During a twelve day field test west of the continental shelf off the coast of Washington state, we conducted multiple environmental data collection flights in a 150 km by 150 km area. We operated a scanning lidar system optimized for ocean profiling collecting near surface atmospheric return signal, surface reflections and optical profiles to several optical depths. The along and across track spatial resolution was approximately 10 meters and the vertical resolution was approximately 0.1 meters. We also deployed ten single use temperature profiling buoys during the test. We will present comparisons of the spatial-temporal lidar data to the buoy data and other public source data, such as satellite derived k-diffuse and Argo float data. It is our expectation that the lidar data will reveal complex and changing vertical optical structures on sub-kilometer horizontal scales that are not adequately captured by other ocean sensing techniques.
Scattering effects in underwater environments significantly challenge optical perception. This paper introduces a foveating confocal bistatic LiDAR system, uniquely capable of adaptive targeting with its MEMS-modulated transmitter and receiver in turbid underwater conditions. By dynamically adjusting its receiver instantaneous field of view to areas of interest, it effectively increases depth sampling in complex and challenging underwater environments. Applying bistatic principles, separating transmitter and receiver, we allow robustness to scattering media effects. We demonstrate LIDAR results in an underwater laboratory tank setting.
Naval Air Warfare Center Aircraft Division (NAWCAD) engineers and scientists recently completed initial laboratory and field testing of the Modulated Underwater Laser Imaging System (MULIS) prototype. This represents the culmination of years of collaboration between NAWCAD, industry, and academia partners to transition NAWCAD’s radar-encoded laser imaging technology out of the lab and into the field. This paper presents results from both initial laboratory and field tests of the MULIS prototype. Laboratory tests evaluated imaging performance in a variety of simulated water clarity conditions. MULIS was then integrated into a REMUS 600 Autonomous Underwater Vehicle (AUV) for a field test event in the Chesapeake Bay in the summer of 2023. Multiple successful missions were run over the course of the field test, obtaining 3D imagery of the submerged objects despite the challenging water clarity conditions in the Chesapeake Bay.
During summer and fall of 2022, multiple flights were conducted with an airborne blue wavelength scanning lidar west of San Diego and in waters surrounding Iceland. Geo-registered lidar 𝑘 profiles reveal multiple ocean parameters such as mixed layer depth variations and dense plankton layers over scales of meters to kilometers. These measurements can be compared to both historical measurements of lidar 𝑘 profiles conducted with in situ instrumentation, as well as to satellite-derived measurements of ocean parameters. The main challenge of in situ oceanographic measurements is the difficulty in achieving efficient coverage of a wide area. Meanwhile, satellites cover a wide area but may not provide sufficient resolution for oceanographic studies; for example NASA’s Aqua/MODIS satellite pixel spacing resolution is on the order of 10 kilometers. The airborne lidar measurements provide larger coverage area than an in situ instrument while also providing higher resolution and greater depth penetration than a satellite measurement. This paper provides an overview of the airborne blue wavelength scanning lidar and demonstrates measurements of two ocean water properties, the average diffuse attenuation coefficient in the mixed layer and the mixed layer depth. The airborne lidar measurements of these properties show reasonable agreement with relevant satellite and in situ databases.
In this work, the radar processing technique of Range-Doppler processing is investigated for its potential to enhance performance of the radar-encoded laser system in rangefinding applications. One of the challenges experienced by this system is in discriminating between the returns from underwater objects and environmental clutter in highly-scattering and/or low signal-to-noise ratio conditions. The intention of this work is to investigate whether the addition of a new dimension, velocity, will improve ranging performance. This work presents the application of the Range-Doppler processing technique to transform data collected in a laboratory water tank. Results and performance improvements using the radar-encoded laser system are compared against those obtained with a conventional, short-pulse laser.
This paper presents experiments using a time of flight (ToF) camera modified to use 525 nm green laser illumination to capture amplitude and depth images of an underwater scene. Experiments in object imaging and ranging were conducted in both clear and turbid water. 3D imaging using flood illumination was successfully performed in clear water and in some turbid water conditions. Ranging using collimated laser beams was performed in turbid water. Several major error sources were observed, including low illumination levels, fixed pattern noise, and backscatter contribution to the phase measurement. To attempt to address these concerns, multiple lasers were used to improve illumination levels and spatial frequency domain filtering was performed to mitigate fixed pattern noise. Additionally, experiments with using multiple modulation frequencies suggested that there may be potential for discriminating backscatter from object reflection.
In this work, we investigate the use of a radar processing technique to enhance detection performance of a short pulse laser system. One of the challenges experienced by this system is discriminating between the return from an underwater object and the clutter return caused by backscatter in highly-scattering and/or low signal-to-noise ratio (SNR) conditions. Taking inspiration from the radar processing community, we apply the Range-Doppler processing transform to data we collect in our laboratory water tank. This work will present the modified Range-Doppler technique, provide laboratory test tank results, and demonstrate performance improvements achieved using the modified Range-Doppler technique.
This paper investigates a total variation (TV) regularization image processing algorithm to restore underwater range images taken with a modified commercial time-of-flight (ToF) camera. The ToF camera illuminator was modified to support 532 nm flood illumination for underwater operation. This approach can produce highresolution amplitude and range images while rejecting a significant amount of ambient light. However, scattering due to the water turbidity adversely impacts image quality by introducing high amounts of image noise and image blurring that affect both the amplitude and range images. The TV regularization algorithm is applied to experimental images taken in a small test tank in the presence of a scattering agent to simulate a range of practical turbidities. Algorithm details are provided, and baseline and processed images are presented. The processed images demonstrate image restoration that retains the downrange edge features of the object being imaged is possible for a range of practical turbidities.
In this work we investigate the use of pattern classification algorithms to enhance detection performance of the underwater radar-encoded laser system. A challenge encountered with this system is the automatic detection of the return from an underwater object in highly-scattering and/or low signal-to-noise ratio (SNR) conditions. Previous efforts were largely based on threshold detection and result in detection errors in such challenging conditions. Other efforts attempt to use signal processing to remove scatter returns, but this does not address low SNR cases. We take a different approach here, investigating the use of machine learning to develop classifiers which combine various shape and statistical features to discriminate between object and non-object returns. Such pattern classifiers are commonly used in a variety of applications; the novelty in this work is applying such techniques to the problem of automatic object detection in a degraded visual environment, namely turbid water. We describe our framework and features, then demonstrate the performance of three pattern classification detectors using a series of test data collected in a variety of water conditions in a laboratory test tank. All three pattern classification detectors outperform a standard detection method. There are subtle performance differences between the classifiers that may result in application-specific tradeoff considerations.
Axicon spatial coherence filtering is presented as a method to improve underwater optical ranging. In underwater environments, light detection and ranging (lidar) is often limited by scattering from particulate matter. Previous work suggests that scattered light and object-reflected light have different spatial phase distributions. This work exploits this difference in spatial phase, using an axicon to optically separate light with different degrees of spatial coherence. The performance of the lidar system with and without the axicon filter is compared. Axicon spatial coherence filtering demonstrates the ability to suppress multipath backscatter and forward scatter, leading to improvements in range accuracy
KEYWORDS: Image processing, Signal attenuation, Ranging, Backscatter, Scattering, Modulation, Image enhancement, Detection and tracking algorithms, Sensors, Signal to noise ratio
A processing technique for enhancing imagery and ranging data collected by an underwater modulated pulse laser system is presented. Laser-based sensors offer high-resolution and high-accuracy for imaging and ranging applications in the underwater environment. However, these capabilities can be degraded in turbid waters due to scattering. We present experimental results demonstrating a technique inspired by image processing which reduces the effects of both backscatter and forward scatter. By combining individual return waveforms of the modulated pulse laser system together and applying the processing approach as described, images can be formed in which an object in the scene can be distinguished from scatter using an edge detector. Results obtained by applying the technique to laboratory experimental data are presented and compared to a baseline approach. Useful three-dimensional imagery was generated out to 6.9 attenuation lengths, a 25% improvement over the baseline. In range finding experiments without a range-gate, a test object was detected at 11.1 attenuation lengths downrange, compared to 5.7 attenuation lengths for the baseline.
This work presents a processing technique for enhancing images collected by an underwater modulated pulse laser imaging system. Laser-based sensors offer high-resolution and high-accuracy ranging in the underwater environment. However, these capabilities can be degraded in turbid waters due to scattering. This work presents experimental results demonstrating an image processing technique that reduces the effects of both backscatter and forward scatter. Without the use of gating, filtering, or a priori information, the processing technique can generate useful imagery to 6.9 attenuation lengths in a controlled laboratory environment.
Adaptive filtering and channel estimation techniques are applied to laser based ranging systems that utilize wide-band intensity modulation to measure the range and reflectivity of underwater objects. The proposed method aims to iteratively learn the frequency dependent characteristics of the underwater environment using a frequency domain adaptive filter, which results in an estimate for the channels optical impulse response. This work presents the application of the frequency domain adaptive filter to simulated and experimental data, and shows it is possible to iteratively learn the underwater optical channel impulse response while using Hybrid Lidar/Radar techniques.
KEYWORDS: Principal component analysis, Backscatter, Signal to noise ratio, LIDAR, Modulation, Scattering, Statistical signal processing, Ranging, Absorption, Signal processing, Sensors, Environmental sensing, Photons
This work presents a new statistical signal processing approach to reduce the effects of forward scatter on range accuracy for an underwater modulated pulse lidar. Lidar sensors offer the potential for high-resolution, high-accuracy ranging in the underwater environment. For the modulated pulse lidar rangefinder, performance is limited in turbid waters primarily due to forward scatter, which causes decreased range resolution and accuracy. This work presents simulated and experimental results demonstrating the ability of statistical signal processing to reduce range error for systems operating in these turbid conditions. Experimental results demonstrated 60% reduction in range error compared to a baseline approach.
Blue-green laser systems are being developed for optical imaging and ranging in the underwater environment. The imaging application requires high range resolution to distinguish between multiple targets in the scene or between multiple target features, while the ranging application benefits from measurements with high range accuracy. The group at the Naval Air Warfare Center Aircraft Division (NAWCAD) in Patuxent River, MD has been investigating the merging of wideband radar modulation schemes with a pulsed laser system for underwater imaging and ranging applications. For the imaging application, the narrow peak produced by pulse compression at the receiver offers enhanced range resolution relative to traditional short pulse approaches. For ranging, the selection of modulation frequency bands approaching 1GHz provides backscatter and forward scatter suppression and enhanced range accuracy. Both passband and baseband digital processing have been applied to data collected in laboratory water tank experiments. The results have shown that the choice of processing scheme has a significant impact on optimizing the performance of modulated pulse laser systems for either imaging or ranging applications. These different processing schemes will be discussed, and results showing the effect of the processing schemes for imaging and ranging will be presented.
This work demonstrates a new statistical approach towards backscatter “clutter” rejection for continuous-wave underwater lidar systems: independent component analysis. Independent component analysis is a statistical signal processing technique which can separate a return of interest from clutter in a statistical domain. After highlighting the statistical processing concepts, we demonstrate that underwater lidar target and backscatter returns have very different distributions, facilitating their separation in a statistical domain. Example profiles are provided showing the results of this separation, and ranging experiment results are presented. In the ranging experiment, performance is compared to a more conventional frequency-domain filtering approach. Target tracking is maintained to 14.5 attenuation lengths in the laboratory test tank environment, a 2.5 attenuation length improvement over the baseline.
KEYWORDS: Backscatter, Modulation, Ranging, Signal attenuation, Ocean optics, Signal detection, Scattering, Digital signal processing, Receivers, Signal processing
The performance of a frequency-modulated continuous-wave (FMCW) hybrid lidar-radar system will be presented in the context of an underwater optical ranging application. In adapting this technique from the radar community, a laser is intensity-modulated with a linear frequency ramp. A custom wideband laser source modulated by a new wideband digital synthesizer board is used to transmit an 800 MHz wide chirp into the underwater channel. The transmitted signal is mixed with a reference copy to obtain a “beat” signal representing the distance to the desired object. The expected form of the return signal is derived for turbid waters, a highly scattering environment, indicating that FMCW can detect both the desired object and the volumetric center of the backscatter “clutter” signal. This result is verified using both laboratory experiments and a realistic simulation model of the underwater optical channel. Ranging performance is explored as a function of both object position and water turbidity. Experimental and simulated results are in good agreement and performance out to ten attenuation lengths is reported, equivalent to 100 meters in open ocean or 5 meters in a turbid harbor condition.
In this paper simulation and experimental results are presented for two hybrid lidar-radar modulation techniques for underwater laser ranging. Both approaches use a combination of multi-frequency and single frequency modulation with the goal of simultaneously providing good range accuracy, unambiguous range, and backscatter suppression. The first approach uses a combination of dual and single frequency modulation. The performance is explored as a function of increasing average frequency while keeping the difference frequency of the dual tones constant. The second approach uses a combination of a stepped multi-tone modulation called frequency domain reflectometry (FDR) and single frequency modulation. The FDR technique is shown to allow simultaneous detection of the range of both the volumetric center of the backscattered “clutter” signal and the desired object. Experimental and simulated results are in good agreement for both techniques and performance out to ten attenuations lengths is reported.
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