A sea surface oil spill monitoring system based on GNSS-R receiver

Abstract

This invention provides a GNSS-R receiver-based marine oil spill monitoring system, comprising an unmanned aerial vehicle (UAV) platform, a GNSS-R receiver mounted on the UAV platform and connected in sequence, a signal processing module, an oil spill feature extraction module, a data storage and transmission module, and a ground station data processing module. In this system, the GNSS-R receiver uses reduced polarization to acquire GNSS scattered signals from the ocean surface, effectively addressing the need for independent inversion and application of typical marine disaster parameter inversion. The GNSS-R receiver supports multi-band and multi-polarization GNSS scattered signal reception, improving the accuracy and reliability of oil spill detection and enabling high-precision oil spill detection under complex meteorological conditions.

Description

Technical Field

[0001] The present invention relates to the fields of marine environment monitoring technology and low-altitude economy, and in particular to a sea surface oil spill monitoring system based on a GNSS-R receiver. The system is based on the Beidou multi-band and uses an unmanned aerial vehicle platform equipped with a GNSS-R receiver to perform sea surface oil spill monitoring. Background Art

[0002] Traditional oil spill monitoring methods (such as radar and optical remote sensing) are restricted by weather and lighting conditions, are costly and have limited coverage.

[0003] Specifically, although traditional oil spill monitoring methods (such as radar and optical remote sensing) play an important role in marine environmental monitoring, they have many limitations in practical applications, especially in terms of weather, lighting conditions, cost and coverage. These problems seriously affect the efficiency and accuracy of monitoring.

[0004] First, traditional oil spill monitoring methods are extremely sensitive to weather conditions. For example, while synthetic aperture radar (SAR) can operate in all weather conditions, strong winds, high waves, or rainfall can severely interfere with radar signals, leading to inaccurate monitoring results. Optical remote sensing technology is even more dependent on good lighting conditions. Cloudy days, nighttime conditions, or haze can significantly reduce the image quality of optical sensors, even rendering valid data impossible to obtain. This high dependence on weather and lighting significantly reduces the monitoring capabilities of traditional methods in complex meteorological conditions, making them unable to meet the needs of real-time, continuous monitoring.

[0005] Secondly, traditional monitoring methods are expensive. The research, development, manufacturing, and maintenance of radar and optical remote sensing equipment require significant capital investment, especially for high-resolution satellite and airborne sensors. Furthermore, data processing and analysis require specialized technical teams and high-performance computing resources, further increasing the overall cost of monitoring. For many developing countries or resource-limited regions, this high cost makes traditional monitoring methods difficult to scale up and apply.

[0006] Furthermore, traditional methods have limited coverage. While satellite remote sensing technology can provide large-scale monitoring data, its spatial resolution is typically low, making it difficult to capture small-scale oil spills or precisely identify spill boundaries. While airborne sensors can provide high-resolution data, their coverage is limited by flight time and range, making continuous monitoring of large areas impossible. This limited coverage makes traditional methods inadequate for responding to sudden oil spills, making it difficult to meet the demands of rapid response and precise positioning.

[0007] Furthermore, the accuracy of traditional monitoring methods is affected by a variety of factors. For example, optical remote sensing technology is susceptible to interference from light reflected from the sea surface, making it particularly difficult to distinguish between oil spill areas and non-spill areas, especially in direct sunlight or under high sea waves. While radar technology can penetrate clouds and operate at night, its ability to identify oil spill types is limited, making it difficult to distinguish between different types of oil slicks (such as crude oil, diesel, or lubricants). This lack of accuracy significantly reduces the effectiveness of traditional methods in complex marine environments.

[0008] On the other hand, existing GNSS-R technologies are mostly based on satellites or ground platforms, with low spatial resolution, making it difficult to achieve high-precision monitoring of local areas.

[0009] Specifically, existing GNSS-R (Global Navigation Satellite System-Reflected Signal) technology primarily relies on satellites or ground-based platforms. While capable of large-scale marine environmental monitoring, its low spatial resolution makes it difficult to meet the needs of high-precision monitoring in localized areas. Limited by orbital altitude and sensor performance, satellite platforms typically only offer spatial resolutions of hundreds to thousands of meters, making them unable to accurately capture details of small-scale oil spills or complex sea surface features. While ground-based platforms can provide higher resolution, their coverage is limited and their deployment flexibility is limited, making them incapable of meeting the demands of rapid response to sudden oil spills. These limitations render existing GNSS-R technology inadequate for high-precision monitoring of localized areas.

[0010] In contrast, drone platforms offer a novel solution for the application of GNSS-R technology. Due to their flexible deployment, high maneuverability, and low cost, drones are capable of acquiring high-spatial-resolution GNSS scattered signal data while flying at low altitudes. Equipped with multi-band, multi-polarization GNSS-R receivers, drones can achieve precise monitoring of localized areas, accurately identifying key parameters such as oil spill boundaries and oil film thickness. Furthermore, drone monitoring is highly timely, enabling rapid arrival at the scene of an oil spill and obtaining real-time monitoring data, providing strong support for emergency decision-making.

[0011] The development of drone monitoring technology is of great significance to the study of the low-altitude economy. The low-altitude economy refers to various economic activities carried out using low-altitude airspace resources, including drone logistics, agricultural monitoring, environmental monitoring and other fields. The application of drones in GNSS-R oil spill monitoring has not only promoted the innovation of low-altitude monitoring technology, but also provided a new growth point for the development of the low-altitude economy. Through the drone platform, efficient monitoring of water environments such as oceans, rivers, and lakes can be achieved, providing rich data support for low-altitude economic research and promoting the improvement and development of related industrial chains. Therefore, the application of drones equipped with GNSS-R technology not only has broad prospects in the field of oil spill monitoring, but also injects new vitality into the study of the low-altitude economy.

[0012] On the other hand, existing GNSS-R devices usually only support a single frequency band or polarization channel and cannot fully utilize the advantages of multi-band and multi-polarization signals.

[0013] Specifically, existing GNSS-R (Global Navigation Satellite System Reflected Signal) equipment has significant limitations in practical applications. This is primarily due to the fact that it typically supports only a single frequency band or a single polarization channel, failing to fully utilize the advantages of multi-band, multi-polarization signals. GNSS signals consist of multiple frequency bands (such as GPS's L1 / L2, Beidou's B1 / B2, and Galileo's E1 / E5), each with varying sensitivity to sea surface characteristics. For example, low-frequency signals are more sensitive to sea surface roughness, while high-frequency signals are better suited for detecting subtle changes such as oil slicks. However, most existing equipment only supports a single frequency band, which prevents it from fully capturing changes in sea surface characteristics, limiting monitoring accuracy and application scope. Furthermore, the polarization characteristics of GNSS scattered signals (such as left-hand circular polarization (LR), right-hand circular polarization (RR), vertical linear polarization (VR), and horizontal linear polarization (HR)) are crucial for oil spill detection. Different polarization channels exhibit significant differences in the reflection characteristics of oil slicks and seawater, but existing equipment typically only supports a single polarization channel, failing to fully utilize the advantages of multi-polarization signals, further reducing monitoring accuracy and reliability.

[0014] More notably, there are currently virtually no drone-mounted GNSS-R equipment for oil spill surface monitoring on the market. Drone platforms offer the advantages of flexible deployment, high spatial resolution, and low cost, making them suitable for addressing the shortcomings of traditional satellite and ground-based platforms. However, due to the difficulty of technical integration, existing GNSS-R equipment is large and consumes high power, making it difficult to meet the payload and endurance requirements of drones. Currently, drones are only equipped with single-polarization GNSS-R receivers, not multi-polarization receivers. This hardware makes it impossible to effectively monitor ocean parameters. Furthermore, the design and signal processing algorithms for multi-band, multi-polarization GNSS-R receivers present technical challenges, hindering their commercialization.

[0015] Therefore, developing a lightweight, low-power, multi-band, multi-polarization GNSS-R monitoring device suitable for UAVs has become an important research direction in the field of oil spill monitoring. Such a device can not only improve monitoring accuracy and timeliness, but also provide strong technical support for low-altitude economic research and marine environmental protection.

[0016] In summary, traditional oil spill monitoring methods have significant limitations in terms of weather adaptability, cost, coverage, and monitoring accuracy. These limitations not only affect the reliability and practicality of monitoring data, but also restrict their widespread global application. Therefore, it is particularly important to develop a new low-cost, high-precision, all-weather, and wide-coverage oil spill monitoring technology. In recent years, oil spill monitoring methods based on GNSS-R (Global Navigation Satellite System Reflected Signal) technology have gradually gained attention. Utilizing multi-band, multi-polarization GNSS scattered signals, they can achieve high-precision oil spill detection under complex meteorological conditions. Combined with the advantages of low cost and wide coverage, they bring new solutions to the field of oil spill monitoring. Summary of the Invention

[0017] The purpose of the present invention is to provide a sea surface oil spill monitoring system based on GNSS-R receivers to achieve high spatial resolution, flexible deployment and real-time monitoring.

[0018] To achieve the above-mentioned objectives, the present invention provides a sea surface oil spill monitoring system based on a GNSS-R receiver, comprising an unmanned aerial vehicle (UAV) platform, a GNSS-R receiver installed on the UAV platform and connected in sequence, a signal processing module, an oil spill feature extraction module, a data storage and transmission module, and a ground station data processing module.

[0019] The GNSS-R receiver uses a reduced polarization mode to receive GNSS scattered signals from the sea surface. The oil spill feature extraction module is configured to determine the dielectric constant values ​​of the sea surface in different regions and the corresponding oil spill areas based on the DDM waveforms and polarization characteristics of the actually measured GNSS scattered signals. The dielectric constant values ​​of the sea surface in different regions and the corresponding oil spill areas are pre-calculated using a GNSS scattered signal simulation module for the DDM waveforms and polarization characteristics of the oil-free sea surface and different oil spilled sea surfaces under different polarization channels, and the pre-calculated results are compared with the DDM waveforms and polarization characteristics of the actually measured GNSS scattered signals.

[0020] The simulation module of the GNSS scattered signal is configured to perform the following steps:

[0021] S1: establishing an oil spill sea surface dielectric constant module, wherein the oil spill sea surface dielectric constant module is used to determine the dielectric constants of the oil-free sea surface and different oil spill sea surfaces according to the dielectric constants of seawater and oil film as the dielectric constants of the sea surface, so as to obtain the Fresnel reflection coefficient;

[0022] S2: Establishing an oil spill sea surface roughness spectrum module, wherein the oil spill sea surface roughness spectrum module is used to obtain a wave spectrum model of the oil spill sea surface based on a traditional wave spectrum model and the impact of the oil spill on the wave spectrum, and further obtain a surface spectral density function and a surface root mean square height;

[0023] S3: establishing a microwave scattering module, wherein the microwave scattering module is used to determine the microwave scattering coefficient of the sea surface under different polarization channels according to the Fresnel reflection coefficient, the surface spectral density function and the surface root mean square height;

[0024] S4: establishing a DDM simulation module, wherein the DDM simulation module is used to simulate and obtain a DDM waveform according to GNSS signal parameters and microwave scattering coefficients under different polarization channels of the sea surface;

[0025] S5: Use the oil spill sea surface dielectric constant module to obtain the Fresnel reflection coefficient, use the oil spill sea surface roughness spectrum module to obtain the surface spectral density function and surface root mean square height, then use the microwave scattering module to determine the microwave scattering coefficient of the sea surface under different polarization channels. Finally, use the DDM simulation module to simulate the DDM waveform, and obtain the polarization characteristics based on the DDM waveforms of different polarization channels. The DDM waveform and polarization characteristics are used as the pre-calculated results.

[0026] The microwave scattering module is based on an integral equation model. It determines the microwave scattering coefficient under different polarization channels of the sea surface according to the input parameters such as incident angle, frequency, polarization channel, Fresnel reflection coefficient, surface spectral density function and surface root mean square height. When the polarization direction p of the incident signal and the polarization direction q of the scattered signal are different, the integral equation model is used to calculate the microwave scattering coefficient under different polarization channels through the polarization correlation kernel function. To realize the calculation of the full polarization scattering matrix, the backscattering coefficient related to the polarization channel pq is obtained as the microwave scattering coefficient.

[0027] The GNSS-R receiver supports receiving GNSS scattered signals in multiple frequency bands.

[0028] The signal processing module is configured to preprocess the received GNSS scattered signal and extract key parameters; the preprocessing includes filtering, amplification, and digitization; the key parameters include the power, phase, polarization characteristics, and DDM waveform of the actually measured GNSS scattered signal.

[0029] The data storage and transmission module supports both real-time data transmission and offline storage.

[0030] The ground station data processing module is configured to receive data transmitted by the data storage and transmission module and perform in-depth analysis and visualization processing.

[0031] In-depth analysis refers to the use of machine learning algorithms to analyze data. Specifically, it includes: based on the polarization characteristics of GNSS scattered signals in different areas and the value of the dielectric constant of the sea surface, using machine learning algorithms to classify and identify oil spill characteristics as analysis results; classification and identification results include whether there is an oil spill, the type of oil spill, the thickness of the oil film, and the distribution density.

[0032] The ground station data processing module is connected to the oil spill report generation module, and the oil spill report generation module is configured to obtain the analysis results derived by the ground station data processing module and generate an oil spill report according to the analysis results.

[0033] The GNSS-R receiver in the present invention's marine oil spill monitoring system uses reduced polarization to acquire GNSS scattered signals from the ocean surface, effectively addressing the independent inversion and application requirements for typical marine disaster parameter inversion. Furthermore, the GNSS-R receiver supports the reception of multi-band (Beidou, GPS, Galileo, etc.) and multi-polarization (LR, RR, VR, HR) GNSS scattered signals. The combination of multi-band (such as Beidou, GPS, Galileo) and multi-polarization (LR, RR, VR, HR) GNSS-R receivers improves the accuracy and reliability of oil spill detection, enabling high-precision oil spill detection even in complex meteorological conditions. Furthermore, the GNSS-R receiver, when deployed on an unmanned aerial vehicle (UAV), achieves rapid response and wide-area coverage through the UAV platform, offering the advantages of low cost and wide coverage. The UAV platform's flexible deployment capabilities enable rapid response to sudden oil spill incidents, covering areas difficult to reach by traditional satellite and ground platforms. Furthermore, the GNSS-R receiver's high spatial resolution enables precise capture of details of small oil spills, providing unprecedented accuracy for oil spill monitoring.

[0034] Furthermore, by integrating signal processing, oil spill feature extraction, and data transmission modules, the present invention fully automates the entire process from data acquisition to result generation, significantly reducing monitoring costs and time. The present invention's low cost, high efficiency, and all-weather monitoring capabilities offer broad application prospects in areas such as marine environmental monitoring, oil spill emergency response, and pollution assessment, providing strong technical support for marine environmental protection and ecological security. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a device structure diagram of the sea surface oil spill monitoring system based on the GNSS-R receiver of the present invention. DETAILED DESCRIPTION

[0036] like Figure 1 The figure shows a GNSS-R receiver-based marine oil spill monitoring system according to one embodiment of the present invention, comprising an unmanned aerial vehicle (UAV) 10 and a ground station 20. UAV 10 includes a UAV platform 11, and a GNSS-R receiver 12, a signal processing module 13, an oil spill feature extraction module 14, and a data storage and transmission module 15, which are installed on and sequentially connected to the UAV platform 11. Ground station 20 includes a ground station data processing module 21 and an oil spill report generation module 22.

[0037] UAV platform 11 utilizes either multi-rotor or fixed-wing drones, offering long endurance and high stability, making it suitable for complex marine environment monitoring missions. Multi-rotor drones are suitable for low-altitude, high-precision localized monitoring, while fixed-wing drones are suited for rapid, large-scale, long-distance inspections. Both types of drones can serve as receivers for GNSS-R reflected signals, but their varying speeds result in different Doppler shifts, resulting in differences in the resulting DDM waveforms and, consequently, in the parameters used in the inversion algorithm.

[0038] The core equipment carried by the UAV platform 11 includes a GNSS-R receiver 12 , a signal processing module 13 , an oil spill feature extraction module 14 and a data storage and transmission module 15 .

[0039] Among them, the GNSS-R receiver 12 uses a reduced polarization mode to receive GNSS scattered signals from the sea surface. In the following, the oil spill feature extraction module 14 compares the DDM waveform and polarization characteristics of the actually measured GNSS scattered signals with the pre-calculated results obtained by the GNSS scattered signal simulation module to obtain the dielectric constant of the sea surface, significantly improving the accuracy and reliability of oil spill detection.

[0040] The GNSS-R receiver 12 supports multi-band GNSS scattered signal reception. This includes the frequency bands used by Beidou B1 / B2, GPS L1 / L2, and Galileo E1 / E5. Multi-band signal reception allows the receiver to fully utilize the differences in sensitivity of different frequency bands to sea surface features. For example, low-frequency signals (such as GPS L1 and Beidou B1) are more sensitive to sea surface roughness and wave height, while high-frequency signals (such as Galileo E5 and GPS L5) are better able to detect subtle changes such as oil slicks. By integrating multi-band data, the GNSS-R receiver can more comprehensively analyze the characteristic variations of sea surface reflected signals, thereby improving the accuracy of oil spill detection.

[0041] Reduced polarization mode specifies the polarization state of the transmitter, i.e., the polarization state of the transmitter is RHCP, and the polarization states of the receiver are left-hand circular polarization (LHCP), right-hand circular polarization (RHCP), vertical polarization (V), and horizontal polarization (H). Polarization channels include left-hand circular polarization (LR), right-hand circular polarization (RR), vertical linear polarization (VR), and horizontal linear polarization (HR). The first letter in LR, RR, VR, and HR represents the receiving polarization state, and the second represents the transmitting polarization state. L stands for left-hand circular polarization (LHCP), R stands for right-hand circular polarization (RHCP), V stands for vertical polarization, and H stands for horizontal polarization. Therefore, the reduced polarization mode provides more effective observation parameters for the inversion algorithm, thereby generating more observation data. Polarization sensitivity for oil spill monitoring can be determined from all four polarizations, a feat previously unattainable with a single polarization. Reduced polarization is a new GNSS-R remote sensing method that uses GNSS-R receivers to fully accept all four polarization modes. By analyzing the sensitivity of each polarization mode and using polarization combinations such as polarization ratios for effective oil spill monitoring, it increases the single polarization state of existing GNSS-R remote sensing, which is beneficial for monitoring and inversion.

[0042] Receiving signals from different polarization channels further enhances the monitoring capabilities of GNSS-R receivers. Different polarization channels (left-hand circular polarization (LR), right-hand circular polarization (RR), vertical linear polarization (VR), and horizontal linear polarization (HR)) exhibit significant differences in the reflection characteristics of the sea surface and oil slicks. For example, the reflection intensity of right-hand circular polarization signals from oil slicks is generally lower than that from seawater, while the reflection intensity of left-hand circular polarization signals is more sensitive to sea surface roughness. By analyzing the reflection characteristics of signals from multiple polarization directions, the GNSS-R receiver 12 can more accurately distinguish oil spill areas from normal sea surfaces and even identify the type and thickness of oil slicks. Furthermore, the fusion of multi-polarization data can effectively reduce interference from environmental noise and improve the stability of monitoring results.

[0043] In practical applications, the GNSS-R receiver 12, through the signal processing module 13 and the oil spill feature extraction module 14, captures the characteristics of GNSS scattered signals in real time to rapidly generate high-precision oil spill monitoring data. This oil spill monitoring data not only includes the location and extent of the oil spill area, but also provides detailed information such as the thickness and distribution density of the oil film, providing a scientific basis for oil spill emergency response and pollution assessment. Combined with the high spatial resolution and flexible deployment capabilities of the UAV platform, the multi-band, multi-polarization signal reception capabilities of the GNSS-R receiver 12 provide an efficient, low-cost, and all-weather solution for marine oil spill monitoring, with broad application prospects.

[0044] The signal processing module 13 is configured to preprocess the received GNSS scattered signal and extract key parameters to obtain key parameters of the GNSS scattered signal. The preprocessing includes filtering, amplification, and digitization. The key parameters include the power, phase, polarization characteristics, and DDM waveform of the actually measured GNSS scattered signal.

[0045] The signal processing module is one of the core components of the GNSS-R sea surface oil spill monitoring system. Its main task is to efficiently process the received GNSS scattered signals to extract key characteristic parameters and provide reliable data support for oil spill detection.

[0046] First, the signal processing module 13 preprocesses the raw GNSS scattered signals, including filtering, amplification, and digitization. The filtering stage uses a low-pass or bandpass filter to remove high-frequency noise and low-frequency interference, ensuring an optimal signal-to-noise ratio. The amplification stage uses a high-gain amplifier to enhance weak signals, enabling them to be effectively processed by subsequent circuits. The digitization stage uses a high-speed analog-to-digital converter (ADC) to convert the analog signals into digital signals, facilitating subsequent algorithm processing and analysis.

[0047] After preprocessing, the signal processing module 13 further extracts key parameters of the reflected signal, such as power, phase, and polarization channel. Power information reflects the intensity of the reflected signal and can be used to distinguish between oil slicks and normal sea surfaces, as oil slicks typically significantly reduce the power of the reflected signal. Phase information, on the other hand, reflects changes in sea surface height and roughness. By analyzing phase differences, minute fluctuations in the sea surface can be accurately calculated, thereby identifying oil spill areas. Polarization characteristic parameters are a unique advantage of GNSS-R technology. Different polarization channels (such as left-hand circular polarization (LR), right-hand circular polarization (RR), vertical linear polarization (VR), and horizontal linear polarization (HR)) exhibit significant differences in their reflection characteristics for oil slicks and seawater. By extracting power and phase information from different polarization channels to obtain different polarization characteristics, the accuracy and reliability of oil spill detection can be further improved. Polarization characteristics can include polarization dual-station radar scattering coefficients or polarization ratios (i.e., the ratio of the effective reflectivities corresponding to different polarization channels).

[0048] The oil spill feature extraction module 14 is configured to determine the dielectric constant values ​​of the sea surface in different regions and the corresponding oil spill areas based on the DDM waveform and polarization characteristics (such as the polarization ratios of LR / RR and VR / HR) of the actually measured GNSS scattered signals.

[0049] In the present invention, the dielectric constant values ​​of different sea surface areas and the corresponding oil spill areas are determined by precalculating the DDM waveforms and polarization characteristics of oil-free sea surfaces and different oil spilled sea surfaces under different polarization channels using a GNSS scattered signal simulation module. The precalculated results are then compared with the DDM waveforms and polarization characteristics of the actual measured GNSS scattered signal. The GNSS scattered signal simulation module does not need to be installed on the drone platform 11; its calculation results only need to be stored in the data storage and transmission module 15.

[0050] The simulation module for GNSS scattered signals is set up to perform the following steps:

[0051] Step S1: establishing an oil spill sea surface dielectric constant module, wherein the oil spill sea surface dielectric constant module is used to determine the dielectric constants of the oil-free sea surface and different oil spill sea surfaces according to the dielectric constants of seawater and oil film as the dielectric constant of the sea surface, so as to obtain the Fresnel reflection coefficient;

[0052] Step S2: establishing an oil spill sea surface roughness spectrum module, wherein the oil spill sea surface roughness spectrum module is used to obtain a wave spectrum model of the oil spill sea surface based on a traditional wave spectrum model and the impact of the oil spill on the wave spectrum, and further obtain a surface spectral density function and a surface root mean square height;

[0053] Step S3: establishing a microwave scattering module, wherein the microwave scattering module is used to determine the microwave scattering coefficient of the sea surface under different polarization channels according to the Fresnel reflection coefficient, the surface spectral density function and the surface root mean square height;

[0054] Step S4: establishing a DDM simulation module, wherein the DDM simulation module is used to simulate and obtain a DDM waveform based on GNSS signal parameters and microwave scattering coefficients under different polarization channels of the sea surface;

[0055] Step S5: The Fresnel reflection coefficient is obtained using the oil spill sea surface dielectric constant module, and the surface spectral density function and surface root mean square height are obtained using the oil spill sea surface roughness spectrum module. The microwave scattering coefficient of the sea surface under different polarization channels is then determined using the microwave scattering module. Finally, the DDM waveform is simulated using the DDM simulation module, and polarization characteristics are obtained based on the DDM waveforms of different polarization channels. The DDM waveform and polarization characteristics are used as the pre-calculated results.

[0056] In step S1, seawater is a complex electrolyte solution whose dielectric constant is significantly affected by temperature, salinity, and frequency. In microwave frequency bands (such as the L band and C band), the dielectric constant of seawater is usually expressed in complex form, that is, the dielectric constant of seawater is:

[0057] ,

[0058] in, Indicates the polarizability of seawater, usually between 70 and 80; imaginary part It represents the loss characteristics of seawater, which is closely related to the electrical conductivity and is usually between 30 and 40. j is an imaginary unit.

[0059] The dielectric constant model of seawater (such as the Debye model) can describe its variation with frequency and temperature. Accordingly, the dielectric constant of seawater is:

[0060] ,

[0061] in, is the dielectric constant of seawater in the high-frequency limit, is the static dielectric constant of seawater, is the relaxation time of seawater, is the electrical conductivity of seawater, is the angular frequency, is the dielectric constant of vacuum.

[0062] Among them, the dielectric constant of seawater at the high frequency limit is , the dielectric constant of seawater in static state , relaxation time of seawater , the conductivity of seawater It is related to the properties of seawater and can be obtained through experimental calibration. The properties of seawater include salinity and temperature.

[0063] The dielectric constant model of the oil film can be obtained through experimental measurement or empirical formula. In this embodiment, when the oil spill is crude oil, the change in the dielectric constant of the crude oil film with frequency can be described by the Cole-Cole model. Therefore, the dielectric constant of the oil film is:

[0064] ,

[0065] in, is a distribution parameter describing the width of dielectric relaxation, is the dielectric constant of the oil film at the high frequency limit, is the static dielectric constant of the oil film, is the relaxation time of the oil film, is the angular frequency.

[0066] Among them, the dielectric constant of the oil film at the high frequency limit is , the static dielectric constant of the oil film , relaxation time of oil film They are all related to the properties of the oil film and can be obtained through experimental calibration. The properties of the oil film include the oil material, the thickness of the oil film and the temperature.

[0067] After obtaining the dielectric constants of each layer of seawater and oil film, the following formula is used to obtain the equivalent dielectric constant of the oil spill sea surface :

[0068] ,

[0069] in, is the dielectric constant of the oil film, which is about 2.5 - j0.1 in the C band for crude oil; is the oil-water coupling empirical coefficient, which is about 0.05. is the sea surface wave number, d‌ represents the oil film thickness, in meters.

[0070] Fresnel reflection coefficient The value of (Fresnel reflection coefficient) is obtained by solving the equivalent dielectric constant of the oil spill sea surface. According to the equivalent dielectric constant of the oil spill sea surface, the corrected Fresnel reflection coefficient is obtained as follows:

[0071] When p=v (vertical polarization), ,

[0072] When p=h (horizontal polarization), ,

[0073] The Fresnel reflection coefficient solved from this is Includes Fresnel reflection coefficients for parallel and perpendicular polarizations 、 .

[0074] In step S2, the wave spectrum of the oil spill sea surface is converted into a rough spectrum, thereby obtaining a surface spectral density function The roughness spectrum is used to describe the energy distribution of the two-dimensional wave number space of the sea surface height fluctuation. It's the wave spectrum The wave number domain form of is the wave number, and the two are related by wave number-frequency conversion. In the present invention, the surface spectral density function and surface RMS height The combined effect of the above two indicates the degree of surface roughness.

[0075] The step S2 specifically includes:

[0076] Step S21: Create a traditional ocean wave spectrum Model, wave spectrum The model includes the frequency spectrum and directional spectrum ;

[0077] Among them, the wave spectrum Indicates the wave energy at frequency and azimuth The distribution of waves. The model usually includes the frequency spectrum and directional spectrum , that is, the spectrum of waves The model is:

[0078] = × ,

[0079] Among them, the frequency spectrum Used to describe the distribution of energy with frequency, directional spectrum Describing wave energy at different azimuths The distribution characteristics on the surface of the ocean are used to correct the anisotropy of the wave spectrum and reflect the influence of wind direction on the wave propagation direction.

[0080] On an oil-spilled sea surface, the oil film alters the surface roughness and wave characteristics, affecting the shape and energy distribution of the wave spectrum. Specifically, the presence of an oil film has the following effects on surface waves: 1. Damping effect: The oil film suppresses the generation of small-scale waves (such as capillary waves) and reduces surface roughness. 2. Energy redistribution: The oil film alters the distribution of wave energy, shifting energy from high frequencies to low frequencies. 3. Directional change: The oil film alters the directional distribution of waves, making them more concentrated. These effects lead to changes in the shape and energy distribution of the wave spectrum.

[0081] Step S22: Correct the frequency spectrum according to the property parameters of the oil film , get the frequency spectrum of the oil spill sea surface , using the frequency spectrum of the oil spill sea surface To replace the original frequency spectrum , and obtain a model of the wave spectrum of the oil spill sea surface.

[0082] In order to describe the wave characteristics of the rough sea surface caused by oil spills, the traditional wave spectrum model can be modified.

[0083] In this embodiment, the traditional wave spectrum model adopts Pierson-Moskowitz spectrum (PM spectrum), whose input parameters include wind speed, wind direction, etc. The PM spectrum is suitable for fully developed wind waves, and its frequency spectrum Expressed as:

[0084] ,

[0085] in, is the frequency spectrum amplitude constant, usually taken as 0.0081; is the acceleration due to gravity; is the peak frequency, is the frequency.

[0086] Correct the frequency spectrum according to the property parameters of the oil film , get the frequency spectrum of the oil spill sea surface , specifically including: introducing a damping factor related to the property parameters of the oil film To correct the frequency spectrum , get the frequency spectrum of the oil spill sea surface :

[0087] ,

[0088] in, is the damping factor related to the property parameters of the oil film, is the frequency spectrum.

[0089] The directional spectrum With existing technology, there is no need to make corrections based on the oil spill situation.

[0090] Surface spectral density function and surface RMS height for:

[0091] ,

[0092] ,

[0093] ,

[0094] in, represents the surface spectral density function, represents the surface RMS height, represents the group velocity, is the spectral density function of frequency and direction, is the wind speed parameter, is the angular frequency, is the free space wave number, and h is the water depth.

[0095] In step S3, the surface root mean square height is and the surface spectral density function The microwave scattering module is introduced to describe surface roughness and solve for the scattering coefficient.

[0096] In this embodiment, the microwave scattering module is based on an integral equation model (IEM) and determines the microwave scattering coefficient of the sea surface under different polarization channels according to input parameters such as incident angle, frequency, polarization channel, Fresnel reflection coefficient, surface spectral density function, and surface root mean square height.

[0097] The Integral Equation Model (IEM) is a microwave scattering model for rough surfaces. It effectively simulates scattering behavior under varying dielectric properties, surface roughness, and incident angles. Therefore, in this example, the IEM is used as the microwave scattering model for the sea surface to describe the scattering characteristics.

[0098] The integral equation model (IEM model) decomposes the scattering field of a rough surface into a coherent scattering part and an incoherent scattering part. The coherent scattering part is composed of the surface root mean square height The incoherent scattering is caused by the surface roughness and can be solved by the perturbation method.

[0099] When the polarization direction of the incident signal is the same as the polarization direction of the scattered signal, the backscattering coefficient (i.e., the bistatic scattering coefficient) can be expressed as:

[0100] ,

[0101] in, Polarization channel Backscatter coefficient, polarization channel The first p represents the polarization direction of the incident signal and p represents the polarization direction of the scattered signal. The polarization directions of both can be vertical polarization h or horizontal polarization v; is the angle of incidence, is the coherent scattering term, is the incoherent scattering term.

[0102] Among them, the coherent scattering term Expressed as:

[0103] ,

[0104] in, is the wave number, is the Fresnel reflection coefficient, is the surface root mean square height, is the angle of incidence.

[0105] In the reduced polarization mode, the coherent component usually only exists in the circular polarization co-polarization channel (RR or LL), so it is necessary to convert the linear polarization Fresnel reflection coefficient into a circular polarization expression. =h, is the Fresnel reflection coefficient of vertical polarization, =v, is the Fresnel reflection coefficient for horizontal polarization.

[0106] Incoherent scattering term Expressed as:

[0107] ,

[0108] in, is the wave number, is the surface root mean square height, is the angle of incidence, is the surface spectral density function, is the polarization coupling coefficient, is the free space wave number.

[0109] Among them, the polarization coupling coefficient The mathematical expression is:

[0110] ,

[0111] in, is the angle of incidence, is the azimuth, is the surface wave number vector, is the Kirchhoff scattering term, is the corrected curvature compensation term, also known as the high-order curvature compensation term.

[0112] According to the existing technology, it is currently believed that incoherent scattering includes Kirchhoff terms, Kirchhoff scattering terms It can be expressed as:

[0113] ,

[0114] in, is the Fresnel reflection coefficient, the formula of which has been detailed above; is the geometric projection factor.

[0115] Curvature compensation item before correction for:

[0116] ,

[0117] in, is the surface wave number vector, is the projection of the surface wave number, k is the electromagnetic wave number, is the angle of incidence, is the Fresnel reflection coefficient, 、 is the empirical curvature function.

[0118] Corrected curvature compensation term for:

[0119] = ‌,

[0120] in, is the corrected curvature compensation term, is the curvature compensation term before correction, ≈300 rad / m is the characteristic cutoff wave number of the oil film, tanh is the hyperbolic tangent function, is the free space wave number, and Δκ is the wave number variation.

[0121] Therefore, the polarization coupling coefficient and surface spectral density function directly related to the energy distribution as an integral kernel function, and directly related to the dielectric constant through the Fresnel reflection coefficient To affect the polarization coupling coefficient The oil spill information is used to correct the polarization coupling coefficient through dielectric constant, surface spectral density, and high-order curvature terms. The correction of the dielectric constant is as described above, and the correction of the surface spectral density is achieved by the correction of the wave spectrum described above.

[0122] When the polarization direction p of the incident signal is different from the polarization direction q of the scattered signal, the integral equation model (IEM) can be used to obtain the polarization-dependent kernel function. To realize the calculation of the full polarization scattering matrix, the backscattering coefficient related to the polarization channel pq is obtained as the microwave scattering coefficient.

[0123] The full polarization scattering matrix (Sinclair matrix) is:

[0124] ,

[0125] in, is the scattering amplitude under the polarization channel pq, p represents the polarization direction of the incident signal, q represents the polarization direction of the scattered signal, and the polarization channel pq can be taken as 、 、 、 .

[0126] Among them, the integral equation model (IEM) is used to obtain the polarization-related kernel function. To realize the calculation of the full polarization scattering matrix, specifically including:

[0127] Step S31: For each polarization channel (HH, VV, HV, VH), calculate the corresponding polarization correlation kernel function :

[0128] For co-polarization (HH / VV):

[0129] ,

[0130] ,

[0131] Where n is the order, 、 are the Fresnel reflection coefficients for parallel and perpendicular polarizations, is the angle of incidence, is the scattering angle, , where for backscattering, is 180°.

[0132] For cross-polarization (HV / VH), the polarization kernel function It usually includes second-order derivative terms of surface tilt and asymmetric scattering contributions, which need to be calculated through vector radiation transfer equations or high-order expansion.

[0133] The polarization kernel function The definition is derived from the classic small perturbation model (SPM), which was first systematically explained by Ulaby et al. in the "Microwave Remote Sensing" series. For details, see [Fung, AK (1994). Microwave Scattering and Emission Models for Users. Artech House.] and [Ulaby, FT, Moore, RK, & Fung, AK (1982). Microwave Remote Sensing: Active and Passive, Volume II. Addison-Wesley]. Chapter 3 of [Fung, AK (1994). Microwave Scattering and Emission Models for Users. Artech House.] derives the polarization coefficient of the small perturbation model (SPM) in detail. Small perturbation model polarization coefficient The specific form of is directly quoted from the standard formula of the small perturbation model (SPM), and the literature refers to Fung (1994) or Ulaby (1982).

[0134] Step S32: Polarization correlation kernel function Substituting the bistatic scattering formula of the integral equation model (IEM model) into the backscattering coefficients of the four polarization channels is calculated. ;

[0135] The bistatic scattering formula of the integral equation model (IEM model) is:

[0136] ,

[0137] in, is the wave number, is the surface root mean square height, is the angle of incidence, n is the order, is the wave vector component of the electromagnetic wave number, where is the projection of the surface wave number in the electromagnetic wave number space, Calculated based on the incident angle θ; is the surface spectral density function, ρ(0) is the value of the normalized surface height autocorrelation function at zero lag (i.e., zero distance), and its value is 1.

[0138] Note: For the cross-polarization term (HV / VH), it is usually necessary to calculate to a higher order (such as ) to capture asymmetric scattering effects.

[0139] Step S33: Based on the scattering coefficients of the four polarization channels , and obtain the complete full polarization scattering matrix , used to synthesize the backscattering coefficient of any polarization channel.

[0140] Among them, in the synthesized polarization channel pq, the polarization direction p of the incident signal is usually RHCP, and the polarization direction of the scattered signal can include various polarization directions such as LHCP, RHCP, H, V, etc.

[0141] According to the relationship between the scattering coefficient and the scattering amplitude , combined with phase information (the phase spectrum of the surface correlation function needs to be considered), the complete full polarization scattering matrix is ​​obtained Thus, the present invention can synthesize the backscatter coefficient of any transceiver polarization channel (such as circular polarization and 45° linear polarization) through the linear combination of a portion of the transceiver channels, without using the dielectric constant to calculate the backscatter coefficients of all polarization channels, which greatly reduces the amount of calculation and makes the generated results applicable to the GNSS-R receiver 12 of the present invention using a reduced polarization mode.

[0142] In step S4, the DDM waveform is simulated using an electromagnetic model of a time delay and Doppler simulation system, so that the DDM waveform is a function of the time delay and the Doppler frequency.

[0143] According to the electromagnetic model of the time delay and Doppler simulation system, in the DDM waveform, the replica signal delay is And the frequency of the replicated signal is The energy value of the received signal at for:

[0144] ,

[0145] in, It is the energy value of the received signal obtained at the receiver, which is a function of time delay and frequency; is the energy of the transmitted satellite-of-opportunity signal; is the antenna gain of the satellite-of-opportunity signal; : Antenna gain of the reflected signal receiver; is the distance between the receiver and the surface reflectivity point; is the distance between the opportunity satellite signal and the saline surface reflectivity point; is the wavelength of the satellite-of-opportunity signal; is the coherent integration time; is the attenuation caused by Doppler shift, and are the frequencies of the replica signal and the incident signal, respectively. These parameters are all GNSS signal parameters.

[0146] is the microwave scattering coefficient of the sea surface, which is calculated according to step S3 above and configured according to the polarization channel actually used by the receiver; is a trigonometric function, and are the delay of the replica signal and the delay of the incident signal, respectively. The replica signal is a copy of the incident signal that is generated by the receiver according to the signal parameters of the satellite and is completely synchronized with the incident signal. A is the effective scattering area, that is, the Green scintillation area, which is only related to the illuminated area. dA is the area element.

[0147] Because the present invention calculates a full-polarization scattering matrix and uses it to determine the microwave scattering coefficients for different polarization channels on the sea surface, it can be applied to obtaining DDM waveforms for any polarization channel in a compact polarimetry mode. Compact polarimetry (CP) refers to a technique that transmits signals in a single polarization state and receives signals in all polarization states (such as single circular polarization transmission and dual linear polarization reception). This technique, combined with a mathematical model to reconstruct full polarization information, reduces the number of polarization channels actually received (for example, from four fully polarized channels to two). At the same time, key polarization parameters (such as Stokes subvectors) are derived through an algorithm, thereby reducing hardware complexity while retaining sufficiently comprehensive information.

[0148] In step S5, polarization characteristics (such as polarization ratio) are obtained by subtracting, adding, dividing, and multiplying the DDM waveforms of different polarization channels, so as to find the optimal polarization combination that is most sensitive to oil spill monitoring, thereby performing effective monitoring.

[0149] The data storage and transmission module 15 is configured to store the pre-calculated results of the simulation module of the GNSS scattered signal and the data processed by the signal processing module 13 and the oil spill feature extraction module 14, and transmit the data processed by the signal processing module 13 and the oil spill feature extraction module 14 to the ground station part 20 via a wireless communication link.

[0150] The data storage and transmission module 15 can support both real-time data transmission and offline storage, ensuring efficient management and flexible application of monitoring data.

[0151] First, the data storage and transmission module 15 is responsible for storing the key parameters of the GNSS scattered signal processed by the signal processing module 13, the data processed by the oil spill feature extraction module 14, and relevant environmental parameters (such as wind speed, wave height, sea surface temperature, etc.). These relevant environmental parameters are measured by the environmental sensors on the drone. To meet the needs of long-term monitoring, the storage module typically uses a large-capacity, low-power solid-state drive (SSD) or flash memory device, capable of storing monitoring data for days or even weeks. In addition, the data storage and transmission module 15 also has data compression and encryption functions to reduce storage space usage and ensure data security. In offline mode, the data storage and transmission module 15 can operate independently, temporarily storing data locally and transmitting it again when communication conditions are restored, ensuring data integrity and continuity.

[0152] Secondly, the data storage and transmission module 15 transmits monitoring data to the ground station in real time via a wireless communication link. Depending on the application scenario, various communication methods can be selected, such as 4G / 5G mobile networks, satellite communications, or low-power wide area networks (LPWANs). 4G / 5G communications are suitable for offshore areas, offering high bandwidth and low latency, enabling real-time data transmission. Satellite communications are suitable for offshore or uninhabited areas, ensuring global data coverage. LPWAN technologies (such as LoRa or NB-IoT) are suitable for low-power, long-distance transmission requirements. The data transmission module also has adaptive capabilities, dynamically adjusting transmission rates and priorities based on network conditions to ensure timely transmission of critical data.

[0153] Furthermore, the data storage and transmission module 15 supports both real-time data transmission and offline storage modes. In real-time transmission mode, monitoring data is sent directly to the ground station via a wireless communication link for real-time viewing and analysis by researchers and decision-makers. In offline storage mode, data is temporarily stored locally and transferred in batches once communication conditions are restored. This dual-mode design not only enhances system flexibility but also ensures data reliability in complex environments.

[0154] Through efficient data storage and transmission modules15, the GNSS-R sea surface oil spill monitoring system can achieve seamless management and application of monitoring data, providing strong technical support for oil spill emergency response, pollution assessment and marine environmental protection.

[0155] The ground station data processing module 21 is a key link in the GNSS-R sea surface oil spill monitoring system. It is configured to receive data transmitted by the data storage and transmission module, perform in-depth analysis and visualization processing, and provide a scientific basis for oil spill emergency response and marine environmental protection.

[0156] First, the ground station data processing module 21 receives the data transmitted by the data storage and transmission module, including the power, phase, and polarization characteristics of the GNSS scattered signal and the results processed by the oil spill feature module.

[0157] The ground station data processing module 21 is configured to first perform data verification and cleaning to remove noise and outliers, ensuring data accuracy and reliability. Subsequently, it performs in-depth analysis, which involves using machine learning algorithms to analyze the data. For example, it can classify and identify oil spill characteristics based on the polarization characteristics of GNSS scattered signals in different regions and the dielectric constant of the sea surface using machine learning algorithms (such as support vector machines and neural networks). This analysis results in efficient and intelligent oil spill monitoring, reducing manual intervention and improving processing speed. The classification and identification results include whether an oil spill has occurred, the type of oil spill, the thickness of the oil film, and the distribution density. Oil spill types include crude oil, diesel, or lubricating oil.

[0158] Support vector machines (SVMs) effectively process high-dimensional data by constructing optimal classification hyperplanes, making them suitable for oil spill identification with small sample sizes. Neural networks (such as convolutional neural networks (CNNs)) mimic the neuronal structure of the human brain and can automatically learn complex nonlinear features, making them suitable for processing and analyzing large-scale data. These algorithms, through learning and training from historical data, can automatically identify oil spill areas and distinguish between different types of oil slicks (such as crude oil, diesel, or lubricating oil), thereby enhancing the intelligence of monitoring. In this embodiment, the machine learning algorithm uses the polarization characteristics of GNSS scattered signals (such as the polarization ratios of LR / RR and VR / HR) and the dielectric constant as input data, and outputs the presence of an oil spill, the type of spill, and the thickness and distribution density of the oil slick.

[0159] Polarization ratio is a key parameter for oil spills. Different polarization channels (such as left-hand circular polarization (LR), right-hand circular polarization (RR), vertical linear polarization (VR), and horizontal linear polarization (HR)) exhibit significant differences in the reflection characteristics of oil slicks and normal sea surfaces. For example, the reflection intensity of right-hand circular polarization signals from oil slicks is generally lower than that from seawater, while left-hand circular polarization signals are more sensitive to sea surface roughness. By calculating polarization ratios (such as LR / RR and VR / HR), the difference in reflection between oil slicks and seawater can be quantified, effectively distinguishing oil spill areas. Furthermore, changes in dielectric constant are also crucial for identifying oil spills. The dielectric constant of oil slicks is generally lower than that of seawater, resulting in changes in the amplitude and phase of the reflected signal. By analyzing changes in dielectric constant, the presence and distribution of oil spills can be further verified.

[0160] Therefore, in practical applications, this invention can not only generate information on the location and extent of the oil spill, but also provide detailed information such as the thickness and distribution density of the oil film, providing a scientific basis for oil spill emergency response and pollution assessment. By combining multi-band, multi-polarization GNSS-R signals with machine learning algorithms, it provides an efficient, accurate, and intelligent solution for marine oil spill monitoring, with broad application prospects.

[0161] Furthermore, the ground station data processing module 21 boasts powerful visualization capabilities, transforming complex monitoring data into intuitive graphs and charts. For example, it can generate high-resolution oil spill distribution maps, clearly showing the location, extent, and density of the spill area. It can also produce dynamic time series graphs, reflecting the trend and speed of the oil spill's spread. Furthermore, the ground station data processing module 21 can estimate the spill area and oil film thickness, and, by combining environmental parameters (such as wind speed, wave height, and current direction), predict the spill's spread path, providing support for emergency decision-making.

[0162] The ground station data processing module 21 is connected to the oil spill report generation module 22. The oil spill report generation module 22 is configured to obtain the analysis results exported by the ground station data processing module 21 and generate oil spill reports in various formats (such as images, tables, and electronic reports) based on the analysis results for storage or sharing to facilitate subsequent research and application.

[0163] Through the efficient analysis and visualization functions of the ground station data processing module, the GNSS-R sea surface oil spill monitoring system can provide comprehensive and accurate data support for oil spill emergency response, pollution assessment and marine environmental protection, and has important application value and social significance.

[0164] Experimental results:

[0165] The working process of the sea surface oil spill monitoring system based on GNSS-R receiver of the present invention is as follows:

[0166] 1. Signal Reception: The UAV platform 11 is equipped with a GNSS-R receiver 12, which receives direct signals from GNSS satellites and GNSS scattered signals reflected from the sea surface. The direct signals are used for positioning and time synchronization, while the scattered GNSS signals carry information about sea surface characteristics.

[0167] The GNSS-R receiver 12 supports multi-band (Beidou, GPS, Galileo) and multi-polarization (LR, RR, VR, HR) signal reception. The reflected signal contains information such as sea surface roughness and oil film thickness.

[0168] Among them, the drone platform uses a multi-rotor drone with a flight time of ≥60 minutes and a load capacity of ≥2kg.

[0169] The GNSS-R receiver supports BeiDou B1 / B2, GPS L1 / L2, and Galileo E1 / E5 frequency bands, and supports LR, RR, VR, and HR polarization signal reception.

[0170] 2. Signal processing: Preprocessing and time-frequency analysis of the received GNSS scattered signals to extract key parameters.

[0171] Preprocessing includes filtering (noise removal), amplification (signal enhancement), and digitization (conversion to digital signals). Time-frequency analysis involves extracting the power, phase, polarization characteristics, and DDM waveform of the reflected signal.

[0172] The signal processing module implements real-time signal processing based on FPGA or DSP chips. The signal processing module is implemented in C++ or Python and supports multi-band and multi-polarization signal processing.

[0173] 3. Oil spill detection: Based on the DDM waveform and polarization characteristics of different polarization channels, the dielectric constant is obtained to identify the oil spill area.

[0174] Among them, polarization ratio analysis: the reflection intensity of oil film to right circular polarization (RR) signal is usually lower than that of seawater, while left circular polarization (LR) signal is more sensitive to sea surface roughness.

[0175] Dielectric constant: The dielectric constant of the oil film is lower than that of seawater, which causes the amplitude and phase of the GNSS scattered signal to change.

[0176] 4. Data transmission: The processed data is transmitted to the ground station through wireless communication link.

[0177] Communication methods include 4G / 5G, satellite communications, or low-power wide area networks (LPWANs). Real-time transmission ensures the real-time and continuity of monitoring data. Offline storage: When communication conditions are poor, data is temporarily stored locally and transmitted when conditions are restored. The data storage and transmission module utilizes 4G / 5G or satellite communication links to support real-time data transmission.

[0178] 5. Result generation: After receiving the data, the ground station conducts further analysis and visualization to generate an oil spill monitoring report.

[0179] Machine learning models: Utilize algorithms such as support vector machines (SVM) and convolutional neural networks (CNN) to automatically classify and identify oil spill areas. Machine learning models employ TensorFlow or PyTorch frameworks to train oil spill feature classification models.

[0180] Data analysis: Combine environmental parameters (such as wind speed, wave height, and current direction) to predict the oil spill spread path.

[0181] Visualization: Generate oil spill distribution map, time series map and oil film thickness map. The ground station data processing module 21 provides a visualization interface to support the generation and export of oil spill distribution map.

[0182] Report generation: Output detailed information such as oil spill location, area, thickness, etc. to provide support for emergency decision-making.

[0183] The GNSS-R receiver in the present invention's marine oil spill monitoring system uses reduced polarization to acquire GNSS scattered signals from the ocean surface, effectively addressing the independent inversion and application requirements for typical marine disaster parameter inversion. Furthermore, the GNSS-R receiver supports the reception of multi-band (Beidou, GPS, Galileo, etc.) and multi-polarization (LR, RR, VR, HR) GNSS scattered signals. The combination of multi-band (such as Beidou, GPS, Galileo) and multi-polarization (LR, RR, VR, HR) GNSS-R receivers improves the accuracy and reliability of oil spill detection, enabling high-precision oil spill detection even in complex meteorological conditions. Furthermore, the GNSS-R receiver, when deployed on an unmanned aerial vehicle (UAV), achieves rapid response and wide-area coverage through the UAV platform, offering the advantages of low cost and wide coverage. The UAV platform's flexible deployment capabilities enable rapid response to sudden oil spill incidents, covering areas difficult to reach by traditional satellite and ground platforms. Furthermore, the GNSS-R receiver's high spatial resolution enables precise capture of details of small oil spills, providing unprecedented accuracy for oil spill monitoring.

[0184] Furthermore, by integrating signal processing, oil spill feature extraction, and data transmission modules, the present invention fully automates the entire process from data acquisition to result generation, significantly reducing monitoring costs and time. The present invention's low cost, high efficiency, and all-weather monitoring capabilities offer broad application prospects in areas such as marine environmental monitoring, oil spill emergency response, and pollution assessment, providing strong technical support for marine environmental protection and ecological security.

[0185] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Various modifications are possible. Any simple, equivalent changes and modifications made in accordance with the claims and description of the present invention are within the scope of protection of the patent claims. Anything not fully described in this invention is conventional technology.

Claims

1. A sea surface oil spill monitoring system based on GNSS-R receiver, characterized in that: It includes a UAV platform, a GNSS-R receiver, a signal processing module, an oil spill feature extraction module, and a data storage and transmission module installed on the UAV platform and connected in sequence, and a ground station data processing module; The GNSS-R receiver adopts a reduced polarization mode to receive GNSS scattered signals from the sea surface; The oil spill feature extraction module is configured to determine the dielectric constant values ​​of the sea surface in different regions and the corresponding oil spill areas based on the DDM waveform and polarization characteristics of the actually measured GNSS scattered signal; The dielectric constant values ​​of the sea surface in different areas and the corresponding oil spill areas were obtained by pre-calculating the DDM waveforms and polarization characteristics of the oil-free sea surface and different oil spill sea surfaces under different polarization channels through a GNSS scattered signal simulation module. The pre-calculated results were compared with the DDM waveforms and polarization characteristics of the actual measured GNSS scattered signal. The simulation module of the GNSS scattered signal is configured to perform the following steps: Step S1: establishing an oil spill sea surface dielectric constant module, wherein the oil spill sea surface dielectric constant module is used to determine the dielectric constants of the oil-free sea surface and different oil spill sea surfaces according to the dielectric constants of seawater and oil film as the dielectric constant of the sea surface, so as to obtain the Fresnel reflection coefficient; Step S2: establishing an oil spill sea surface roughness spectrum module, wherein the oil spill sea surface roughness spectrum module is used to obtain a wave spectrum model of the oil spill sea surface based on a traditional wave spectrum model and the impact of the oil spill on the wave spectrum, and further obtain a surface spectral density function and a surface root mean square height; Step S3: establishing a microwave scattering module, wherein the microwave scattering module is used to determine the microwave scattering coefficient of the sea surface under different polarization channels according to the Fresnel reflection coefficient, the surface spectral density function and the surface root mean square height; Step S4: establishing a DDM simulation module, wherein the DDM simulation module is used to simulate and obtain a DDM waveform based on GNSS signal parameters and microwave scattering coefficients under different polarization channels of the sea surface; Step S5: The Fresnel reflection coefficient is obtained using the oil spill sea surface dielectric constant module, and the surface spectral density function and surface root mean square height are obtained using the oil spill sea surface roughness spectrum module. The microwave scattering coefficient of the sea surface under different polarization channels is then determined using the microwave scattering module. Finally, the DDM waveform is simulated using the DDM simulation module, and polarization characteristics are obtained based on the DDM waveforms of different polarization channels. The DDM waveform and polarization characteristics are used as the pre-calculated results.

2. The system according to claim 1, wherein: The microwave scattering module is based on an integral equation model and determines the microwave scattering coefficient of the sea surface under different polarization channels according to the input parameters of the incident angle, frequency, polarization channel, Fresnel reflection coefficient, surface spectral density function and surface root mean square height; When the polarization direction p of the incident signal is different from the polarization direction q of the scattered signal, the integral equation model is used to calculate the full polarization scattering matrix through the polarization-related kernel function, and the backscattering coefficient related to the polarization channel pq is obtained as the microwave scattering coefficient.

3. The system according to claim 1, wherein: The GNSS-R receiver supports receiving GNSS scattered signals in multiple frequency bands.

4. The system according to claim 1, wherein: The signal processing module is configured to preprocess the received GNSS scattered signals and extract key parameters; The preprocessing includes filtering, amplification, and digitization; the key parameters include the power, phase, polarization characteristics, and DDM waveform of the actually measured GNSS scattered signal.

5. The system according to claim 1, wherein: The data storage and transmission module supports both real-time data transmission and offline storage.

6. The system according to claim 1, wherein: The ground station data processing module is configured to receive data transmitted by the data storage and transmission module and perform in-depth analysis and visualization processing.

7. The system according to claim 6, characterized in that In-depth analysis refers to the use of machine learning algorithms to analyze data. Specifically, it includes: based on the polarization characteristics of GNSS scattered signals in different areas and the value of the dielectric constant of the sea surface, using machine learning algorithms to classify and identify oil spill characteristics as analysis results; classification and identification results include whether there is an oil spill, the type of oil spill, the thickness of the oil film, and the distribution density.

8. The system according to claim 6, wherein: The ground station data processing module is connected to the oil spill report generation module, and the oil spill report generation module is configured to obtain the analysis results derived by the ground station data processing module and generate an oil spill report according to the analysis results.

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