Precise Navigation Research Articles

Silicon nitride passive and active photonic integrated circuits: trends and prospects

The use of silicon nitride in integrated photonics has rapidly progressed in recent decades. Ultra-low-loss waveguides based on silicon nitride are a favorable platform for the research of nonlinear and microwave photonics and their application to a wide variety of fields, including precision metrology, communications, sensing, imaging, navigation, computation, and quantum physics. In recent years, the integration of Si and III-V materials has enabled new large-scale, advanced silicon nitride-based photonic integrated circuits with versatile functionality. In this perspective article, we review current trends and the state-of-the-art in silicon nitride-based photonic devices and circuits. We highlight the hybrid and heterogeneous integration of III-V with silicon nitride for electrically pumped soliton microcomb generation and ultra-low-noise lasers with fundamental linewidths in the tens of mHz range. We also discuss several ultimate limits and challenges of silicon nitride-based photonic device performance and provide routes and prospects for future development.

Modeling and assessment of five-frequency BDS precise point positioning

Since its full operation in 2020, BeiDou Satellite Navigation System (BDS) has provided global services with highly precise Positioning, Navigation, and Timing (PNT) as well as unique short-message communication. More and more academics focus on multi-frequency Precise Point Positioning (PPP) models, but few on BDS five-frequency PPP models. Therefore, this study using the uncombined and Ionospheric-Free (IF) observations develops five BDS five-frequency PPP models and compares them with the traditional dual-frequency model, known as Dual-frequency IF (DF) model. Some biases such as Inter-Frequency Biases (IFB) and Differential Code Bias (DCB) are also addressed. With the data collected from 20 stations, the BDS dual- and five-frequency PPP models are comprehensively evaluated in terms of the static and simulated kinematic positioning performances. Besides, the study also analyzes some by-product estimated parameters in five-frequency PPP models such as Zenith Troposphere Delay (ZTD). The results of experiment show that five-frequency PPP models have different levels of improvement compared with the DF model. In the static mode, the one single Five-Frequency IF combination (FF5) model has the best positioning consequent, especially in the up direction, and in the simulated kinematic mode, the Three Dual-frequency IF combinations (FF3) model has the largest improvement in convergence time.

Cooperative Navigation for Low-Cost UAV Swarm Based on Sigma Point Belief Propagation

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms.

An accurate 3D augmented reality navigation system with enhanced autostereoscopic display for oral and maxillofacial surgery.

Oral and maxillofacial surgery demands high precision navigation to achieve optimal surgical outcomes. Augmented reality (AR) has been a promising solution for intuitive and accurate guidance, whereas existing systems have room for improvement. We propose a high quality and accurate in situ AR navigation system. Enhanced image quality is achieved by deploying eye tracking in lenticular-based autostereoscopic display. Viewpoint tracking and optical tracking are integrated to assure accurate in situ images. The proposed system can provide in situ AR images in real-time, with a viewing angle of 59.5°×49.1°, 3D image resolution of 0.35×0.21mm, and image accuracy of 0.99±0.70mm. A maxillary drilling experiment obtains average position error of 1.12±0.30mm and localization time of 8.7±3.9s, significantly better than conventional 2D navigation system. The proposed system can display high-quality AR images and therefore reduce positioning error and operating time.

Reliable SINS/SAR/GNSS Integrated Navigation System for the Supersonic Vehicle

In the GNSS-challenged/denied environments, compared with other aided navigation systems, synthetic aperture radar- (SAR-) aided navigation systems can achieve all-weather, all-day, and global positioning. This paper is aimed at designing a reliable navigation system for the supersonic vehicle in the whole flight phase. Considering the SAR is of great significance for the reliable and precise navigation of the supersonic vehicle, we introduce it to the terminal phase to cope with the interference of the global navigation satellite system (GNSS). At the same time, to accelerate the speed of SAR positioning, a positioning method based on the area feature and stack sequential decoding algorithm (SSDA) is designed. Then, applying the SAR positioning under the framework of the Kalman filter (KF), we propose a tightly coupled strapdown inertial navigation system (SINS)/SAR/GNSS integration algorithm, which can achieve positioning without a complicated fusion method between the front phase and terminal phase. The flight experiment results show that the proposed method can correct the average position error to 1.7 m within 1 cycle.

Principle and performance of multi-frequency and multi-GNSS PPP-RTK

PPP-RTK which takes full advantages of both Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), is able to provide centimeter-level positioning accuracy with rapid integer Ambiguity Resolution (AR). In recent years, with the development of BeiDou Navigation Satellite System (BDS) and Galileo navigation satellite system (Galileo) as well as the modernization of Global Positioning System (GPS) and GLObal NAvigation Satellite System (GLONASS), more than 140 Global Navigation Satellite System (GNSS) satellites are available. Particularly, the new-generation GNSS satellites are capable of transmitting signals on three or more frequencies. Multi-GNSS and multi-frequency observations become available and can be used to enhance the performance of PPP-RTK. In this contribution, we develop a multi-GNSS and multi-frequency PPP-RTK model, which uses all the available GNSS observations, and comprehensively evaluate its performance in urban environments from the perspectives of positioning accuracy, convergence and fixing percentage. In this method, the precise atmospheric corrections are derived from the multi-frequency and multi-GNSS observations of a regional network, and then disseminated to users to achieve PPP rapid AR. Furthermore, a cascade ambiguity fixing strategy using Extra‐Wide‐Lane (EWL), Wide-Lane (WL) and L1 ambiguities is employed to improve the performance of ambiguity fixing in the urban environments. Vehicle experiments in different scenarios such as suburbs, overpasses, and tunnels are conducted to validate the proposed method. In suburbs, an accuracy of within 2 cm in the horizontal direction and 4 cm in the vertical direction, with the fixing percentage of 93.7% can be achieved. Compared to the GPS-only solution, the positioning accuracy is improved by 87.6%. In urban environments where signals are interrupted frequently, a fast ambiguity re-fixing can be achieved within 5 s. Moreover, multi-frequency GNSS signals can further improve the positioning performance of PPP-RTK, particularly in the case of small amount of observations. These results demonstrate that the multi-frequency and multi-GNSS PPP-RTK is a promising tool for supporting precise vehicle navigation.

Magnetically driven helical hydrogel micromotor for tumor DNA detection

The DNA released by apoptotic tumor cells is one of the cancer markers, which can be used for early diagnosis, and prognosis evaluation. How to detect tumor DNA in a non-invasive, quick, and simple manner is attracting booming research enthusiasm. Here, we proposed a DNA detection system based on a magnetic helical hydrogel (PVA/MA/Fe3O4) micromotor incorporating fluorescent probe SYBR Green. Combining the precise navigation capabilities of the magnetically driven helical micromotor with high sensitivity of nucleic acid dyes, the micromotor-based fluorescent probing system not only exhibits excellent controllable moving performance, but also responds to the DNA released by apoptotic tumor cells with high efficiency, holding great promise for directed DNA detection.

Clock and Power-Induced Bias Correction for UWB Time-of-Flight Measurements

Ultra-Wide Band (UWB) communication systems can be used to design low cost, power efficient and precise navigation systems for mobile robots, by measuring the Time of Flight (ToF) of messages traveling between on-board UWB transceivers to infer their locations. Theoretically, decimeter level positioning accuracy or better should be achievable, at least in benign propagation environments where Line-of-Sight (LoS) between the transceivers can be maintained. Yet, in practice, even in such favorable conditions, one often observes significant systematic errors (bias) in the ToF measurements, depending for example on the hardware configuration and relative poses between robots. This letter proposes a ToF error model that includes a standard transceiver clock offset term and an additional term that varies with the received signal power (RxP). We show experimentally that, after fine correction of the clock offset term using clock skew measurements available on modern UWB hardware, much of the remaining pose dependent error in LoS measurements can be captured by the (appropriately defined) RxP-dependent term. This leads us to propose a simple bias compensation scheme that only requires on-board measurements (clock skew and RxP) to remove most of the observed bias in LoS ToF measurements and reliably achieve cm-level ranging accuracy. Because the calibrated ToF bias model does not depend on any extrinsic information such as receiver distances or poses, it can be applied before any additional error correction scheme that requires more information about the robots and their environment.

A SINS/CNS integrated navigation scheme with improved mathematical horizon reference

Strap-down inertial navigation system (SINS) and celestial navigation system (CNS) integrated navigation have been widely applied in ships, aircraft and spacecraft etc. However, in practice, its performance is highly depending on the horizon reference accuracy, which is usually difficult to guarantee. In this work, we propose a SINS/CNS integrated navigation scheme based on a novel mathematical horizon reference (MHR) determination method. More specifically, inertial coordinate system is used in constructing the MHR, where the attitude and position errors could be decoupled and compensated, respectively. Then, a more accurate MHR could be established, thus resulting in better navigation results. Comparative numerical simulations for a maneuvering flight have been conducted, and it is shown the proposed approach has the best converging speed as well as the highest navigation precision compared with SINS and existing MHR-based SINS/CNS schemes, and all-state optimal estimation could be achieved, thus enabling fully autonomous and high-precision navigation.

Analysis of an Unmanned Underwater Vehicle Propulsion Model for Motion Control

The knowledge of dynamic models to describe the behaviors of unmanned underwater vehicles allows to design a precise navigation, guidance, and control system. A practical identification strategy is presented in this contribution, a procedure that can be easily executed during field trials and that is able to obtain a rapid assessment about the characteristics of the vehicle dynamic model. The developed procedure is designed for small-size-class remotely operated vehicles, characterized by low operative speeds. The vehicle is equipped with proprioceptive sensors in charge of measuring data needed to identify its dynamic model. Building on the proposed practical identification experiments, different vehicle propulsion models are considered and analyzed: the first is a general theoretical model accounting for the vehicle hydrodynamics (standard marine Fossen model); the others are modified models introducing additional terms in the produced thrust (torque), namely, a term proportional to the rate of change of the propeller revolutions per minute (r.p.m.), (where is the revolution rate of the propeller), and a delayed copy of the terms connected to the fluid axial flow velocity. For all considered degrees of freedom, the fitting performance of the proposed models resulted to be superior to the first. Moreover, a control system building on the obtained dynamic characterization is described.

Multi-GNSS Precise Point Positioning with UWB Tightly Coupled Integration.

Global Navigation Satellite Systems (GNSSs) can provide high-precision positioning services, which can be applied to fields including navigation and positioning, autonomous driving, unmanned aerial vehicles and so on. However, GNSS signals are easily disrupted in complex environments, which results in a positioning performance with a significantly inferior accuracy and lengthier convergence time, particularly for the single GNSS system. In this paper, multi-GNSS precise point positioning (PPP) with tightly integrating ultra-wide band (UWB) technology is presented to implement fast and precise navigation and positioning. The validity of the algorithm is evaluated by a set of GNSS and UWB data. The statistics indicate that multi-GNSS/UWB integration can significantly improve positioning performance in terms of the positioning accuracy and convergence time. The improvement of the positioning performance for the GNSS/UWB tightly coupled integration mainly concerns the north and east directions, and to a lesser extent, the vertical direction. Furthermore, the convergence performance of GNSS/UWB solution is analyzed by simulating GNSS signal interruption. The reliability and robustness of GNSS/UWB solution during GNSS signal interruption is verified. The results show that multi-GNSS/UWB solution can significantly improve the accuracy and convergence speed of PPP.

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM.

The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous localization and mapping (SLAM) technology is a hot spot in solving the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, and 0.6 m/s, respectively; the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the SD is less than 6 cm; the average course deviation is <3°, and the SD is <1.5°. Both the positioning accuracy and the navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.

Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

Autonomous navigation in narrow indoor environments such as indoor factory, warehouse and laboratory environments, and so on requires higher flexibility and navigation accuracy of the vehicle. This article presents an autonomous navigation method for four-wheel steering vehicle which combines extended Kalman filtering (EKF) and rapidly-exploring random tree (RRT) to improve the precision and flexibility of autonomous navigation of the vehicle in narrow indoor environments. The four-wheel steering model was established by the key parameters such as shape size and minimum angle of rotation of the experimental vehicle. Considering the problem that the uncertainty of pose estimation increases with time during autonomous navigation, an error model is schemed by adding noise to the output terminal of the analog odometer sensor. In order to suppress the accumulation of the uncertainty and keep it stable for a long time, the prediction and update steps of Kalman filter are introduced to filter the error. Then, the simultaneous positioning and mapping are established. Based on accurate positioning, a set of driving paths to reach the target is generated by RRT sampling algorithm. The simulation results show that positioning uncertainty remains stable over time, which verifies the effectiveness of the method. The overall positioning percentage error is 0.21%. Compared with traditional dead reckoning algorithm, the positioning accuracy is improved by 73.1% and the vehicle flexibility is increased by 68.6%. The four-wheel steering vehicle can find an ideal trajectory in narrow indoor environments, which assures the efficiency of the autonomous navigation and the traveling quality of the navigation route. Finally, the experimental results are consistent with the simulation results, which further verifies the effectiveness of the proposed algorithm.

116 OpEyes-Augmented Reality Surgical Navigation and Advanced Minimally Invasive Pedicle Screw Navigation

INTRODUCTION: The spine surgery market is moving away from open dissection to minimally invasive spine surgery (MISS). Precise operative navigation is imperative for MISS to achieve its potential. Guidance systems have been available for years, but adoption is limited by laborious set-up, costs and intraoperative imaging requirements. Herein is described the use of OpEyes-Augmented Reality (Novarad, American Fork, UT) for navigation and guidance of MISS for highly accurate insertion of pedicle screws. METHODS: A Microsoft HoloLens 2 (Microsoft, Redmond, WA) was used as the hardware platform for OpEyes-AR software. AprilTags, printed with CT visible ink, were either adhered to the skin, anchored to the iliac crests, or a vertebra. Following CT, the radiologist identified virtual pathways for ideal placement of pedicle screws for each vertebra.The annotated studies were wirelessly uploaded to the software/visor combination by viewing an encrypted QR code assigned by the pre-op system. The studies were registered to the AprilTags captured during CT. The virtual pathways were called by voice command for each vertebra and were used to establish skin entry point, trajectory, and depth with target bullseyes. Fluoroscopy was not required for navigation. Excella MIS hardware was used for placement of the screws into the pedicles of a donor torso (Innovasis, Salt Lake City, UT). RESULTS: Pedicle screws were instrumented through levels L5-T9 for a total of 15 screws. There were no breaches. Many of the thoracic pedicles were narrow with little or no tolerance. Guidance and navigation were performed solely with OpEyes-AR. CONCLUSION: OpEyes-AR is a tether-less, holographic visor and software combination offering the potential for precise navigation of MISS percutaneous insertion of pedicle screws with a nominal OR footprint and elimination of OR imaging.

Investigation of geomagnetic reference models based on the Iridium^{circledR } constellation

The World Magnetic Model (WMM) is a geomagnetic main field model that is widely used for navigation by governments, industry and the general public. In recent years, the model has been derived using high accuracy magnetometer data from the Swarm mission. This study explores the possibility of developing future WMMs in the post-Swarm era using data from the Iridium satellite constellation. Iridium magnetometers are primarily used for attitude control, so they are not designed to produce the same level of accuracy as magnetic data from scientific missions. Iridium magnetometer errors range from 30 nT quantization to hundreds of nT errors due to spacecraft contamination and calibration uncertainty, whereas Swarm measurements are accurate to about 1 nT. The calibration uncertainty in the Iridium measurements is identified as a major error source, and a method is developed to calibrate the spacecraft measurements using data from a subset of the INTERMAGNET observatory network producing quasi-definitive data on a regular basis. After calibration, the Iridium data produced main field models with approximately 20 nT average error and 40 nT maximum error as compared to the CHAOS-7.2 model. For many scientific and precision navigation applications, highly accurate Swarm-like measurements are still necessary, however, the Iridium-based models were shown to meet the WMM error tolerances, indicating that Iridium is a viable data source for future WMMs.Graphical

Research on Predictive Control Algorithm of Vehicle Turning Path Based on Monocular Vision

To solve the issue that the monocular vision vehicle navigation system is limited by the field of vision acquired by the charge-coupled device camera and cannot acquire navigation turning path information throughout the turning process, decreasing the vehicle turning control accuracy, this paper proposed a turning control algorithm based on monocular vision vehicle turning path prediction. Firstly, the camera’s distortion was adjusted. Secondly, the camera imaging model was built, and the turning path’s position information was determined using the imaging position relationship. The vehicle motion model was built in accordance with the vehicle steering mode. Lastly, the cornering trajectory of a vehicle was estimated using the vehicle’s front axle length and front-wheel adjustment data, determining the vehicle turning point and turn operations on the basis of the projected relationship between the vehicle turning track and the turning path position. The experimental results showed that the proposed algorithm can effectively measure the position parameters of the cornering path and complete vehicle cornering control. The maximum absolute error of intercept and slope in turn path position parameters were 0.2525 m and 0.014 m, respectively. The cornering control accuracy was 0.093 m and 0.085 m, which met the vehicle navigation cornering control requirements. At the same time, the research can provide theoretical reference for research on precise navigation control of other cornering vehicles and other path guidance modes.

Chinese Deep Space Stations: A brief review [Antenna Applications Corner

To meet the rapidly growing needs of current and future deep space exploration missions, China has developed three deep space stations: Jiamusi and Kashi, (China), and Argentina (South America), for providing continuous telemetry, tracking, and command (TT&C), reliable communication, and precise navigation services. Integrated with state-of-the-art technologies, these three deep space stations are not only powerful for transmission but also extremely sensitive for receiving. Many remarkable innovations and achievements were made during the construction process, including the beam waveguide (BWG), adjustable subreflector, high-power transmitter, cryogenic low-noise amplifier (LNA), antenna-arraying technique, digital baseband receiver, and interferometry. This article briefly reviews the system architecture, general performance, critical system design issues, and contributions made by these Chinese deep space stations (CDSSs) during Chang’e missions.

A Novel Two-Stage Adaptive Filtering Model-Based GNSS/INS Tightly Coupled Precise Relative Navigation Algorithm for Autonomous Aerial Refueling

Autonomous aerial refueling (AAR) has generated great interest in recent years. However, much research has focused on the vision-based close docking stage; few studies have been conducted on the navigation algorithm for the rendezvous and following stages. High-precision relative navigation in following stage can provide favourable conditions for successful docking. Aiming at precise relative navigation in the complex high dynamic environment of aerial refueling rendezvous and following stages, a two-stage adaptive filtering architecture is exploited in this paper. An adaptive main Kalman filter (AKF) is realized for ambiguity eliminated GNSS/INS tightly coupled integrated system, and a robust adaptive subfilter is developed for GNSS individually. Particularly, aiming at the influence of pseudorange observation multipath outliers and state abnormal disturbances in unmanned air vehicle- (UAV-) tanker proximity, an INS-aided bifactor robust and classified factor adaptive filtering (IBRCAF) algorithm for single-frequency ambiguity resolution is proposed. Finally, the effectiveness of the algorithm is verified by the simulation experiments for UAV-tanker. The results indicate that the IBRCAF algorithm can efficiently suppress the influence of pseudorange multipath gross errors and abnormal state disturbances and greatly raise the success rate of ambiguity resolution, and the two-stage adaptive filtering algorithm of IBRCAF-AKF can significantly improve relative navigation performance and achieve centimeter-level accuracy.

Uncertainty-Driven Ontology for Decision Support System in Air Transport

Recent electronics advances for air transport have increased aircraft density, volume, and frequency in the airspace. These advances come with control requirements for precise navigation, coordinated Air Traffic Management (ATM) or Unmanned aircraft system Traffic Management (UTM), and proactive security. The tight tolerances of aircraft control necessitate management of spatial uncertainty, timeliness precision, and confidence assessment, which have, respectively, variance, reliability, and veracity situation awareness and assessment metrics. Meeting such airspace requirements involves the ability to evaluate how those metrics impact ATM/UTM operations, making the complex interrelationships between them a key aspect for coping with the fast worldwide growth of air transport. To support such growth, ontologies have been proposed as a promising technology for making such interrelationships explicit, while facilitating communication between avionics devices. This paper investigates the use of ontologies in support of electronic ATM/UTM operations, highlighting the use of Uncertainty Representation and the Reasoning Evaluation Framework (URREF) in realizing the ability for Air Traffic Controllers (ATCs) to semantically communicate with aircraft operators concerning physical airspace coordination. Using Avionics Analytics Ontology (AAO) endowed with the URREF, application examples based on two airspace situations are presented. Example results for northeast coast of Brazil atmospheric volcanic ash as well as for the Eyjafjallajokull volcano eruption show a 65–80% success in providing warnings to ATCs for airspace control. The paper demonstrates that an ontology-based UTM enhances the capability and accuracy of an ATM to suggest rerouting in the presence of remarkably deteriorated weather conditions.

Radiometric Stability Assessment of the DSCOVR EPIC Visible Bands Using MODIS, VIIRS, and Invariant Targets as Independent References

The DSCOVR mission was designed to take advantage of the first Lagrangian position (L1) to continuously observe the Earth sunlit disk. To facilitate EPIC V03 data product validation and fusion, the EPIC V03 navigation and calibration stability is assessed. The Aqua-MODIS, NPP-VIIRS, and N20-VIIRS based radiometric scaling factors are also provided. The V03 navigation error was 15.5 km, a 50% improvement over V02 and within what can be achieved by an objective image alignment algorithm. Both the navigation accuracy and precision were improved in V03 and were found to be comparable across all EPIC visible channels. The all-sky tropical ocean and deep convective cloud ray-matched MODIS- and VIIRS-referenced EPIC inter-calibration gains are within 0.4% of one-another, and are also within 0.4% of a previous study’s NPP-VIIRS-referenced gains. The inter-calibration study reveals that EPIC bands 5 and 6 degraded mostly within the first year of operation and becoming stable thereafter, whereas bands 7 and 10 were stable during the 6-years record. The capability of the V03 navigation allowed EPIC stability to be monitored using DCC and Libya-4 invariant targets. The EPIC V03 calibration was mostly stable within 0.3% over the 6-years record, as determined from inter-calibration and invariant target monitoring methods. Remarkably, both the DCC- and Libya-4-based methods were able to confirm the stability of the E-8 and E-9 oxygen absorptions—a stability comparable to that of the E-7 and E-10 reference bands. No significant change in the navigation accuracy or calibration stability was observed after the DSCOVR 2019 safe mode incident. The impressive stability of the DSCOVR EPIC L1B V03 channel radiances can greatly benefit the Earth remote sensing community by providing diurnally complete daytime radiative flux and environmental retrievals for future sensors located at L1.