Satellite Hardware Research Articles

Numerical simulation for large baseline interferometric imaging altimeter

Sea topography information holds significant importance in oceanic research and the climate change detection. Radar imaging altimetry has emerged as the leading approach for global ocean observation, employing synthetic aperture radar (SAR) interferometry to enhance the spatial resolution of Sea topography. Nevertheless, current payload capacity and satellite hardware limitations prevent the extension of the interferometric baseline by enlarging the physical antenna size. This constraint hinders achieving centimeter-level accuracy in interferometric altimetry. To address this challenge, we conducted a numerical simulation to assess the viability of a large baseline interferometric imaging altimeter (LB-IIA). By controlling the baseline within the range of 600–1000 m through spiral orbit design in two satellites and mitigating baseline de-correlation with the carrier frequency shift (CFS) technique, we aimed to overcome the above limitations. Our findings demonstrate the efficacy of the CFS technique in compensating for baseline decoherence, elevating coherence from less than 0.1 to over 0.85. Concurrently. The height difference accuracy between neighboring sea surfaces reaches 1 cm within a 1 km resolution. This study is anticipated to serve as a foundational reference for future interferometric imaging altimeter development, catering to the demand for high-precision sea topography data in accurate global bathymetry inversion.

Onboard Data Prioritization Using Multi-Class Image Segmentation for Nanosatellites

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing.

Analysis of factors affecting the estimation of the multi-GNSS satellite differential code biases (SDCBs)

The satellite hardware differential code biases (SDCBs) are one of the important factors affecting the accuracy of GNSS-based ionospheric total electron content (TEC) estimation. The accuracy of SDCB estimates is directly influenced by the data processing methodology. To analyze the impact of various factors on multi-GNSS SDCB estimation, we conducted a comprehensive analysis of the number of contributed stations, the data sampling rate, the contributed satellite systems, and the cut-off elevation angle. Two sets of GNSS data obtained from the International GNSS Service (IGS) multi-GNSS experiment (MGEX) network, covering both high and low solar activities during the ascending phase of solar cycle 25, were processed to estimate multi-GNSS SDCBs. The results indicate that the number of contributed stations is the primary factor that influences multi-GNSS SDCB estimation. To achieve a balance between computational efficiency and accuracy, a data sampling rate of 540 s is recommended, which results in a multi-GNSS SDCB root-mean-square (RMS) error of less than 0.01 ns compared to a 60-second sampling rate. The stability of SDCBs is hardly affected by incorporating observations from multiple GNSS systems, and the differences of the standard deviations of SDCBs for quad-, triple- and dual-system solutions are below 0.002 ns. Additionally, the results indicate that the optimal cutoff elevation angle for multi-GNSS SDCB estimation is between 20° and 30°, which ensures the best stability in the estimated multi-GNSS SDCBs.

Quantifying trade-offs in satellite hardware configurations using a super-resolution framework with realistic image degradation

ABSTRACT When designing and operating Earth observation satellites, trade-offs must often be made between different hardware components, operational considerations, etc… For example, considerations include how much of the mass budget should be spent on imaging lenses?, or how long should image exposure times be?. Resulting limitations in image resolution and quality can be partially compensated for using super-resolution (SR) techniques. However, deep SR networks recently applied to satellite imagery are often trained and tested on data that lacks the typical image degrading noise present in real satellite images. In this work, we combine a method for generating realistically degraded satellite images with a deep SR network, in the context of different satellite hardware configurations and geographical types. We use this framework to assess deep SR performance given realistic remote sensing payloads across different terrain types, by evaluating payload- and terrain-dependent SR performance in reconstructing realistically degraded images. The framework allows us to model the effect of alternative satellite hardware configurations on resulting SR image quality, providing insight into optimal satellite operations and payload design in the context of SR-based image quality enhancements.

Overcoming the challenges of developing CubeSat flight software with limited access to satellite hardware

The drastic rise of CubeSat missions in recent years has forced satellite developers to deliver satellites at high speeds. The complexity of flight software for missions, however, leaves it susceptible to programmatic failure. To develop the DockSat mission in an acceptable timeframe, this article proposes to utilize digital twins of satellite components to allow software development and complex testing at earlier stages of the mission development cycle where hardware and experimental setups are not available.

AutoML-Based Neural Architecture Search for Object Recognition in Satellite Imagery

Advancements in optical satellite hardware and lowered costs for satellite launches raised the high demand for geospatial intelligence. The object recognition problem in multi-spectral satellite imagery carries dataset properties unique to this problem. Perspective distortion, resolution variability, data spectrality, and other features make it difficult for a specific human-invented neural network to perform well on a dispersed type of scenery, ranging data quality, and different objects. UNET, MACU, and other manually designed network architectures deliver high-performance results for accuracy and prediction speed in large objects. However, once trained on different datasets, the performance drops and requires manual recalibration or further configuration testing to adjust the neural network architecture. To solve these issues, AutoML-based techniques can be employed. In this paper, we focus on Neural Architecture Search that is capable of obtaining a well-performing network configuration without human manual intervention. Firstly, we conducted detailed testing on the top four performing neural networks for object recognition in satellite imagery to compare their performance: FastFCN, DeepLabv3, UNET, and MACU. Then we applied and further developed a Neural Architecture Search technique for the best-performing manually designed MACU by optimizing a search space at the artificial neuron cellular level of the network. Several NAS-MACU versions were explored and evaluated. Our developed AutoML process generated a NAS-MACU neural network that produced better performance compared with MACU, especially in a low-information intensity environment. The experimental investigation was performed on our annotated and updated publicly available satellite imagery dataset. We can state that the application of the Neural Architecture Search procedure has the capability to be applied across various datasets and object recognition problems within the remote sensing research field.

New Orbit Determination Method for GEO Satellites Based on BeiDou Short-Message Communication Ranging

The radio determination service system (RDSS), a navigation and positioning system independently developed by China, features services such as short-message communication, position reporting, and international search and rescue. The L-band pseudo-range and phase data are the primary data sources in precise orbit determination (POD) for geostationary Earth orbit (GEO) satellite in the BeiDou system, especially in the orbit manoeuvre period. These data are the only data sources in the POD for GEOs. However, when the pseudo-range and phase data is abnormal due to unforeseen reasons, such as satellite hardware failure or monitoring receiver abnormalities, the data abnormality leads to orbit determination abnormalities or even failures for GEOs, then the service performance and availability of the RDSS system are greatly degraded. Therefore, a new POD method for GEOs based on BeiDou short-message communication ranging data has gained research attention to improve the service reliability of the BeiDou navigation satellite system (BDS)-3, realising the deep integration of communication and navigation services of the BDS. This problem has not been addressed so far. Therefore, in this study, a new POD method for GEO satellites is investigated using high-precision satellite laser ranging (SLR) data and RDSS data. The SLR data are used as the benchmark to calibrate the time delay value of RDSS equipment, and RDSS data are only used in the orbit determination process by fixing the corrected RDSS time delay value, and the satellite orbit parameters and dynamic parameters are solved. Experimental analysis is conducted using the measured SLR and RDSS data of the BDS, and the orbit accuracy in this paper is evaluated by the precise ephemeris of the Multi-GNSS pilot project (MGEX) and SLR data. The results show that the orbit accuracy in the orbital arc and the 2-h orbital prediction arc for GEOs are 6.01 m and 6.99 m, respectively, compared with the ephemeris of MGEX, and the short-arc orbit accuracy after 4 h of manoeuvring is 11.11 m. The orbit accuracy in the radial component by SLR data is 0.54 m. The required orbit accuracy for GEO satellites in the RDSS service of the BDS-3 is 15 m. The orbit accuracy achieved in this paper is superior to that of this technical index. This method expands the application field of the RDSS data and greatly enriches the POD method for GEOs. It can be adopted as a backup technology for the POD method for GEOs based on RNSS data, significantly improving the service reliability of the BeiDou RDSS service.

A new algorithm for intelligent detection of geohazards incorporating attention mechanism

The development of InSAR satellite hardware and data processing technology enables us to rapidly obtain massive high spatial and temporal resolution surface deformation results. The abundant information helps geohazard detection, but also brings challenges for geohazard interpretation. InSAR geohazards intelligent detection methods based on deep learning can greatly improve the efficiency and precision of geohazards interpretation. However, these methods are usually limited by quality of the InSAR result that have various errors, such as decoherence error, topography residue and atmospheric delay. This study proposes a convolutional neural network incorporated an attention mechanism, referred to as DeforNet, which can effectively reduce the influences of the atmospheric delay and topography noises by introducing convolutional block attention module and depth-wise separable convolution. The new method can efficiently and accurately detect small and medium-scale geohazards from InSAR results. The comparison between the DeforNet with FCN, U-net and SegNet, using both synthetic and real samples, show that DeforNet has significant superiority in noise suppression and deformation identification. In the application of the whole Shanxi province, China, the DeforNet detected 1,553 geohazards with the minimum area of 0.21 km2. Our result shows that a strong spatial correlation between the location of geohazards and coal mining in this region. Within the coalfield, the number of identified geohazard accounts for 64.6 % of the total number in Shanxi. We also found that the identification accuracy of DeforNet is affected by the quality of the InSAR results, the scale of the geohazard and the wrap interval. DeforNet can serve to refine the detailed investigation of geohazards and promote the application of InSAR technology in geohazards prevention and mitigation.

Satellite Edge Computing Architecture and Network Slice Scheduling for IoT Support

For 5G and 6G communications, satellites are drawing great attention for global coverage extension and 3-D mobility enhancement. With advancements of satellite hardware, functional satellites are expected to be applied for 6G Internet of Things (IoT) services. In particular, because IoT service has a relatively low computational burden, it is more feasible for satellite edge computing (SatEC) with limited power, making IoT supportable SatEC one of the economically feasible applications for future satellite networks. In this article, an architecture of IoT supportable SatEC is analyzed, and the corresponding network slice scheduling is proposed. First, a multiobjective optimization problem for IoT supportable SatEC is formulated with respect to latency, computational power, and transmission power attenuation. The problem is solved for the satellite offloading rate and altitude by using a heuristic algorithm in low time complexity with time-varying satellite constellation topology and various service requirements for simulations. Next, to analyze the expandability of the SatEC IoT network, a sliced SatEC IoT scheduling problem is formulated in the normalized weighted sum of latency, computational power, and transmission power attenuation. Scheduling rules are proposed to prioritize various applications with the results of the scheduling problem and with the proper SatEC offloading rates predefined in the Pareto optimality of the satellite edge multiobjective Tabu search (SE-MOTS). Finally, efficient satellite constellations are determined by comparing low-Earth orbit (LEO) and very LEO (VLEO) satellite networks, in terms of proper satellite altitudes and offloading strategies for IoT supportable SatEC. Based on simulation results, a scheduling rule for sliced satellite network and a proper offloading strategy of different slices are proposed, and an appropriate altitude of the satellite network for sliced SatEC is discussed.

System Architecture of a High Altitude Live-Streaming System

Modern methods of surveillance can be achieved through various methods including drones, satellite imagery, and aircraft surveillance. However, many of these methods have design flaws; most notably, these design flaws include power usage issues, detectability, and range of coverage. Through the use of a payload and ground station with dish and satellite hardware on a high-altitude balloon flight, one can live-stream aerial footage and greatly reduce security risks. Using a high altitude balloon as the basis for flight not only increases aerial range but also flight time since the batteries are only used to power the hardware required for streaming. The payload also enables lower package visibility as the payload would not be easily caught using radar or similar technologies. We propose a system with the overall goal of providing a reliable high data rate communications link for low latency telemetry and video throughout a high-altitude balloon flight. This system is composed of commercial off the shelf (COTS) parts including Ubiquiti hardware to ensure ease in duplication for industrial use. To ensure a multi-layered encryption and self-monitoring system, we would use two or more different languages to ensure constant, reliable, and encrypted communications on top of proficient rendering and visualization. This paper presents a detailed outline of our system architecture coupled with our test plan and preliminary results to ensure flight-readiness of our live-streaming system.

Optimization-Based Hyperspectral Spatiotemporal Super-Resolution

Due to hardware limitations and financial considerations, it is challenging to acquire fine spatial and temporal resolution (FSFT) images, which leads to the interest in spatiotemporal fusion problems. Instead of directly obtaining costly FSFT images, an alternative is to fuse fine spatial, coarse temporal resolution (FSCT) images with coarse spatial, fine temporal resolution (CSFT) images. Unlike existing spatiotemporal methods which are only designed for multispectral images, this paper first proposes a new fusion framework for hyperspectral spatiotemporal super-resolution, termed HSTSR. In this paper, we first deal with the coarse temporal resolution issue by adopting the fast iterative shrinkage-thresholding algorithm (FISTA) to estimate the missing images at the intermediate time series. Then, we fuse the hyperspectral image and the multispectral image in each time series via coupled nonnegative matrix factorization (CNMF) to get FSFT hyperspectral images. Importantly, we can automatically estimate the associated spatial blurring and spectral downsampling matrices without prior satellite hardware information. Compared with other extended multispectral spatiotemporal methods, our method not only attains satisfying qualities significantly faster, but also requires much less input data.

A Novel GNSS Single-Frequency PPP Approach to Estimate the Ionospheric TEC and Satellite Pseudorange Observable-Specific Signal Bias

As the significant error sources influencing the Global Navigation Satellite System (GNSS) positioning, navigation, and timing (PNT) services, ionospheric delay and satellite hardware delay should be properly calibrated. Particularly, the pseudorange observable-specific signal bias (OSB) is convenient and can be directly corrected in the raw pseudorange measurement. In this work, we present a novel single-frequency ionospheric-free-half precise point positioning (PPP) (SFPPP2) approach for ionospheric studies, in which the ionospheric vertical total electron content (VTEC) and satellite OSB are isolated from the slant ionospheric observables by means of the ionospheric multilayer mapping function (MF). The computation and parameterization methods of the ionospheric VTEC and satellite pseudorange OSB are present. To validate the effectiveness and reliability of the novel method, we investigate and compare the performance of the single-frequency ionospheric-float PPP (SFPPP1), SFPPP2, and dual-frequency ionospheric-float PPP (DFPPP1) solutions for ionosphere sensing. The analytical results indicate that the novel approach can extract the slant ionospheric observables with the accuracy of submeters. The accuracy of the estimated ionospheric VTEC by the single-frequency PPP approaches is in the submeter level, which exhibits a slightly worse accuracy than that from the dual-frequency PPP solution. The estimated ionospheric VTEC accuracy is improved with the multilayer MF compared with the single-layer MF. The estimated BeiDou Navigation Satellite System (BDS) pseudorange OSB with the proposed SFPPP2 approach is stable, reliable, and of the high accuracy, and the rms’s with respect to Chinese Academy of Sciences (CAS) product for C2I and C6I signals with single-layer and multilayer MFs are 0.40, 0.41, 0.60, and 0.63 ns, respectively. The proposed PPP approach can retrieve the VTEC and satellite pseudorange OSB by mass-market receivers for the GNSS users.

A variant of raw observation approach for BDS/GNSS precise point positioning with fast integer ambiguity resolution

The Precise Point Positioning (PPP) technique uses a single Global Navigation Satellite System (GNSS) receiver to collect carrier-phase and code observations and perform centimeter-accuracy positioning together with the precise satellite orbit and clock corrections provided. According to the observations used, there are basically two approaches, namely, the ionosphere-free combination approach and the raw observation approach. The former eliminates the ionosphere effects in the observation domain, while the latter estimates the ionosphere effects using uncombined and undifferenced observations, i.e., so-called raw observations. These traditional techniques do not fix carrier-phase ambiguities to integers, if the additional corrections of satellite hardware biases are not provided to the users. To derive the corrections of hardware biases in network side, the ionosphere-free combination operation is often used to obtain the ionosphere-free ambiguities from the L1 and L2 ones produced even with the raw observation approach in earlier studies. This contribution introduces a variant of the raw observation approach that does not use any ionosphere-free (or narrow-lane) combination operator to derive satellite hardware bias and compute PPP ambiguity float and fixed solution. The reparameterization and the manipulation of design matrix coefficients are described. A computational procedure is developed to derive the satellite hardware biases on WL and L1 directly. The PPP ambiguity-fixed solutions are obtained also directly with WL/L1 integer ambiguity resolutions. The proposed method is applied to process the data of a GNSS network covering a large part of China. We produce the satellite biases of BeiDou, GPS and Galileo. The results demonstrate that both accuracy and convergence are significantly improved with integer ambiguity resolution. The BeiDou contributions on accuracy and convergence are also assessed. It is disclosed for the first time that BeiDou only ambiguity-fixed solutions achieve the similar accuracy with that of GPS/Galileo combined, at least in mainland China. The numerical analysis demonstrates that the best solutions are achieved by GPS/Galileo/BeiDou solutions. The accuracy in horizontal components is better than 6 mm, and in the height component better than 20 mm (one sigma). The mean convergence time for reliable ambiguity-fixing is about 1.37 min with 0.12 min standard deviation among stations without using ionosphere corrections and the third frequency measurements. The contribution of BDS is numerically highlighted.

Pass-by-Pass Ambiguity Resolution in Single GPS Receiver PPP Using Observations for Two Sequential Days: An Exploratory Study

“Pass-by-pass” or “track-to-track” ambiguity resolution removes Global Navigation Satellite System (GNSS) satellite hardware delays between adjacent undifferenced (UD) ambiguities, which is often applied in precise orbit determination (POD) for Low Earth Orbit (LEO) satellites to improve the accuracy of orbits. In this study, we carried out an exploratory study to use the “pass-by-pass” ambiguity resolution by differencing the undifferenced ambiguity candidates for two adjacent passes in sidereal days for a single Global Positioning System (GPS) receiver static Precise Point Positioning (PPP). Using the GPS observations from 132 globally distributed reference stations of International GPS Service (IGS), we find that 99.08% wide-lane (WL) and 97.83% narrow-lane (NL) double-difference ambiguities formed by the “pass-by-pass” method for all stations can be fixed to their nearest integers within absolute fractional residuals of 0.2 cycles. These proportions are higher than the corresponding values of network solution with multiple receivers with 97.39% and 91.20%, respectively. About 97% to 98% of ambiguities can be fixed finally on average. The comparison of the estimated station coordinates with the IGS weekly solutions reveals that the Root Mean Square (RMS) in East and North directions are 2-4 mm and is about 6 mm in the Up direction. For hourly data, it is found that the mean positioning accuracy improvement can achieve to about 10% after ambiguity resolution. From a dam deformation monitoring application, it shows that the fixing rate of WL and NL ambiguity can be closed to 100% and higher than 90%, respectively. The time series generated by PPP are also in agreement with the short baseline solutions.

Deep learning-based object recognition in multispectral satellite imagery for real-time applications.

Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.

Design of intelligent high-resolution remote sensing satellites based on new integrated electronic system

As the brain of intelligent satellites, the on-board integrated electronic system is the core system in the overall design of smart satellites. Based on modular integrated electronic technology, this paper innovatively adopts system engineering methods to uniformly design the information processing, control and management procedures, hardware and software equipment of intelligent satellites to realize the sharing of information resource and the optimization of the entire satellite system, which takes the lead in the research area of intelligent satellites in China. The research content mainly includes hardware architecture design and software network architecture design of intelligence satellites. In terms of hardware architecture, creative ideas of modular and open structure design are adopted, and standardized interfaces are designed for on-board hardware, realizing efficient integration and plug-and-play of satellite hardware. As for the software network architecture, the hierarchical design is inventively adopted to facilitate the reuse of software resources; through the unified deployment of on-board computing resources, the ability of autonomous management of tasks and the autonomous processing of data is improved to facilitate rapid application. The overall ingenious design has the characteristics of openness, versatility and flexibility, and meets the application requirements of intelligent satellites.

ҚАЗАҚСТАННЫҢ АУЫЛ ШАРУАШЫЛЫҒЫ ӨНДІРІСІНІҢ ЭНЕРГИЯ ТИІМДІЛІГІН АРТТЫРУДЫҢ КЕЙБІР АСПЕКТІЛЕР

The authors consider the aim of the study - the issues of improving the energy efficiency of the country's agro-industrial complex. The relevance of the topic is that the studied sector of the national economy is characterized by high energy intensity and a low level of efficient consumption of energy resources, which negatively affects the competitiveness of products in domestic and world markets. The main problems and factors affecting the low efficiency of energy use are identified. The importance of increasing energy saving in agricultural sector through innovative resource-saving technologies is justified: introduction of precision farming system, biogas technologies. The advantages of their specific elements, economic efficiency, ensuring the rational use of energy resources are presented. Recommendations have been developed aimed at activating crop productivity management system based on a complex of satellite and computer hardware. The experience of foreign countries in the use of biogas technologies is analyzed, which indicates the prospects for the development of this direction of renewable energy, which contributes to the solution of not only energy, but also environmental problems. The significant benefits of operating biogas plants aimed to meet the energy needs of livestock farms and rural populations are shown. The economic feasibility and efficiency of the implementation of biogas projects have been proven, which simultaneously allow the rational management of agricultural waste. Proposals have been developed to stimulate the promotion of environmentally friendly biogas technologies in rural areas, due to the fact that expansion of the livestock production industry can have a negative impact on the environment if the industry's waste is not recycled properly.

Analysis and Assessment of BDS-2 and BDS-3 Broadcast Ephemeris: Accuracy, the Datum of Broadcast Clocks and Its Impact on Single Point Positioning

For the global ordinary users, the broadcast ephemeris plays important roles in positioning, navigation and timing (PNT) services. With the construction of a new generation of the BeiDou navigation satellite system (BDS), the development of BDS has entered the era of globalization. It is meaningful for global users to analyze and assess the BDS-2 and BDS-3 broadcast ephemeris. Therefore, the satellite orbits and clock offsets calculated by broadcast ephemeris are compared with the precise orbit and clock offset products provided by three analysis centers (i.e., Helmholtz Centre Potsdam German Research Center for Geosciences (GFZ), Wuhan University (WHU) and Shanghai Astronomical Observatory (SHA)), and the corresponding signal-in-space range error (SISRE) and the orbit-only SISRE are analyzed to assess the accuracy of BDS broadcast ephemeris. Due to the upgrade of BDS-3 satellite hardware technology and inter-satellite links payload and the development of satellite orbit determination algorithm, the accuracy of broadcast orbit and clock offsets has been greatly improved. The root mean square (RMS) of BDS-3 broadcast orbit errors is improved by 86.30%, 89.47% and 76.86%, and the standard deviation (STD) is improved by 79.41%, 77.00% and 76.78% compared with BDS-2 in the radial, along-track and cross-track directions. The corresponding RMS and STD of all BDS-3 satellite clock offsets are improved by 40.34% and 52.49% than that of BDS-2, respectively. Meanwhile, the mean RMS and STD are 1.78 m and 0.40 m for BDS-2 SISRE, 1.72 m and 0.34 m for BDS-2 orbit-only SISRE, 0.50 m and 0.14 m for BDS-3 SISRE, and 0.17 m and 0.04 m for BDS-3 orbit-only SISRE. It is noteworthy that the average broadcast-minus-precise (BMP) clock values of BDS-2 and BDS-3 are inconsistent, which can indirectly prove that the datum of broadcast clock offsets for BDS-2 and BDS-3 are inconsistent. The inconsistency of the datum of satellite clock offsets and receiver hardware delay bias between BDS-2 and BDS-3 will result in the inter-system bias (ISB) on the receiver segment. For JAVAD TRE_3 receivers, the ISB is relatively small and thus can be ignored. However, for the TRIMBLE ALLOY, SEPT POLARX5, CETC-54-GMR-4016, CETC-54-GMR-4011, GNSS-GGR and UB4B0-13478 receivers, estimating ISB can improve the positioning accuracy of single point positioning (SPP) by 20.15%, 19.81% and 12.76% in north, east and up directions, respectively.

The INSPIRE-2 CubeSat for the QB50 Project

The 2-unit CubeSat INSPIRE-2/AU03 was designed, built, tested, delivered, and accepted by the European Union’s QB50 project in less than 10 months and for less than US$120,000 in non-salary outlays including launch, despite being a first satellite. It carried 5 instruments (a multi-Needle Langmuir Probe, a diffraction-limited spectrograph, an advanced GPS receiver, and 2 radiation detectors) and the satellite hardware included both Commercial Off-The-Shelf (COTS) and Australian components. INSPIRE-2 was deployed into space from the International Space Station by Nanoracks on 26 May 2017 following an Atlas V launch. The satellite was brought online a month after launch as a result of a major campaign with the international radio amateur community. The uplink function of the Communications board was badly damaged in July 2017 in the first of that year’s two major, extended, space weather periods, plausibly due to radiation damage. While INSPIRE-2’s radio beacons were resurrected and continued for over a year the Communications board’s handshaking protocols meant that downlinking of data was not possible. INSPIRE-2 lived for over a year in space with mostly functioning systems. This paper summarises the major firsts and importance of INSPIRE-2 and its fellow Australian QB50 CubeSats UNSW-EC0 and SuSat (e.g., the first Australian CubeSats and the first Australian-built satellites in 16 years), as well as the science and technical goals, instruments, spacecraft systems, recovery, initial data, and evidence that the major space weather events of 12 July - 4 August 2017 significantly damaged INSPIRE-2 and caused an outage from 26 July to 5 September 2017. It also discusses the lessons learned and the reasons why CubeSats in constellations like QB50, whether international or Australian, provide excellent opportunities for scientific, technical, and commercial development, public outreach and engagement, and international engagement.

Research on GNSS Signal Abnormal Monitoring Technology

With the continuous development and popularization of positioning and navigation system related applications, it is becoming more and more important to improve the security and reliability of global satellite navigation system. Due to the complex space electromagnetic environment and the failure of satellite hardware in the transmission process, the satellite signal may be abnormal, thus affecting the normal use of GNSS services by users. This makes the research of GNSS signal anomaly detection and recognition technology significant. The anomaly of satellite signal is mainly reflected in the anomaly of parameters and positioning results in the process of signal processing of the receiver. Therefore, according to the output parameters of the receiver in the acquisition, tracking, message resolution and other stages, the integrity of satellite signal can be detected, and the possible signal anomaly can be identified. This paper mainly introduces the monitoring methods and analysis methods of GNSS signal anomaly monitoring technology, and comprehensively evaluates the signal quality of the receiver from five categories of indicators: time domain index, frequency domain index, modulation domain index, correlation domain index and measurement domain index, respectively. The acquisition methods of each signal quality index and its indication are also discussed. The relationship between the standard and the performance of navigation signal is introduced.