A lunar surface parameter monitoring device and method based on spaceborne GNSS-R and compact polarimetry

Abstract

The application relates to the technical field of deep space exploration and planetary remote sensing, in particular to a lunar surface parameter monitoring device and method based on a satellite-borne GNSS-R and a compact polarization technology.The device comprises a satellite platform, a GNSS-R receiver, a compact polarization antenna array, an on-orbit processing unit, a data storage and telemetry module and a ground processing system.The method obtains dual-polarization components by receiving direct and moon-reflected signals of a multi-navigation satellite system; generates a delay Doppler map and extracts polarization bit features on orbit; inverses a lunar surface dielectric constant and roughness based on a physical model and a machine learning model; and generates products by the ground system after data storage and transmission.The application realizes high-precision, global coverage and high-frequency monitoring of key physical parameters of the lunar surface, the system has the characteristics of light weight, low power consumption and intelligence, and effectively makes up for the deficiencies of traditional detection technology in terms of time and space continuity, system complexity and cost.

Description

Technical Field

[0001] This invention relates to the fields of deep space exploration and planetary remote sensing technology, and more specifically to a lunar surface parameter monitoring device and method based on spaceborne GNSS-R and simplified polarization technology. Background Technology

[0002] As human exploration of deep space continues to deepen, the Moon, as Earth's closest natural satellite and a forward outpost for deep space exploration, plays a crucial role in the precise detection of its surface physical properties. This is of great significance for lunar scientific research, resource development and utilization, and the construction of future lunar bases. However, existing lunar exploration technologies still have limitations in achieving global coverage, high spatiotemporal resolution, low cost, and long-term continuous monitoring, making it difficult to meet the growing demands of scientific research and engineering applications.

[0003] Existing technologies mainly face the following four prominent problems:

[0004] First, traditional lunar exploration methods have limitations.

[0005] Current mainstream lunar surface exploration technologies, such as optical imaging, laser altimetry, and radar detection, face multiple constraints in their application. Optical imaging technology is highly dependent on sunlight conditions and cannot operate effectively during lunar night or in permanently shadowed polar regions, resulting in blind spots and time discontinuities in observations. While laser altimetry can provide accurate elevation information, its spatial resolution is limited by orbital altitude, making it difficult to achieve high-resolution global topographic mapping, and its data acquisition efficiency is relatively low. Radar detection (such as synthetic aperture radar SAR) has a certain penetration capability and can acquire subsurface information, but the systems are typically power-hungry, heavy, and generate large amounts of data, placing stringent requirements on satellite platforms and increasing mission costs and complexity. These traditional methods generally struggle to simultaneously meet the multiple requirements of global coverage, high spatiotemporal resolution, low cost, and long-term continuous monitoring, limiting our understanding of the dynamic changes in lunar surface characteristics.

[0006] Second, while existing GNSS-R technology is mature in Earth applications, there are gaps and challenges in its application in deep space.

[0007] GNSS-R technology has been successfully applied in Earth sciences to monitor parameters such as ocean altimetry, soil moisture, and sea surface wind fields, demonstrating advantages such as low cost, abundant data sources, and high spatiotemporal resolution. However, this technology is currently almost entirely focused on Earth applications, with its signal processing models, inversion algorithms, and system designs optimized for Earth's atmospheric and liquid water-covered environment. The Moon, as a celestial body without an atmosphere and with vastly different surface media characteristics, exhibits fundamentally different reflected signal characteristics compared to Earth. Models and algorithms applicable to Earth cannot be directly transferred to the inversion of lunar surface parameters. Furthermore, the Earth-Moon distance is much greater than the near-Earth orbit altitude, resulting in extremely weak navigation satellite signals reaching the Moon, posing unprecedented challenges to receiver sensitivity, anti-interference capabilities, and signal acquisition and tracking technologies. Currently, there is no mature spaceborne GNSS-R system supporting lunar applications; related technological research remains in the theoretical exploration and preliminary experimental stages, lacking on-orbit verification.

[0008] Third, traditional fully polarized receivers are difficult to adapt to the constraints of deep space exploration missions.

[0009] Polarization information is crucial for retrieving the dielectric properties, roughness, and composition of the lunar surface. However, traditional fully polarimetric measurement receivers require multiple independent radio frequency channels and complex calibration systems, resulting in complex structures, large sizes, and high power consumption. In deep space exploration missions, satellite platform payload resources (such as weight, size, power, and data transmission bandwidth) are extremely precious and limited. These shortcomings of traditional polarimetric receivers make it difficult to meet the stringent requirements of long-term, unmaintained deep space missions for lightweight payloads, low power consumption, and high reliability, thus hindering the practical application of polarimetric remote sensing technology in lunar exploration.

[0010] Fourth, the lack of efficient monitoring methods for key parameters on the lunar surface hinders scientific research and resource exploration.

[0011] Physical parameters of the lunar surface, such as dielectric constant, microscopic roughness, rock distribution, and regolith thickness, are key indicators for studying lunar geological evolution, space weathering processes, and assessing the potential of resources like water ice. Currently, obtaining these parameters mainly relies on in-situ measurements from a few landers or high-resolution remote sensing of specific areas, which suffers from limited spatial coverage, sparse sampling points, and a lack of time-series data. This situation severely restricts scientists from constructing global-scale models of lunar surface characteristics and makes it difficult to support the demand for refined environmental information in future lunar resource exploration and base site selection. Therefore, developing a technology capable of large-scale, high-frequency, and long-term continuous monitoring of lunar surface parameters has become an urgent technological need in the fields of lunar science and exploration. Summary of the Invention

[0012] In view of this, the present invention provides a lunar surface parameter monitoring device and method based on spaceborne GNSS-R and simplified polarization technology, aiming to solve the problems of existing lunar exploration technologies in achieving global coverage, high spatiotemporal resolution, and long-term continuous monitoring, as well as the technical gaps and adaptability problems of traditional GNSS-R technology and fully polarized measurement methods in deep space exploration applications. This will improve the inversion capability and monitoring efficiency of key physical parameters such as the dielectric constant, roughness, and composition distribution of the lunar surface, and provide reliable technical support for lunar scientific research, resource exploration, and future base construction.

[0013] To achieve the above objectives, the present invention adopts the following technical solution:

[0014] In a first aspect, the present invention provides a lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology, comprising:

[0015] A highly stable satellite platform is used to carry various components and provide high-precision attitude and orbit control, radiation resistance and thermal control capabilities, and energy support.

[0016] A high-sensitivity multi-frequency GNSS-R receiver is used to receive direct signals from multiple navigation satellite systems and signals reflected from the lunar surface;

[0017] A simplified polarized antenna array, connected to the high-sensitivity multi-frequency GNSS-R receiver, is used to receive the left-hand circularly polarized and right-hand circularly polarized components of the reflected signal;

[0018] The on-orbit real-time signal processing and inversion unit is connected to the high-sensitivity multi-frequency GNSS-R receiver and is used to process the reflected signal in real-time on-orbit to generate a delayed Doppler map and extract polarization feature parameters, and invert the physical parameters of the lunar surface based on this.

[0019] A high-capacity radiation-resistant data storage and deep space telemetry module is connected to the on-orbit real-time signal processing and inversion unit to store inversion results and data and transmit them down to the ground.

[0020] The ground calibration, verification, and product generation system is used to receive downlink data, perform fine processing and verification, and generate the final scientific data product.

[0021] In one specific implementation, the high-sensitivity multi-frequency GNSS-R receiver is configured to support receiving navigation signals from at least two different systems: GPS, BeiDou, and Galileo navigation satellite systems.

[0022] In one specific implementation, the reduced polarization antenna array is configured to acquire the left-hand circular polarization component and the right-hand circular polarization component of the reflected signal through a dual-polarization design, and to calculate the polarization ratio for surface parameter inversion based thereon.

[0023] In one specific implementation, the on-orbit real-time signal processing and inversion unit includes an on-orbit preprocessing module configured to perform correlation operations on the reflected signal to generate a delayed Doppler map, extract waveform feature values ​​from the delayed Doppler map, and calculate the polarization ratio based on the signal received by the reduced polarization antenna array.

[0024] In one specific implementation, the on-orbit real-time signal processing and inversion unit further includes a parameter inversion engine configured to invert the dielectric constant and roughness of the lunar surface based on a physical model using the waveform eigenvalues ​​and polarization ratio.

[0025] In one specific implementation, the parameter inversion engine further integrates a machine learning model configured to analyze a feature vector composed of the waveform feature values ​​and polarization ratios to classify lunar surface composition and optimize the inversion results of the physical model.

[0026] In one specific implementation scheme, the on-orbit real-time signal processing and inversion unit implements real-time signal processing and feature extraction functions based on a field-programmable gate array or an application-specific integrated circuit.

[0027] In one specific implementation scheme, the high-capacity radiation-resistant data storage and deep space telemetry module adopts a hierarchical storage strategy, prioritizing the storage of key lunar surface parameter products obtained through inversion, and performing lossy compression or triggered storage on intermediate feature data.

[0028] Secondly, the present invention provides a method for monitoring lunar surface parameters based on spaceborne GNSS-R and reduced polarization technology, executed by the aforementioned lunar surface parameter monitoring device based on spaceborne GNSS-R and reduced polarization technology, and comprising the following steps:

[0029] By using a spaceborne simplified polarized antenna array and a GNSS-R receiver, the system synchronously receives direct signals from navigation satellites and signals reflected from the lunar surface, and obtains the left-hand and right-hand circular polarization components of the reflected signals.

[0030] The reflected signal is preprocessed in real time on orbit to generate a delayed Doppler image and extract characteristic parameters including polarization ratio.

[0031] Using the extracted feature parameters, the dielectric constant and roughness of the lunar surface are calculated through an inversion model;

[0032] The parameter products obtained from the inversion are stored in orbit.

[0033] The stored data is transmitted downlink to the ground station via a deep space telemetry link;

[0034] The downlink data is processed and verified at the ground station to generate the final lunar surface parameter data product.

[0035] In one specific implementation scheme, the inversion model is a multi-parameter collaborative inversion model, which inverts the dielectric constant through the polarization ratio and inverts the surface roughness through the waveform characteristics of the delayed Doppler plot, and the inversion process integrates data from different navigation satellite systems and different frequency bands.

[0036] Compared with existing technologies, the lunar surface parameter monitoring device and method based on spaceborne GNSS-R and reduced polarization technology described in this invention are used for high-precision, global, and long-term monitoring of lunar surface physical parameters. By integrating a high-sensitivity multi-frequency GNSS-R receiver and a reduced polarization antenna array on a spaceborne platform, and combining on-orbit real-time signal processing and AI-assisted inversion models, efficient and reliable inversion of key parameters such as lunar surface dielectric constant, roughness, and composition distribution is achieved. This effectively improves the spatiotemporal coverage, data accuracy, and system applicability of lunar surface characteristic detection, and has the following beneficial effects:

[0037] 1. It has achieved high-precision, global coverage and high-frequency monitoring of the physical parameters of the lunar surface, overcoming the shortcomings of traditional optical, laser and radar detection technologies, which are limited by lighting conditions, have limited spatial resolution, are costly and difficult to achieve long-term continuous observation.

[0038] 2. By adopting simplified polarization technology, the system's radio frequency channel structure and complexity are simplified while ensuring the ability to acquire key polarization information. This enables lightweight and low-power payload design, making it more suitable for resource-constrained deep space exploration missions.

[0039] 3. By utilizing multi-band GNSS signal reception and AI-assisted inversion models, various lunar surface parameters can be retrieved in a coordinated manner, improving the inversion accuracy and automation level of parameters such as dielectric constant, roughness, and composition distribution, and enhancing the ability to identify and classify different lunar surface types.

[0040] 4. It has expanded the application scope of GNSS-R technology to the field of deep space exploration, provided feasible technical solutions for solving challenges such as capturing weak signals on the lunar surface and modeling signals from celestial bodies without atmosphere, and promoted the development of planetary remote sensing technology.

[0041] 5. By employing on-board real-time processing and hierarchical storage strategies, the pressure on downlink data transmission is effectively reduced, improving the overall efficiency of the monitoring system and the availability of data products.

[0042] In summary, the technical solution provided by this invention can effectively extend the application scope of GNSS-R technology to the field of deep space exploration, promoting the development of planetary remote sensing technology. Through its unique global coverage and high-frequency monitoring capabilities, this device is expected to compensate for the shortcomings of traditional optical, laser, and radar detection methods, providing continuous and reliable data support for cutting-edge scientific research such as lunar surface topography evolution, resource distribution characteristics, and environmental change monitoring, demonstrating clear scientific value and application prospects. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 This is a block diagram of the overall composition of a lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology as described in this invention.

[0045] Figure 2 This is a flowchart illustrating the overall process of a lunar surface parameter monitoring method based on spaceborne GNSS-R and simplified polarization technology, as described in this invention.

[0046] Figure 3 This is a schematic diagram illustrating the principle of simplified polarization technology and signal reception.

[0047] Figure 4 This is a schematic diagram illustrating the principle of real-time on-orbit signal processing and feature extraction.

[0048] Figure 5 This is a schematic diagram illustrating the principle of the lunar surface parameter inversion model.

[0049] Figure 6 Flowchart for ground verification and accuracy evaluation of lunar surface parameter inversion results. Detailed Implementation

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Global Navigation Satellite System Reflectance Measurement (GNSS-R), as an emerging passive remote sensing technology, has shown great potential in the field of Earth remote sensing due to its advantages such as low cost, all-day / all-weather operation, and high spatiotemporal resolution, providing a new technical approach for lunar surface parameter detection. It should be noted that the GNSS signal in the Earth-Moon space is extremely weak, with power far lower than the thermal noise floor of the Earth's surface. The core idea for acquisition is to accumulate signal energy through extremely long integration time, thereby "extracting" it from the noise. This invention employs a strategy of "long-time coherent integration + incoherent integration," for example, using a coherent integration of up to 500 ms, followed by 20 incoherent integrations, with a total processing time of 10 seconds, to achieve an acquisition sensitivity of -185 dBW, overcoming the challenge of acquiring weak Earth-Moon signals.

[0052] This invention introduces GNSS-R technology into the field of deep space exploration, aiming to provide a lunar surface parameter monitoring device and method based on spaceborne GNSS-R and reduced polarization technology. The device receives signals reflected or scattered by navigation satellites from the lunar surface via a spaceborne platform, and, combined with an innovative design of a reduced polarization receiver, achieves high-precision inversion of multiple physical parameters of the lunar surface, such as dielectric constant, roughness, and composition distribution.

[0053] The simplified polarization technique, as an advanced polarization measurement method, can efficiently acquire polarization information of reflected signals while maintaining a lightweight and low-power system. Compared with traditional full polarization measurement, this technique effectively reduces system complexity and data volume while maintaining high parameter inversion accuracy, making it very suitable for resource-constrained deep space exploration missions.

[0054] Through the above-mentioned technical approach, this invention effectively overcomes the limitations of existing lunar exploration technologies in terms of global coverage, spatiotemporal resolution, and mission cost, providing new technical support for lunar scientific research and resource exploration.

[0055] like Figure 1 As shown, the lunar surface parameter monitoring device based on spaceborne GNSS-R and reduced polarization technology described in this invention is used to perform high-precision monitoring of lunar surface physical parameters using Global Navigation Satellite System (GNSS-R) reflectance measurement technology and a reduced polarization receiver. It is a highly integrated spaceborne system comprising six core modules that work collaboratively to achieve high-precision, global, and long-term monitoring of lunar surface physical parameters.

[0056] 1 High-stability satellite platform

[0057] This platform serves as the carrier and foundation of the entire monitoring device. To meet the demands of the lunar extreme environment (vast temperature differences, high-intensity radiation, long-duration missions) and the requirements of precision measurement, the platform must possess the following characteristics:

[0058] High-precision attitude and orbit control: Equipped with high-precision star sensors, gyroscopes and propulsion systems to ensure satellite attitude stability and keep the GNSS-R receiver antenna continuously aligned with the target area on the lunar surface.

[0059] Strong radiation resistance and thermal control capabilities: The use of radiation-hardened electronic components and effective passive / active thermal control design ensures long-term reliable operation of the equipment in the extreme environment of lunar space.

[0060] Ample energy and payload support: Equipped with large-size solar panels and high-capacity batteries, providing a continuous and stable power supply for the entire system, especially the receiver and processing unit. The platform must provide standard mechanical, electrical, and thermal interfaces for the payload.

[0061] 2 High-sensitivity multi-frequency GNSS-R receiver

[0062] This is the core sensor of the system, specifically designed to capture extremely weak navigation satellite signals reflected from the lunar surface. Specifically, the acquisition employs an FFT-based parallel code phase search algorithm, combined with navigation message bit prediction / cancellation technology, achieving a coherent integration time of up to 500ms, supplemented by 20 incoherent integrations, for a total acquisition processing time of 10 seconds. The frequency search step size is 0.5Hz, and the code phase search step size is 0.5 chip. Tracking utilizes a vector tracking loop based on a high-precision orbital dynamics model, with the carrier tracking loop (PLL) bandwidth set at 0.5Hz and the code tracking loop (DLL) bandwidth set at 0.1Hz to maximize thermal noise suppression. The receiver reference clock uses a temperature-controlled crystal oscillator (OCXO) with short-term stability (1-second Allen variance) better than 1e-11 and an instantaneous dynamic range greater than 70dB. The design goal is to achieve an acquisition sensitivity of -185dBW and a tracking sensitivity of -190dBW.

[0063] Multi-band signal reception: Supports receiving signals from multiple navigation satellite systems such as GPS (L1, L2, L5), Beidou (B1, B2, B3), and Galileo (E1, E5, E6), and utilizes the differences in the sensitivity of different frequency signals to surface characteristics to improve the dimensionality and accuracy of inversion information.

[0064] High sensitivity and dynamic range: Advanced acquisition and tracking algorithms (such as high-gain correlators and vector tracking loops) are employed to ensure reliable locking and tracking of weak reflected signals at the Earth-Moon distance.

[0065] Special front-end design: Equipped with a low-noise amplifier (LNA) and bandpass filter to suppress noise to the maximum extent and enhance useful signals, ensuring the quality of the original signal.

[0066] 3-Simplified Polarized Antenna Array

[0067] This module serves as the "gateway" for receiving signals, and its polarization characteristics are crucial for retrieving surface parameters. In a preferred embodiment of the invention, the reduced polarization antenna array employs an integrated dual-port circularly polarized microstrip antenna to simultaneously and continuously receive the left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) components of the reflected signal, ensuring that both channels observe the same scattering event and avoiding signal decorrelation. Key antenna design parameters include: port isolation >20dB to ensure the independence of the two polarization channels and prevent signal crosstalk; axial ratio <3dB (within the primary observation angle) to generate high-quality circularly polarized waves and ensure the accuracy of polarization measurements. The antenna backend connects to two independent and symmetrical receiver RF front-ends, which perform low-noise amplification, down-conversion, and digitization of the RHCP and LHCP signals, respectively. Compared to traditional fully polarized systems, this design significantly simplifies the number of RF channels and system complexity while ensuring the acquisition of critical polarization information.

[0068] like Figure 3 As shown, the dual-polarization / reduced polarization design employs a specially designed antenna array that can simultaneously or rapidly alternately receive the left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) components of the reflected signal. Compared to traditional fully polarized systems, this design significantly simplifies the number of RF channels and system complexity while ensuring the acquisition of key polarization information (such as polarization ratio and phase difference).

[0069] High gain and beamforming: The antenna needs to have a certain gain and be able to use beamforming technology to align the main lobe with the lunar surface in order to improve the signal-to-noise ratio and suppress interference from other directions.

[0070] 4. On-orbit real-time signal processing and inversion unit

[0071] This module is the "brain" of the device, responsible for transforming raw data into scientific products. To support this real-time processing, the following hardware resource requirements apply: The selected FPGA must be a mid-to-high-end model (such as the Xilinx Kintex UltraScale series), providing thousands of DSP blocks to support parallel correlation operations. For example, to achieve parallel processing of 16 delays × 5 Dopplers, approximately 80 independent correlator channels are needed. Simultaneously, high-speed external memory (such as DDR4) is required to provide a stable memory bandwidth of 10-20 GB / s for caching and accumulating DDM data. The core algorithm steps include:

[0072] 1. Signal preprocessing and correlation: Local carrier and C / A code are generated in parallel on the FPGA and mixed and coherently integrated with the input signal;

[0073] 2. DDM generation, performing incoherent accumulation on the correlator output;

[0074] 3. Feature extraction: Extract parameters such as peak power and delay spread from the DDM in real time.

[0075] like Figure 4 As shown, on-orbit preprocessing: Based on high-performance FPGA or ASIC chips, the coherent / incoherent integration and correlation operations of the reflected signal are completed in real time to generate a delayed Doppler image (DDM), and characteristic values ​​such as power, waveform, signal-to-noise ratio, and polarization ratio (such as LHCP / RHCP) are extracted from it.

[0076] like Figure 5 As shown, the parameter inversion engine integrates inversion algorithms based on physical models (such as geometric optical models and small perturbation models) and machine learning models (such as neural networks and support vector machines). Utilizing preprocessed feature data, it inverts parameters such as the dielectric constant, RMS height (roughness), correlation length, and lunar regolith thickness of the lunar surface in real-time or near real-time.

[0077] The physical model is specifically based on the geometric optics model. Dielectric constant. The inversion is achieved through polarization ratio The relationship between Fresnel reflection coefficient and the core mathematical relationship is as follows:

[0078]

[0079] in, and Here, is the Fresnel reflection coefficient, and is the angle of incidence. and complex permittivity Functions:

[0080]

[0081] By solving the above equations numerically, the dielectric constant can be deduced from the measured polarization ratio. Roughness parameter (root mean square height) Relevant length The inversion of the DDM is obtained by fitting the waveform characteristics (such as the slope of the leading and trailing edges and the distribution width) with the physical relationship between the scattering cross section derived from the geometric optics model and the DDM waveform characteristics (such as the slope of the leading and trailing edges and the distribution width) through a nonlinear optimization algorithm.

[0082] The machine learning model, acting as a parallel inversion engine, employs a hybrid model combining convolutional neural networks and fully connected networks. Its inputs include normalized dual-polarized DDM data blocks (e.g., 32×32 pixels) and geometric parameters such as the angle of incidence. The outputs are regression values ​​for dielectric constant, root-mean-square height, and correlation length. Training of this model relies on a large-scale synthetic dataset generated by the aforementioned physical model to address the scarcity of real-world lunar labeling data. A validation set is constructed using measured data from Apollo lunar regolith samples and topographic data from the lunar orbiter, enabling training and validation of the model on the ground. In in-orbit applications, this AI model serves as a rapid proxy for the physical model, significantly improving real-time processing efficiency.

[0083] Data compression and filtering: Perform lossy or lossless compression on massive amounts of raw intermediate data, and have a "trigger-based" storage function (such as storing high-resolution data only when a change in the feature of interest is detected) to save valuable on-board storage resources and downlink bandwidth.

[0084] The specific logic of the "trigger-based" storage function is based on key parameters and data quality indicators of real-time inversion, setting multi-level, combinable judgment conditions. This mainly includes:

[0085] 1. Scientific Event Trigger: When the dielectric constant retrieved in real time deviates significantly from the normal range (e.g., a set threshold greater than 5) or a significant abrupt change in spatial gradient is detected, the storage of high-resolution data or raw data is triggered. This may indicate targets of high scientific value, such as water bodies, ice, or enrichment of special minerals.

[0086] 2. Data Quality Trigger: When the signal-to-noise ratio (SNR) or peak coherence of the delayed Doppler image (DDM) falls below a preset threshold (e.g., 10 dB), raw data storage is triggered. This indicates an encounter with an extreme roughness or complex scattering scenario, requiring in-depth analysis by the ground system.

[0087] The above conditions can be configured with AND / OR logic combinations, such as assigning the highest storage priority when both dielectric constant anomaly and signal-to-noise ratio sudden drop are detected simultaneously, in order to capture the most scientifically valuable transient events.

[0088] 5 high-capacity radiation-resistant data storage and deep space telemetry modules

[0089] This module is responsible for data storage and transmission.

[0090] High-capacity solid-state storage: Employs radiation-resistant solid-state storage (SSD) with TB-level capacity to store preprocessed data, inversion results, and necessary raw data fragments.

[0091] High-speed deep-space communication link: Equipped with a high-speed data transmission system in X-band or Ka-band, used to transmit stored data at high speed downlink to the ground-based deep-space tracking and control network when the satellite passes over the ground station. The communication protocol needs to have strong error correction capabilities to cope with deep-space channel loss.

[0092] 6. Ground Calibration, Validation, and Product Generation System

[0093] This is the ground-based support system that completes the final data processing and value extraction.

[0094] Data reception and decoding: Receive downlink data through the ground station and perform demodulation, decoding and decryption.

[0095] Refined processing and inversion: Utilizing more powerful ground computing resources and running more complex inversion algorithms, the on-orbit results are verified and optimized to generate higher-level data products.

[0096] like Figure 6 As shown, calibration and verification: By comparing the measured data or high-resolution images of other lunar probes (such as LRO and Chang'e), the inversion results are cross-validated and the system is calibrated to continuously optimize the inversion algorithm.

[0097] The verification plan specifically includes:

[0098] Select a region with known true values: Use the measured dielectric constant (approximately 3.0) of lunar soil samples from the Apollo 16 landing site as the key absolute calibration point; use in-situ probe data from the Chang'e 3 and 4 landing sites to verify the surface roughness inversion results.

[0099] Multi-source data cross-validation: The inversion results of this device are cross-validated and fused with rock abundance data obtained by the Diviner instrument of the Lunar Reconnaissance Orbiter (LRO) of the United States, as well as lunar soil characteristics obtained by inversion from microwave radiometer and radar data of China's Chang'e series missions (such as CE-1 / CE-2).

[0100] Accuracy Quantitative Assessment: The root mean square error (RMSE) and coefficient of determination (R²) are used as quantitative indicators to evaluate the consistency between the GNSS-R inversion product and the true / reference data, and finally determine the inversion accuracy (e.g., the target is a dielectric constant inversion accuracy better than ±0.5).

[0101] Visualization and distribution: Generate global lunar surface parameter distribution maps, time-series animations, data reports, and other products, and distribute them to global research institutions through the data platform to serve lunar science research and engineering mission planning.

[0102] Based on the aforementioned lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology, this invention further discloses a lunar surface parameter monitoring method based on spaceborne GNSS-R and simplified polarization technology. This lunar surface parameter monitoring method is a closed-loop, automated processing chain, from signal acquisition to scientific product generation, specifically comprising the following six core steps:

[0103] 1. Signal Reception and Acquisition

[0104] • Description: The spaceborne GNSS-R receiver synchronously receives direct signals from visible navigation satellites (such as GPS, BeiDou, and Galileo) (for precise positioning and time synchronization) and weak signals reflected or scattered by the lunar surface through a simplified polarized antenna array.

[0105] • Key points:

[0106] o Multi-band coordinated reception: Signals from multiple frequency bands such as L1 / L2 / L5, B1 / B2 / B3, E1 / E5 / E6 are acquired in parallel. By utilizing the differences in penetration and sensitivity of different frequencies to the dielectric properties and roughness of the lunar surface, a rich data foundation is provided for subsequent multi-parameter inversion.

[0107] o Polarization information acquisition: The antenna array simultaneously acquires the left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) components of the reflected signal, and fully records the amplitude and phase information of the signal, providing raw data for simplified polarization analysis.

[0108] o Navigation message decoding: Real-time decoding of navigation messages in direct signals to accurately obtain satellite ephemeris, timestamps, and clock difference information, used to calculate the geometric position of the reflection point and perform precise time synchronization.

[0109] 2. On-orbit real-time preprocessing and feature extraction

[0110] • Description: In the on-board processing unit, the raw intermediate frequency sampling data is processed in real time to generate a feature dataset that can be used for inversion.

[0111] • Key points:

[0112] o Correlation processing and DDM generation: The reflected signal is correlated with the locally generated copy code to generate a Delay-Doppler Map (DDM). This map is a core observation of GNSS-R technology, and its waveform characteristics (such as leading edge slope, peak power, and waveform width) are closely related to lunar surface roughness.

[0113] o Polarization parameter calculation: Based on the acquired dual-polarization data, key polarization characteristic parameters are calculated in real time, mainly including the polarizability ratio (LHCP / RHCP) and differential phase. The polarizability ratio is very sensitive to the surface dielectric constant, while the differential phase contains information related to surface roughness.

[0114] o Feature Packaging: Extract a set of standardized feature values ​​(such as peak power, lead edge width, polarization ratio, signal-to-noise ratio, etc.) from each DDM and polarization data, and compress and pack them to greatly reduce the amount of data that needs to be transmitted downlink.

[0115] March table parameter inversion

[0116] • Description: Using preprocessed feature parameters, the physical properties of the lunar surface are inverted through built-in physical and machine learning models.

[0117] • Key points:

[0118] o Multi-parameter collaborative inversion model:

[0119] Dielectric constant inversion: The inversion model is mainly established by using the quantitative relationship between polarizability and surface Fresnel reflectance. A higher dielectric constant usually corresponds to denser basalt or water ice-rich regions, while a lower value may indicate loose, dry lunar regolith.

[0120] Roughness inversion: Using the characteristics of the leading edge slope or differential phase of the DDM waveform, based on the geometric optical model or small perturbation model, the root mean square height (RMS Height) and related length of the lunar surface are inverted to quantify the surface roughness.

[0121] o Machine Learning-Assisted Classification and Optimization: This method utilizes pre-trained machine learning models (such as random forests and convolutional neural networks) to comprehensively analyze feature vectors. The model can learn the complex nonlinear relationships between different lunar surface types (such as crater, maria, highlands, and permanently shadowed areas) and signal features. It can not only perform preliminary classification of surface components but also optimize the inversion results of traditional physical models, improving accuracy and robustness.

[0122] Data fusion: Integrating inversion results from different navigation satellite systems and frequency bands to generate more reliable parameter products with better spatial continuity.

[0123] 4. Data Management and On-Orbit Storage

[0124] • Description: Effective management and large-capacity storage of the parameter products, intermediate feature data, and some original data fragments obtained from the inversion.

[0125] • Key points:

[0126] o Tiered storage strategy: A tiered storage strategy is adopted. Key parameters retrieved in real time (such as global roughness and dielectric constant distribution maps) are stored in their entirety with the highest priority; intermediate feature data can be lossily compressed or stored on demand based on data value; raw data is only stored under specific detection modes or when anomalies are detected.

[0127] Radiation-hardened storage: All data is written to radiation-hardened high-capacity solid-state drives (SSDs) to ensure long-term data integrity and reliability in deep space radiation environments.

[0128] 5. Data downlink transmission

[0129] • Description: When the satellite is in the visible arc of a ground deep space station, a high-speed downlink is activated to transmit the data stored on the satellite to the ground station.

[0130] • Key points:

[0131] o Adaptive data transmission: Based on the quality of the satellite-to-ground link, the ground station's receiving capability, and data priority, the transmission rate and data packet size are dynamically adjusted to ensure efficient and reliable transmission of the most critical data.

[0132] Protocol and Error Correction: Employs a special communication protocol (such as CFDP-CCSDS File Delivery Protocol) suitable for long-latency, high-error-rate deep space channels, and is equipped with powerful forward error correction coding to ensure complete and error-free data transmission.

[0133] 6. Ground finishing and product generation

[0134] • Description: At the ground station, the received data undergoes further refinement and verification, and the final scientific data products are generated for scientists to use.

[0135] • Key points:

[0136] o Data unpacking and reconstruction: Decode and unpack the downlink data stream to reconstruct the global or regional lunar surface parameter dataset.

[0137] o Fine-grained inversion and correction: Utilizing more powerful ground computing resources, running more complex inversion algorithms, and performing systematic error correction and calibration on the on-orbit results based on known ground truth values ​​(such as Apollo landing point data).

[0138] o Multi-source data fusion and verification: The inversion results of this device are compared, fused and verified with other lunar exploration data such as laser altimeter, orbital radar, and optical images to improve the accuracy and reliability of the product.

[0139] Visualization and Publishing: Generate advanced data products such as global lunar surface dielectric constant distribution maps, roughness maps, and composition classification maps, and publish them through the data platform in the form of graphics, images, and data files for use by global scientific research and engineering teams.

[0140] The following is a specific embodiment.

[0141] Hardware implementation

[0142] • Satellite platform: CubeSat or a small lunar orbiter.

[0143] • GNSS-R receiver: Supports multiple frequency bands such as L1 / L2 / B1 / B2, and integrates a simplified polarized antenna array.

[0144] • Processing unit: Real-time polarization processing and feature extraction are implemented based on FPGA or ASIC.

[0145] Software implementation

[0146] • Signal processing algorithm: Implemented in C++ / Python, supporting multi-band and multi-polarization signal processing.

[0147] • Inversion model: A lunar surface parameter classification model trained using PyTorch / TensorFlow.

[0148] • Ground station software: Provides a web-based visualization platform that supports data export and sharing.

[0149] The various embodiments described in this specification are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology, characterized in that, include: A highly stable satellite platform is used to carry various components and provide high-precision attitude and orbit control, radiation resistance and thermal control capabilities, and energy support. A high-sensitivity multi-frequency GNSS-R receiver is used to receive direct signals from multiple navigation satellite systems and signals reflected from the lunar surface; A simplified polarized antenna array, connected to the high-sensitivity multi-frequency GNSS-R receiver, is used to receive the left-hand circularly polarized and right-hand circularly polarized components of the reflected signal; The on-orbit real-time signal processing and inversion unit is connected to the high-sensitivity multi-frequency GNSS-R receiver and is used to process the reflected signal in real-time on-orbit to generate a delayed Doppler map and extract polarization feature parameters, and invert the physical parameters of the lunar surface based on this. A high-capacity radiation-resistant data storage and deep space telemetry module is connected to the on-orbit real-time signal processing and inversion unit to store inversion results and data and transmit them down to the ground. The ground calibration, verification, and product generation system is used to receive downlink data, perform fine processing and verification, and generate the final scientific data product.

2. The lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology according to claim 1, characterized in that, The high-sensitivity multi-frequency GNSS-R receiver is configured to support receiving navigation signals from at least two different systems: GPS, BeiDou, and Galileo.

3. The lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology according to claim 1, characterized in that, The simplified polarized antenna array is configured to acquire the left-hand circular polarization component and the right-hand circular polarization component of the reflected signal through a dual-polarization design, and to calculate the polarization ratio for surface parameter inversion based on this.

4. A lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology according to claim 1, characterized in that, The on-orbit real-time signal processing and inversion unit includes an on-orbit preprocessing module, which is configured to perform correlation operations on the reflected signal to generate a delayed Doppler map, extract waveform feature values ​​from the delayed Doppler map, and calculate the polarization ratio based on the signal received by the reduced polarization antenna array.

5. A lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology according to claim 4, characterized in that, The on-orbit real-time signal processing and inversion unit also includes a parameter inversion engine, which is configured to invert the dielectric constant and roughness of the lunar surface based on a physical model using the waveform eigenvalues ​​and polarization ratio.

6. A lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology according to claim 5, characterized in that, The parameter inversion engine further integrates a machine learning model, which is configured to analyze the feature vector composed of the waveform feature values ​​and polarization ratio to classify the lunar surface composition and optimize the inversion results of the physical model.

7. A lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology according to claim 1, characterized in that, The on-orbit real-time signal processing and inversion unit implements real-time signal processing and feature extraction functions based on field-programmable gate arrays or application-specific integrated circuits.

8. A lunar surface parameter monitoring device based on spaceborne GNSS-R and simplified polarization technology according to claim 1, characterized in that, The high-capacity radiation-resistant data storage and deep space telemetry module adopts a hierarchical storage strategy, prioritizing the storage of key lunar surface parameter products obtained through inversion, and performing lossy compression or triggered storage on intermediate feature data.

9. A method for monitoring lunar surface parameters based on spaceborne GNSS-R and simplified polarization technology, characterized in that, The method is performed by the apparatus of any one of claims 1 to 8 and includes the following steps: By using a spaceborne simplified polarized antenna array and a GNSS-R receiver, the system synchronously receives direct signals from navigation satellites and signals reflected from the lunar surface, and obtains the left-hand and right-hand circular polarization components of the reflected signals. The reflected signal is preprocessed in real time on orbit to generate a delayed Doppler image and extract characteristic parameters including polarization ratio. Using the extracted feature parameters, the dielectric constant and roughness of the lunar surface are calculated through an inversion model; The parameter products obtained from the inversion are stored in orbit. The stored data is transmitted downlink to the ground station via a deep space telemetry link; The downlink data is processed and verified at the ground station to generate the final lunar surface parameter data product.

10. A method for monitoring lunar surface parameters based on spaceborne GNSS-R and simplified polarization technology according to claim 9, characterized in that, The inversion model is a multi-parameter collaborative inversion model, which inverts the dielectric constant through the polarization ratio and inverts the surface roughness through the waveform characteristics of the delayed Doppler plot. The inversion process integrates data from different navigation satellite systems and different frequency bands.

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