 3, 14.39 µg · m- 3, respectively. The performance of the PM2.5 and PM10 concentration retrieval ensemble models is better than that of individual machine learning algorithms and better than that of the linear model. On 15th March 2023, a strong southeastward dust transport process with dust plume deposition was observed at Qingdao by the Wind Flux 3000. The analyses of the dust event demonstrated the Wind Flux 3000's ability to evaluate the transports of PM quantitatively.

Cascaded-prism multi-mode beam scanning method for three-dimensional imaging lidar

Light detection and ranging (lidar) has emerged as an indispensable approach to three-dimensional (3D) perception, which probably suffers from performance limitations with traditional beam steering devices. In this paper, we investigate a cascaded-prism beam scanning method to enhance the versatility of 3D imaging lidar systems. The cascaded-prism-based 3D lidar architecture is theoretically developed with an emphasis on a rigorous beam scan model. By exploiting the additional flexibility of cascaded prisms, the lidar can achieve beam scanning through prism rotation in either step-motion or constant-speed mode, which exhibits superior agility and adaptability in multi-mode pattern analysis. Moreover, the cascaded-prism beam scanning lidar is demonstrated with a 3D imaging performance evaluation in terms of field of view, angular resolution and sampling density. It proves that the cascaded-prism beam scanner can offer lidar systems with flexible and configurable 3D imaging capability while balancing between a wide field of view and high angular resolution.

Fruit Position, Light Exposure and Fruit Surface Temperature Affect Colour Expression in a Dark-Red Apple Cultivar

This study aimed to evaluate the effects of fruit position, light exposure and fruit surface temperature (FST) on apple fruit colour development and fruit quality at harvest, including sunburn damage severity. This was achieved by undertaking two experiments in a high-density planting of the dark-red apple ANABP 01 in Tatura, Australia. In the 2020–2021 growing season an experiment was conducted to draw relationships between fruit position and fruit quality parameters. Here, sample fruit position and level of light exposure were respectively determined using a static LiDAR system and a portable quantum photosynthetically active radiation (PAR) sensor. At harvest the sample fruit were analysed for percentage red colour coverage, objective colour parameters (L*, a*, b*, hue angle and chroma), sunburn damage, fruit diameter (FD), soluble solids concentration (SSC), flesh firmness (FF) and starch pattern index (SPI). A second experiment was conducted in the 2021–2022 growing season and focused on how fruit shading, light exposure and the removal of ultraviolet (UV) radiation affected the FST, colour development and harvest fruit quality. Five treatments were distributed among sample fruit: fully shaded with aluminium umbrellas, shaded for one month and then exposed to sunlight until harvest, exposed for one month and then shaded until harvest, covered with a longpass UV filter and a control treatment. The development of colour in this dark-red apple cultivar was highly responsive to aspects of fruit position, and the intensity and quality of light exposure. The best-coloured fruit were exposed to higher quantities of PAR, exposed to both PAR and UV radiation simultaneously and located higher in the tree canopy. Fruit that were fully exposed to PAR and achieved better colour development also displayed higher FST and sunburn damage severity.

Comparing triple and single Doppler lidar wind measurements with sonic anemometer data based on a new filter strategy for virtual tower measurements

Abstract. In this study, we compare the wind measurements of a virtual tower triple Doppler lidar setup to those of a sonic anemometer located at a height of 90 m above ground on an instrumented tower and with those of two single Doppler lidars to evaluate the effect of the horizontal homogeneity assumption used for single Doppler lidar applications on the measurement accuracy. The triple lidar setup was operated in a 90 m stare and a step–stare mode at six heights between 90 and 500 m above ground, while the single lidars were operated in a continuous scan velocity–azimuth display (VAD) mode where one of them had a zenith angle of 54.7° and the other one of 28.0°. The instruments were set up at the boundary-layer field site of the German Meteorological Service (DWD) in July and August of 2020 during the FESST@MOL (Field Experiment on sub-mesoscale spatiotemporal variability at the Meteorological Observatory Lindenberg) 2020 campaign. Overall, we found good agreement of the lidar methods for the whole study period for different averaging times and scan modes compared to the sonic anemometer. For the step–stare mode wind speed measurements, the comparability between the triple lidar and the sonic anemometer was 0.47 m s−1 at an averaging time of 30 min with a bias value of −0.34 m s−1. For wind speed measured by one single lidar setup for the same period with an averaging time of 30 min, we found a comparability of 0.32 m s−1 at an averaging time of 30 min and a bias value of −0.07 m s−1 as well as values of 0.47 and −0.34 m s−1 for the other one, respectively. We also compared the wind velocity measurements of the single and triple lidars at different heights and found decreasing agreement between them with increasing measurement height up to 495 m above ground for the single lidar systems. We found that the single Doppler lidar with the increased zenith angle produced poorer agreement with the triple Doppler lidar setup than the one with the lower zenith angle, especially at higher altitudes. At a height of 495 m above ground and with an averaging time of 30 min the comparability and bias for the larger zenith angle were 0.71 and −0.50 m s−1, respectively, compared to values of 0.57 and −0.28 m s−1 for the smaller zenith angle. Our results confirm that a single Doppler lidar provides reliable wind speed and direction data over heterogeneous but basically flat terrain in different scan configurations. For the virtual tower scanning strategies, we developed a new filtering approach based on a median absolute deviation (MAD) filter combined with a relatively relaxed filtering criterion for the signal-to-noise ratio output by the instrument.

Enhancing point cloud data fusion through 2D thermal infrared camera and 2D lidar scanning

Sensor data fusion from multiple sources plays a pivotal role in advancing our understanding of real-world phenomena. This paper focuses on the integration of a thermal imager and a lidar to simultaneously capture four-dimensional (XYZT) space coordinates and temperatures. The fusion of data relies on establishing correspondence between overlapping lines in the lidar’s 3D point cloud and the infrared (IR) camera’s 2D thermal map. The presented combination method offers several key advantages. Firstly, it simplifies the calibration process. Furthermore, it can achieve near real-time performance, enabling prompt data analysis. Moreover, the method demonstrates high accuracy in capturing spatial information and temperature measurements. The thermal IR camera used in the experiment features a 1280 × 1024 pixel cooled indium antimonide (InSb) detector, operating in the short wavelength infrared (MWIR) spectral range of 1.5 to 5.5 µm. It provides temperature readings with ± 1 K accuracy and sub-30 mK resolution. On the other hand, the lidar system delivers centimeter-level precision within a range of approximately 1 to 12 m, even in well-lit outdoor environments. The experimental results validate the effectiveness of our combined system, successfully generating high-quality four-dimensional (4D) point clouds. The integration of the thermal IR camera and profilometer presents an intriguing tool with significant potential in various industrial applications. For instance, it can be employed to accurately assess gas leaks, enhance metallurgical processes, and improve safety on construction sites.

Integrated Resonant Electro‐Optic Comb Enabled by Platform‐Agnostic Laser Integration

AbstractThe field of integrated photonics has significantly impacted numerous fields including communication, sensing, and quantum physics owing to the efficiency, speed, and compactness of its devices. However, the reliance on off‐chip bulk lasers compromises the compact nature of these systems. While silicon photonics and III‐V platforms have established integrated laser technologies, emerging demands for ultra‐low optical loss, wider bandgaps, and optical nonlinearities necessitate other platforms. Developing integrated lasers on less mature platforms is arduous and costly due to limited throughput or unconventional process requirements. In response, a novel platform‐agnostic laser integration technique is proposed, utilizing a singular design and process flow, applicable without modification to a diverse range of platforms. Leveraging a two‐step micro‐transfer printing method, nearly identical laser performance is achieved across platforms with refractive indices between 1.7 and 2.5. Experimental validation demonstrates strikingly similar laser characteristics between devices processed on lithium niobate and silicon nitride platforms. Furthermore, the integration of a laser with a resonant electro‐optic comb generator on the thin‐film lithium niobate platform is showcased, producing over 80 comb lines spanning 12 nm. This versatile technique transcends platform‐specific limitations, facilitating applications like microwave photonics, handheld spectrometers, and cost‐effective Lidar systems, across multiple platforms.

Assessment of traffic sign retroreflectivity for autonomous vehicles: a comparison between handheld retroreflectometer and LiDAR data

This study investigates the critical role of retroreflectivity in traffic signs, particularly in the context of autonomous vehicles (AVs), where accurate detection is paramount for road safety. Retroreflectivity, influencing visibility and legibility, is essential for ensuring safe road conditions. The study aims to assess traffic sign retroreflectivity using handheld retroreflectometers and LiDAR data, offering a comprehensive comparison of results with a specific focus on the RA1 and RA2 traffic sign classes. In a real-world setting, an AV equipped with LiDAR sensors, GPS units, and a stereo camera collects data on traffic signs, including point cloud attributes such as intensity and density. Simultaneously, a handheld retroreflectometer measures retroreflectivity coefficients from identified traffic signs. While retroreflectometers provide precision, they face limitations regarding time-consuming measurements and handling large or elevated signs. In contrast, LiDAR systems efficiently evaluate retroreflective features for numerous signs without such constraints. Despite both methods consistently yielding accurate retroreflectivity, the study reveals a limited correlation between LiDAR point cloud data and handheld retroreflectivity coefficients. The implications of these findings are significant, particularly in the selection and maintenance of retroreflective materials in traffic signs, with direct repercussions on overall road safety. The results offer valuable insights into leveraging LiDAR technology to enhance AVs' detection capabilities. Recommendations for further research include exploring factors influencing LiDAR intensity, establishing a more accurate relationship between intensity and retroreflectivity, correcting the point cloud during intensity calibration, and testing empirical prediction models with a larger sample size. These endeavors aim to generate a robust regression graph and determine correlation coefficients, providing a more nuanced understanding of the intricate relationship between LiDAR data and handheld retroreflectivity coefficients in the context of traffic sign assessment.

Apodized slanted teeth grating couplers for LiDAR applications

Solid state LiDAR systems traditionally rely on costly active components for efficient beam scanning. In this study, we propose a cost-effective, purely passive steering approach using apodized slanted grating couplers. Through apodization, we achieve a uniform upward emission profile and enhanced upward transmission. Theoretical calculations indicate successful steering of 91.5∘×42.8∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$91.5^\\circ \ imes 42.8^\\circ$$\\end{document}. Experimental results closely match theoretical predictions, validating the capabilities of our passive steering concept. Additionally, the grating couplers, with a length of 3 mm, enable a farfield FWHM of 0.026∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document}, further enhancing sensing resolution.

Individual Tree Identification and Segmentation in Pinus spp. Stands through Portable LiDAR

Forest inventories are essential for sustainable forest management. In inventories at the tree level, all the information is linked to individuals: species, diameter, height, or spatial distribution, for example. Currently, the implementation of Portable LiDAR (PLS) is being studied, aiming to digitalize forest environments and increase the reliability of forest observations. Performing automatic individual tree identification (ITD) and segmentation (ITS) is essential for the operational implementation of PLS in forestry. Multiple algorithms have been developed for performing these tasks in LiDAR point clouds. Their performance varies according to the LiDAR system and the characteristics of the stand. In this study, the performance of several ITD and ITS algorithms is analyzed in very high-density PLS point clouds in Pinus species stands with a varying presence of understory, shrubs, and branches. The results showed that ITD methods based on finding trunks are more suitable for tree identification in regular stands with no understory. In the ITS process, the methods evaluated are highly conditioned by the presence of understory and branches. The results of this comparison help to identify the most suitable algorithm to be applied to these types of stands, and hence, they might enhance the operability of PLS systems.

Bayesian neuromorphic imaging for single-photon LiDAR.

This paper proposes a Bayesian approach to enable single photon avalanche diode (SPAD) arrays to be used as pseudo event cameras that report changes in the scene. Motivated by the working principle of event cameras, which produce sparse events associated with light flux changes, we adopt a changepoint detection strategy to generate intensity and depth change event streams from direct time-of-flight (dToF) sequences measured by SPAD arrays. Although not our main goal, the algorithm also produces as a by-product, intensity and depth estimates. Unlike the output of passive event cameras that only correspond to light flux changes, the change events detected from the sequential dToFs can relate to changes in light flux and/or depth. The integration of the proposed Bayesian approach with single-photon LiDAR (SPL) systems provides a novel solution to achieve active neuromorphic 3D imaging that offers the advantages of significantly reduced output redundancy and in particular the capacity to report scene depth changes. For each pixel of the SPAD array, asynchronous events are generated by performing online Bayesian inference to detect changepoints and estimate the model parameters simultaneously from individual single-photon measurements. Experiments are conducted on synthetic data and real dToF measurements acquired by a 172×126 pixel SPAD camera to demonstrate the feasibility and efficiency of the proposed Bayesian approach.

Characterization of the offshore wind dynamics for wind energy production in the Gulf of Lion, Western Mediterranean Sea

A ground-based wind lidar system has been deployed on a coastal landmass off Marseille in the Gulf of Lion, western Mediterranean Sea. This location holds significant relevance as it anticipates the imminent deployment of floating wind farms. Over the course of one year, a comprehensive wind dataset was collected by a lidar profiler at altitudes ranging from 60 to 220 m above sea level, aiming to assess the wind resource potential. This study presents an analysis of monthly wind metrics, including key parameters such as wind speed, wind shear exponent, and turbulence intensity. Weibull distributions are provided, along with their scaling and shaping parameters, to elucidate the probability distribution of wind speeds. The focus extends to the diurnal variation of wind metrics and monthly wind speed vertical profiles. Our results indicate that, at an altitude of 140 m above sea level, the one-year average wind speed is 8.3 m/s, with a turbulence intensity (TI) of 13.0 %. The mean wind shear exponent is quantified at 0.077. Notably, the highest wind speeds are consistently observed between 12:00 and 15:00, coinciding with lower values of TI and wind shear exponent, while night-time reveals high levels of both TI and wind shear exponent. Furthermore, an examination of wind direction unveils a bi-modal pattern with a slight variation, specifically wind veer, at each measurement height relative to the 140 m altitude. Additionally, our study explores low-level jets (LLJs), revealing that 80 % of these phenomena occur at altitudes below 140 m, predominantly during the summer period. The mean LLJ core speed, averaged across all measurement heights, is found to be 8.4 m/s.

Improved indirect time of flight (i-ToF) measurements for LiDAR with and without gain on the avalanche photodetector (APD)

Switching modulation involving two or more frequencies exhibits outstanding performance for indirect time-of-flight (i-ToF) LiDAR to address a problem with resolution and ambiguity range. However, the accuracy and precision remain issues when a target is far from the system due to a poorly received signal. This work improves a LiDAR system by introducing adjustable gain on the avalanche photodetector (APD) to enhance the accuracy and precision. The system was experimentally characterized by measuring a target at 5, 25, and 45 meters with gain settings of maximum, middle, and no gain. The middle and maximum gain settings improved the signal-to-noise ratio (SNR), accuracy, and precision. Furthermore, the proposed system may be appropriate for environmental remote-sensing systems, such as mapping and autonomous navigation.

Self-homodyne coherent Lidar system for range and velocity detection.

A high resolution FMCW Lidar system based on a phase-diverse self-homodyne coherent receiver is demonstrated. Using the same linearly chirped waveform for both the transmitted lidar signal and the local oscillator, the self-homodyne coherent receiver performs frequency de-chirping in the photodiodes which significantly simplifies the task of signal processing, and the required receiver bandwidth can be much lower than the signal chirping bandwidth. While only amplitude modulation is required in the lidar transmitter, phase-diverse coherent receiver allows simultaneous detection of target range and velocity through the spectrum of the de-chirped complex waveform. Multi-target detection is also demonstrated experimentally.

Angle-dependent quantum efficiency correction for improved signal accuracy in small-scale Scheimpflug lidar systems

Wide-incidence-angle detection in Scheimpflug lidar causes pixel-wise variations in signal intensity due to differences in quantum efficiency across the detector. This study proposes an angular response correction factor and a correction method to address the difference caused by a wide incidence angle. A Scheimpflug lidar system was developed with a 405 nm laser and an array image sensor; it achieved a detection angle of 11.4°. Experiments with uniform atmosphere and smoke aerosols demonstrated the effectiveness of the correction method. For uniform atmosphere, it ensured consistent signal intensity. In smoke aerosol detection, the Klett method revealed the correction factor’s role in counteracting small-scale variations in the extinction coefficient. The method improved the extinction coefficient accuracy within 4 m by 4%–75%.

Comparative analysis of sandy beach and foredune geomorphic change measurements from Apple lidar and small-unoccupied aerial systems

Sandy beach and foredune environments are common throughout coastlines globally. Coastal landscapes are dynamic and vulnerable to water level fluctuations, storm events, and human disturbances. Standard methods for measuring geomorphic changes include small-unoccupied aircraft systems paired with structure-from-motion photogrammetry (sUAS-SfM), but this can be costly and logistically challenging. We evaluated the accuracy of Apple lidar in comparison to high precision sUAS-SfM and RTK-GPS to map sandy beach and foredune geomorphic change. Checkpoint elevations were measured via RTK-GPS and both the sUAS-SfM and Apple lidar surveyed elevations were compared against these checkpoints to evaluate the performance of both methods for measuring elevations. The sUAS-SfM elevation data were on average around 0.004 m above/below the checkpoint elevations while the Apple Lidar elevations were around 0.039 m. Apple lidar and sUAS-SfM-derived volumetric measurements and spatial patterns of erosion and accretion were compared to evaluate the Apple lidar’s ability to detect geomorphic change over time. The geomorphic changes documented from these two methods were similar though the Apple lidar appeared to capture finer-scale erosion and accretion patterns. Our findings indicate that the Apple lidar can capture sandy beach and foredune geomorphic changes rapidly and accurately, which can promote proactive and resilient coastal management.

Advancement on target ranging and tracking by four-quadrant photon counting lidar.

Single-photon lidar stands out as a promising technology for long-distance lidar applications, owing to its attributes of single-photon sensitivity and high repetition rate. Existing single-photon lidar systems typically rely on single-point scanning for positioning and tracking, necessitating intricate and precise scanning control. In pursuit of a more concise and efficient positioning, we incorporate the four-quadrant theory to articulate the signal formula of photon detection, and propose a novel single-photon four-quadrant positioning method. Our method, which includes signal preprocessing, compensation for longitudinal motion, extraction of pixel intensity, and acquisition of lateral motion, facilitates motion acquisition and positioning for targets. Through simulation calculations, we analyze and compare the effectiveness of each step of the method. With longitudinal and lateral speeds of 100 m/s and 50 m/s, respectively, the trajectory error is 1.7%, and the average speed error is 1.8%. Moreover, for various verification experiments, the trajectory errors are all below 4.2%, and the average speed errors remain under 5.4%, effectively verifying the validity of our method in acquiring the motion information and positioning of targets. It provides an excellent option for acquiring motion information and tracking small moving targets over long distances.

Reduction of lidar ranging error in turbulent water based on WT-ICA method

A new signal-processing method for the underwater lidar system based on wavelet transform (WT) and independent component analysis (ICA) is presented in this paper. Turbulence in water causes stronger scattering in underwater lidar returns, which can reduce the ranging accuracy and spatial resolution of the underwater lidar system. The method we proposed combines wavelet decomposition and independent component analysis (ICA), the data sets from wavelet decomposition are used to form the input matrix of ICA. The new method benefits from adaptive filtering of WT and blind source separation of ICA, therefore, the scattering clutters are further reduced. The WT-ICA signal processing algorithm is tested with experimental data. A mirror and a black Polyvinyl chloride (PVC) plate were used as the targets, respectively. Without using the WT-ICA, the ranging error with the mirror was 10.0 cm at 3 m distance when the attenuation coefficient of the water was 2.0 m-1. A pump was used to generate turbulence in the water tank. When the flow rate of the pump was 200 L/min, after using WT-ICA, the ranging error was reduced from 14.0 cm to 4.0 cm. For the PVC plate, the ranging errors were 4.5 cm and 19.0 cm at 6.0 attenuation length with and without the algorithm respectively. In both cases, applying this algorithm can significantly improve the ranging accuracy. When the algorithm is applied, the ranging error and detection probability of target echoes with and without turbulence were similar. These results indicated that the WT-ICA method had the ability to reduce the influence of turbulence.

A Machine Learning Approach to Correct Turbulence Intensity measured by Floating Lidars

In this work we introduce a supervised Machine Learning (ML) model to correct the Turbulence Intensity (TI) measured by Floating LiDAR Systems (FLS) in offshore environment. The model was developed using data from 46 EOLOS-FLS200 validation campaigns (≈ 4.6 years) carried out at three reference sites in the North Sea. It is based on Gradient Boosting Decision Tree (GBDT) and accounts for wind characteristics, atmospheric conditions, buoy motion, and wave features. Numerical analyses pronounced a consistent improvement in both coefficient of determination (R 2) and Mean Bias Error yielded by the ML-corrected TI. In addition, TI estimates in accordance with the state-of-the-art best practices were successfully obtained, even when evaluating the ML model in a site out of the training dataset, which demonstrates the model’s robustness.

Applying motion compensation to offshore wind lidar reconstructed wind measurements

Floating lidar systems present an effective and advantageous solution for offshore wind speed measurements, providing wind speed and direction data at multiple heights with easy deployment to provide widespread wind information around an area. One of the causes of apprehension, however, concerns buoy motion which induces overestimations of turbulence intensity and potentially imprecise measurements of horizontal wind speed. This research aims to reduce this uncertainty by applying motion compensation to three wind field reconstruction techniques (scalar, vector, and hybrid) to assess the amelioration on horizontal wind speed and turbulence intensity; especially how motion compensation can improve scalar and hybrid outputs. A three-month dataset of a floating lidar system (FLS) was compared to a nearby fixed lidar. The results demonstrate that motion compensation provides an improvement for horizontal wind speed measurement but inflates the overestimation of turbulence intensity from the floating lidar system. Though scalar reconstruction measurements provide slightly more accurate data compared to hybrid reconstructed measurements, motion compensation helps limit the bias disparity; ergo, applying motion compensation reduces the differences in bias for scalar and hybrid reconstructed measurements. This study results in two conclusions. Basic motion compensation provides a notable amelioration for horizontal wind speed FLS measurements, yet further research must be conducted to find a suitable method to improve turbulence intensity. Additionally, the scalar reconstruction method still slightly outperforms hybrid reconstruction in offshore measurement results, but motion compensation aids in limiting the differences between the two.

Enhancing the field-of-view of spectral-scanning FMCW LiDAR by multipass configuration with an echelle grating.

We present a spectral-scanning frequency-modulated continuous wave (FMCW) 3D imaging system capable of producing high-resolution depth maps with an extended field of view (FOV). By employing a multipass configuration with an echelle grating, the system achieves an FOV of 5.5° along the grating axis. The resulting depth maps have a resolution of 70 × 40 pixels, with a depth resolution of 5.1 mm. The system employs an echelle grating for beam steering and leverages the multipass configuration for angular FOV magnification. Quantitative depth measurements and 3D imaging results of a static 3D-printed depth variation target are demonstrated. The proposed approach offers a promising solution for enhancing the FOV of spectral-scanning FMCW LiDAR systems within a limited wavelength-swept range, thereby reducing system complexity and cost, paving the way for improved 3D imaging applications.