Precise Navigation Research Articles

Precise Real-Time Navigation of the LT-1A Satellite Based on New BDS-3 B1C/B2a Signals

Precise Real-Time Navigation of the LT-1A Satellite Based on New BDS-3 B1C/B2a Signals

Design of rural tourism management cloud platform based on intelligent audio processing and sensor navigation positioning

People's living standards are constantly improving, and many people are eager to travel, especially to rural areas, which has become a tourism trend, which has also driven the local economy. Each place is actively utilizing the advantages of its own region to establish a unique tourism economic industry chain and seek the interests of local villagers. Based on this, this article takes the specific development of contemporary rural tourism industry as the basic research object, and develops a specialized rural tourism management cloud service system platform. The system mainly relies on intelligent audio processing and related navigation and positioning functions. By finely processing the collected intelligent audio signals, utilizing specialized data conversion tools to improve the accuracy of the collected signals, and using a linear aliasing model to handle subsequent problems. We also propose a carrier based system coordinate for navigation and positioning algorithms, which can estimate the optimal information value among them. In addition, this article also conducted a specific analysis of the performance of two models, namely the single step model and the Seq2Seq model. The tourism management cloud service system platform for rural areas based on intelligent audio related processing and precise navigation positioning proposed in this article can effectively promote the development of local rural tourism and improve the quality of life of villagers.

Exploring the Future of Indoor Navigation: AR Technology

Indoor navigation within complex environments remains a recurring challenge in the age of rapid technological advancement. Traditional methods often fall short when applied to intricate indoor spaces like shopping malls, airports, and corporate campuses. This paper introduces ARNav, an innovative Augmented Reality (AR) based indoor navigation system designed to enhance indoor navigation accuracy and user experience. ARNav utilizes AR technology to overlay digital information, such as arrows, markers, and directional cues, onto the real-time camera view of users' devices. This dynamic overlay simplifies navigation while offering an immersive experience. The system achieves precise indoor positioning through sensor fusion and mapping techniques, boasts an intuitive user-centric design, and implements robust data security measures. Energy efficiency optimization ensures longer battery life, and the system seamlessly integrates with external data sources, offering real-time information on points of interest and events within indoor spaces. A user feedback mechanism allows for continuous improvement, placing user needs at the forefront of development. This paper discusses the design and implementation of ARNav, highlighting the technologies and mathematical models for accurate positioning and AR overlay. ARNav represents a promising solution for indoor wayfinding, combining AR technology with precise indoor navigation to enhance user experiences. The paper explores potential future developments, including advanced AR features, machine learning integration, and compatibility with various devices and platforms, thus contributing to the field of indoor navigation and AR technology.

Exploring non-curative endoscopic submucosal dissection: Current treatment optimization and future indication expansion.

The increasing popularity of endoscopic submucosal dissection (ESD) as a treatment for early gastric cancer has highlighted the importance of quality assessment in achieving curative resections. This article emphasizes the significance of evaluating ESD quality, not only for curative cases but also for non-curative ones. Postoperative assessment relies on the endoscopic curability (eCura) classification, but management strategies for eCuraC-1 tumour with a positive horizontal margin are unclear. Current research primarily focuses on comparing additional surgical procedures in high-risk patients, while studies specifically targeting eCuraC-1 patients are limited. Exploring management strategies and follow-up outcomes for such cases could provide valuable insights. Furthermore, the application of molecular imaging using near-infrared fluorescent tracers holds promise for precise tumour diagnosis and navigation, potentially impacting the management of early-stage gastric cancer patients. Advancing research in these areas is essential for improving the overall efficacy of endoscopic techniques and refining treatment indications.

A Multi-Step Pseudo-Measurement Adaptive Kalman Filter Based on Filtering Performance Evaluation and Its Application in the INS/GNSS Navigation System

The objective of this paper is to tackle the issue of the degraded navigation accuracy of the inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation system in urban applications, especially under complex environments. This study utilizes historical state estimates and proposes a multi-step pseudo-measurement adaptive Kalman filter (MPKF) algorithm based on the filter performance evaluation. First, taking advantage of the independence between INS and GNSS, the enhanced second-order mutual difference (SOMD) algorithm is utilized for estimating the noise variance of the GNSS, which is decoupled from the estimate error of state and used as a module for filter performance evaluation. Then, the construction of the proposed method is presented, together with the analysis of the noise variance of multi-step pseudo-measurement. Ultimately, the efficacy of the MPKF is confirmed through a real-world vehicle experiment involving a tightly-coupled INS/GNSS integrated navigation application, demonstrating a noteworthy enhancement in navigation precision within densely wooded and built-up areas. Compared to the standard EKF and enhanced redundant measurement-based adaptive Kalman filter (ERMAKF), the proposed algorithm improves the positioning accuracy by 48% and 34%, velocity accuracy by 50% and 35%, and attitude accuracy by 38% and 48%, respectively, in the urban building segment.

Low-altitude remote sensing-based global 3D path planning for precision navigation of agriculture vehicles - beyond crop row detection

Field path planning provides the basis for the autonomous navigation of agricultural vehicles. Existing path planning approaches are constrained to a 2D plane, disregarding the impact of terrain factors on navigation tasks. Furthermore, these methods generate non-global paths, as evidenced by their inability to accommodate the headland turning area while performing path planning based on the actual crop growth in the farmland. Low-altitude remote sensing technology, characterized by its high spatial resolution, timely data acquisition, and strong terrain perception, holds great potential for application in autonomous navigation. This study aims to integrate low-altitude remote sensing technology with path-planning tasks for agricultural vehicles by constructing oblique photography models of fields. The objective is to achieve global 3D path planning leveraging advanced methodologies rooted in deep learning and image processing. Four main steps were included in the proposed method. Low-altitude remote sensing models of field blocks were constructed first. Secondly, the models were converted to image patches for dataset establishment. Thirdly, primary crop row identification was conducted by semantic segmentation. Finally, global 3D paths covering the farmland and headland areas were generated. Tea fields in hilly areas were used to test the algorithm. Experiments revealed that the proposed method could be adapted to different field shapes, row numbers, row spacing, and vehicle turning radii. The proposed method uses deep learning and image processing as the primary technical tools but goes beyond traditional crop row detection to form a global path-planning strategy. In addition, the elevation information enabled by the 3D detection manner allows agricultural vehicles to gain a comprehensive understanding of both their own position and that of their destination. The dataset and the code are available at: https://github.com/ZeroHeading/Global-3D-path-generation.git.

The accuracy of electromagnetic navigation bronchoscopy compared to fluoroscopy in navigation of transbronchial lung cryobiopsy in patients with interstitial lung disease

BackgroundSafely implementing transbronchial lung cryobiopsy (TBLC) in patients with interstitial lung disease (ILD) requires accurate navigation. Traditional fluoroscopy falls short in reducing the risk of post-procedure pneumothorax. The potential of electromagnetic navigation bronchoscopy (ENB) as a more precise navigation method warrants further exploration.MethodsA prospective cohort study was conducted on ILD patients undergoing TBLC. Patients were assigned either fluoroscopy or ENB for cryoprobe positioning. Navigation accuracy was evaluated using cone beam computed tomography (CBCT) images as the standard. Safety and diagnostic yield were also observed.ResultsSeventeen patients underwent TBLC, with 10 guided by fluoroscopy and seven by ENB. Fluoroscopy-guided cryoprobe navigation required more adjustments [9/15 (60%) v.s. 1/9 (11%), p = 0.018] for subsequent TBLC compared to ENB, as confirmed by CBCT images. Clinical characteristics, post-procedure complications, and biopsy specimen size showed no significant differences between the groups. Fourteen patients obtained a pathological diagnosis, and 15 received a multidisciplinary discussion (MDD) diagnosis. In the fluoroscopy group, three patients failed to obtain a pathological diagnosis, and two failed to obtain an MDD diagnosis.ConclusionsENB demonstrates significantly superior accuracy in TBLC navigation compared to traditional fluoroscopy when CBCT images are used as a reference. Further studies are necessary to determine the value of ENB in TBLC navigation for ILD patients.

On the Use of Ultra-WideBand-Based Augmentation for Precision Maneuvering

The limitations of the existing Global Navigation Satellite Systems (GNSS) integrated with Inertial Measurement Units (IMU) have presented significant challenges in meeting the stringent demands of precision maneuvering. The identified constraints in terms of accuracy and availability have required the development of an alternative solution to enhance the performance of navigation systems in dynamic and diverse environments. This paper summarizes the research regarding the integration of ultra-wideband (UWB) technology as an augmentation of the conventional GNSS+IMU system; it proposes an approach that aims to overcome the limitations of conventional navigation systems. By making use of UWB technology, the proposed low-cost UWB-augmented GNSS+IMU system not only fulfils the required performance standards but also offers the unique capability to navigate seamlessly across indoor and outdoor environments. The developed system was validated through comprehensive testing and analysis in both the automotive and maritime sectors. The obtained results highlight the system’s capacity as a dependable and resilient solution for precise navigation, and they promote its use within the domain of accurate maneuvering.

The field experiments results on remote determination of soil moisture portraits

The introduction of precision farming technologies into the practice of agricultural production is an urgent task, the solution of which would ensure the sustainable development of crop production, cost reduction and saving of water and other resources. The purpose of the work is to obtain and demonstrate the results of field experiments on remote determination of soil moisture portraits, temperature maps of the soil surface and relief maps conducted in the fields of the vegetable complex of CJSC «Gorodishche» in the Stupinsky district of the Moscow region using a complex of a new bipolarization microwave radiometer and a precision navigation receiver placed on a tractor. Field experiments on remote determination of portraits of soil moisture and surface temperature were carried out, as well as the microrelief parameter was determined and the features of the mutual correlation characteristics of various thematic maps were studied. Maps of portraits of humidity and temperature and surface topography of the soil were obtained. The electromagnetic compatibility of the microwave radiometer with «John Deere» tractor systems has been verified. Data have been prepared for entering into the database of radiometric measurements of soils. The work carried out will make it possible to clarify some parameters of the microwave radiometric system and its compatibility with the navigation receiver, radio modem and carrier systems. The analysis of the data obtained will make it possible to develop recommendations for optimizing technological processes and reclamation measures. The accumulation of a database of radiometric measurements will allow the further use of artificial intelligence technologies to refine the brightness-humidity conversion algorithms and the introduction of the developed equipment and algorithms into the technological processes of growing crops ensures the sustainable development of crop production technologies, increasing yields and obtaining additional profit by the agro-industrial complex.

Preliminary results of scintillation monitoring at KLEF-Guntur low latitude station using GNSS software defined radio

Abstract Global Navigation Satellite Systems (GNSS) have become an integral part of modern life, supporting various applications, from precise positioning and navigation to timing and synchronization. However, GNSS signals are vulnerable to natural interferences including various atmospheric disturbances, with ionospheric scintillations being a significant challenge. Ionospheric scintillations, caused by irregularities in the Earth’s ionosphere, introduce rapid fluctuations in the amplitude and phase of GNSS signals. These fluctuations can severely degrade the accuracy and reliability of GNSS receivers, leading to positioning errors and navigation failures. Hence, it is crucial to develop effective mitigation strategies. One of the promising approaches to mitigate ionospheric scintillations is the utilization of Software Defined Radio (SDR) technology in GNSS receivers. SDR allows for real-time adaptation to changing signal conditions, enabling the receiver to detect scintillations and adjust its signal processing accordingly. This adaptability enhances the receiver’s stability against ionospheric disturbances, ensuring more robust and accurate positioning and navigation. In this paper, preliminary results of GNSS SDR (Make: iP-Solutions, Japan) installed at Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram (Guntur) (16.44° N, 80.62° E) are presented. Amplitude scintillation index (S4) variations for different PRNs and subsequent positioning results are interpreted from April to September 2023. The results are compared and validated with those of the co-located Novatel GNSS receiver and NAVIC receiver. Most of the S4 variations correlate well with the S4 values from the Novatel and NAVIC receivers. S4 observations from the Septentrio receiver at Daytona Beach (Florida) are also presented. The results of SDR will be extended further for the development of scintillation mitigation algorithms. We plan to install an SDR and employ similar mitigation strategy at this location in the near future.

Assessment of length-of-day and universal time predictions based on the results of the Second Earth Orientation Parameters Prediction Comparison Campaign

Predicting Earth Orientation Parameters (EOP) is crucial for precise positioning and navigation both on the Earth’s surface and in space. In recent years, many approaches have been developed to forecast EOP, incorporating observed EOP as well as information on the effective angular momentum (EAM) derived from numerical models of the atmosphere, oceans, and land-surface dynamics. The Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) aimed to comprehensively evaluate EOP forecasts from many international participants and identify the most promising prediction methodologies. This paper presents the validation results of predictions for universal time and length-of-day variations submitted during the 2nd EOP PCC, providing an assessment of their accuracy and reliability. We conduct a detailed evaluation of all valid forecasts using the IERS 14 C04 solution provided by the International Earth Rotation and Reference Systems Service (IERS) as a reference and mean absolute error as the quality measure. Our analysis demonstrates that approaches based on machine learning or the combination of least squares and autoregression, with the use of EAM information as an additional input, provide the highest prediction accuracy for both investigated parameters. Utilizing precise EAM data and forecasts emerges as a pivotal factor in enhancing forecasting accuracy. Although several methods show some potential to outperform the IERS forecasts, the current standard predictions disseminated by IERS are highly reliable and can be fully recommended for operational purposes.

Research on the technology of intelligent vehicle sensor positioning system in autonomous driving

In this comprehensive exploration of positioning and navigation technologies, we have delved into the intricacies of GPS (Global Positioning System), IMU (Inertial Measurement Unit), and VPS (Visual Positioning Systems) systems, shedding light on their unique attributes and applications. GPS, a global satellite-based system, offers precise positioning on a worldwide scale, although it encounters challenges in complex urban and environmental conditions. Its role in navigation and tracking is undeniable. IMU, characterized by accelerometers and gyroscopes, excels in delivering high-precision positioning over short durations, making it invaluable for applications in aviation, robotics, and virtual reality. However, it is susceptible to drift over time. Visual Positioning Systems (VPS), harnessing computer vision and visual sensors, provide remarkable sub-meter to centimeter-level accuracy when conditions are optimal. Their significance is particularly pronounced in indoor navigation, augmented reality, and robotics, although they may face challenges in less favorable environments. These technologies are not isolated but can synergize to enhance accuracy and reliability. GPS and IMU collaborate to compensate for signal disruptions, while GPS and VPS join forces to tackle urban complexities. IMU and VPS integration offer precise indoor navigation and augmented reality experiences, delivering impeccable positioning and orientation data. Ultimately, the choice of technology hinges on specific application requirements, encompassing accuracy, environmental considerations, cost factors, and the need for complementary systems. As these technologies advance, they hold the promise of revolutionizing navigation across various domains, from autonomous vehicles to immersive augmented reality environments.

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method.

One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object's orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of Δφ = 0.51°, which corresponds to a positioning accuracy of ΔR = 6.55 cm, with a position update frequency of fupdate = 50 Hz.

Unfolded Protein-Based Sandwich AIE Probe Imparts High Fluorescent Contrast for Pan-Cancer Surgical Navigation.

Fluorescence imaging-guided navigation for cancer surgery has a promising clinical application. However, pan-cancer encompasses a wide variety of cancer types with significant heterogeneity, resulting in the lack of universal and highly contrasted fluorescent probes for surgical navigation. Here, we developed an aggregation-induced emission (AIE) probe (MI-AIE-TsG, MAT) with dual activation for pan-cancer surgical navigation. MAT weakly activates fluorescence by targeting the SUR1 protein on the endoplasmic reticulum (ER) through the TsG group. Subsequently, the sulfhydryl groups on the unfolded proteins, which are highly enriched in cancer ER, react with the maleimide (MI) of MAT through the thiol-ene click reaction, further enhancing the fluorescence. The formation of a SUR1-MAT-unfolded protein sandwich complex reinforces the restriction of intramolecular motion and eliminates photoinduced electron transfer of MAT, leading to high signal-to-noise (9.2) fluorescence imaging and use for surgical navigation of pan-cancer. The generally high content of unfolded proteins in cancer cells makes MAT imaging generalizable, and it currently has proven feasibility in ovarian, cervical, and breast cancers. Meanwhile, MAT promotes cellular autophagy by hindering protein folding, thereby inhibiting cancer cell proliferation. This generalizable, high-contrast AIE fluorescent probe spans the heterogeneity of pancreatic cancer, enabling precise pancreatic cancer surgery navigation and treatment.

Development of an Advanced Robotic System with Line Tracking and Sanitization in Healthcare Industry

COVID-19 is highly transmittable, especially for frontline healthcare workers. Far-UVC light at 222 nm wavelength effectively inactivates the virus and is safe for human skin and eyes. To reduce the contact between healthcare workers and infected patients while sanitizing the area, this work introduces UVood, an advanced robotic system with automated line tracking and sanitizer to enhance the safety of frontline healthcare workers during the COVID-19 pandemic. Utilizing far-UVC light, the system achieves effective sanitization without harm to humans. Controlled by an Arduino Mega microcontroller and incorporating infrared (IR) and passive infrared (PIR) sensors, UVood allows precise navigation and continuous disinfection. This prototype also integrates a touchscreen as a user interface for user control. Through the functionality verification, the developed proof-of-concept prototype has successfully demonstrated its great potential in healthcare automation and infection prevention.

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

Tracking scheme of airborne star tracker based on event-triggered sliding mode control with known input delay

The airborne star tracker is crucial in aircraft navigation systems, with its tracking performance directly impacting navigation accuracy. Under airborne conditions, the performance of its tracking control will be compromised by various disturbances. Moreover, the limitation in computational resources is another issue that must be addressed. Assuming that the existing studies on this application did not consider these two aspects of the effects simultaneously, this study proposes a novel event-triggered sliding mode control (ET-SMC) scheme considering the known input time delay for star tracking control to address these two issues. First, an extended state observer (ESO) is presented to estimate the disturbance generated by airborne conditions. Second, the ET-SMC scheme further enhances the robustness and improves resource utilization, thus easing the processor burden. An ET mechanism related to the disturbance estimation triggering error in the ESO is introduced to ensure that the system input is only updated when necessary. A novel, easy-to-implement sliding gain is also proposed to increase system adaptability and reduce inherent chattering. The reachability of the sliding surface and the existence of a practical sliding mode of the system are ensured based on the Lyapunov theory. The ultimate upper bound of system states considering the known input time delay is also proven, thereby confirming the stability of the proposed design. The exclusion of Zeno behavior validates the feasibility of the proposed ESO-based ET-SMC. Finally, the effectiveness of the proposed method is verified using comparative simulations and target-tracking experiments. The experimental results demonstrate that the proposed method excels in robustness, disturbance attenuation, high tracking accuracy, and computational resource efficiency. These enhancements are anticipated to result in a more stable tracking performance for the star tracker, thereby contributing to precise aircraft navigation.

Efficacy of navigation system-assisted distraction osteogenesis for hemifacial microsomia based on artificial intelligence for 3 to 18 years old: study protocol for a randomized controlled single-blind trial

BackgroundMandibular distraction osteogenesis (MDO) is a major part of the treatment for hemifacial microsomia patients. Due to the narrow surgical field of the intraoral approach, osteotomy accuracy is highly dependent on the surgeons’ experience. Electromagnetic (EM) tracking systems can achieve satisfying accuracy to provide helpful real-time surgical navigation. Our research team developed an EM navigation system based on artificial intelligence, which has been justified in improving the accuracy of osteotomy in the MDO in animal experiments. This study aims to clarify the effect of the EM navigation system in improving the MDO accuracy for hemifacial microsomia patients.MethodsThis study is designed as a single-centered and randomized controlled trial. Altogether, 22 hemifacial microsomia patients are randomly assigned to the experiment and control groups. All patients receive three-dimensional CT scans and preoperative surgical plans. The EM navigation system will be set up for those in the experiment group, and the control group will undergo traditional surgery. The primary outcome is the surgical precision by comparing the osteotomy position of pre- and postoperative CT scan images through the Geomagic Control software. The secondary outcomes include mandibular symmetry (occlusal plane deviation angle, mandibular ramus height, and body length), pain scale, and complications. Other indications, such as the adverse events of the system and the satisfactory score from patients and their families, will be recorded.DiscussionThis small sample randomized controlled trial intends to explore the application of an EM navigation system in MDO for patients, which has been adopted in other surgeries such as orthognathic procedures. Because of the delicate structures of children and the narrow surgical view, accurate osteotomy and protection of nearby tissue from injury are essential for successful treatment. The EM navigation system based on artificial intelligence adopted in this trial is hypothesized to provide precise real-time navigation for surgeons and optimally improve patient outcomes, including function and aesthetic results. The results of this trial will extend the application of new navigation technology in pediatric plastic surgery.Trial registrationChinese Clinical Trial Registry ChiCTR2200061565. Registered on 29 June 2022.

Intelligent Navigation of a Magnetic Microrobot with Model-Free Deep Reinforcement Learning in a Real-World Environment.

Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications.

Distributed satellite system autonomous orbital control with recursive filtering

In this article, we propose a recursive orbital elements filter for autonomous control of Distributed Satellite Systems (DSS) that significantly reduces the variance of relative orbital elements between the observed and the desired satellite orbits. Leveraging satellite kinematics and control inputs data, combined with a model of relative dynamics, the filter provides smooth and continuous orbital control, while minimizing propellant consumption. In conjunction with Precise Point Positioning (PPP) navigation, the proposed filter enables onboard continuous low-thrust control compatible with high-performance electric propulsion. We also propose a restricted transverse/normal control law that simplifies the thruster's configurations and/or attitude manoeuvres required for propulsion pointing. The applicability and validity of our proposed techniques are verified by numerical simulations with two case studies: a constellation for Differential Interferometric Synthetic Aperture Radar (DInSAR) for global infrastructure monitoring; and a maritime domain awareness mission based on along-track interferometric synthetic aperture radar which requires single-pass interferometry for responsive ship traffic surveillance, and the coverage of a very large maritime zone with high revisit rates.