Space-ground collaborative GNSS-S sea surface wind wave detection system and on-orbit intelligent real-time inversion method

Abstract

The invention provides a sky-ground collaborative GNSS-S sea surface wind wave detection system, which is composed of a GNSS-S satellite, an offshore buoy, a ground processing station and a global user, and is characterized in that the ground processing station carries out space-time matching on observation data of the GNSS-S satellite in a period of time and wind wave data measured by the offshore buoy; a mapping model of GNSS-S observed quantity characteristics and actually measured wind wave data is trained through deep learning, and then the trained mapping model is uploaded to a GNSS-S satellite, so that the GNSS-S satellite realizes in-orbit intelligent real-time inversion of sea surface wind wave information based on observation data and the mapping model, and an inversion result is broadcasted to global users. Therefore, the sea surface wind wave parameter inversion method has the beneficial effects of high sea surface wind wave parameter inversion precision, high real-time performance, wide coverage range, high reliability, low cost and the like.

Description

Technical Field

[0001] The present invention relates to the field of meteorological monitoring technology, and in particular to a ground-ground coordinated GNSS-S sea surface wind and wave detection system and an on-orbit intelligent real-time inversion method. Background Art

[0002] Sea surface wind field and wave height are two important aspects of marine environmental research. Among them, sea surface wind field is the main driving force for the formation of ocean circulation, and has a direct impact on the interaction between the ocean and the atmosphere; ocean waves have an important impact on human coastal and marine activities, marine environmental changes, and global climate change, and studying their movement laws is of great significance. Traditional means of obtaining sea surface wind and wave information mainly use offshore buoys, ship channel measurements, shore-based stations, etc. However, the above methods have the disadvantages of small observation range and low detection efficiency, and cannot obtain global wave information.

[0003] With the rapid development of ocean remote sensing technology and GNSS technology, satellite-borne GNSS-R technology has emerged. As a new dual-base radar measurement method, satellite-borne GNSS-R technology has the advantages of all-day and all-weather measurement, high observation efficiency, low payload weight, small receiving antenna size, low cost, low power consumption, and rapid realization of network observation. At present, GNSS-R professional satellites such as UK-DMC-1 satellite (launched by the United Kingdom in 2003), TDS-1 satellite (launched by the United Kingdom in 2014), CYGNSS constellation (launched by the United States in 2016) and "Wind Catching No. 1" A / B dual satellites (launched by China in 2019) have accumulated a large amount of GNSS reflection signal data. Since 2022, Fengyun-3e satellite, Jilin-1 wide-band 01B satellite, Jilin-1 wide-band 01C satellite and Nanyue Science Star satellite have also been successfully launched with GNSS-R payloads and entered the predetermined orbit. The above-mentioned GNSS-R satellites or payloads can realize on-orbit GNSS reflection signal processing to obtain delay-Doppler maps. However, due to the complexity of the wind and wave inversion algorithm and the limitation of the on-board computer processing level, it is impossible to realize the real-time or quasi-real-time inversion of wind and wave parameters on-orbit. The delay-Doppler maps are all transmitted to the ground station for solution, which is very unfavorable for real-time early warning of severe marine disasters such as hurricanes and tsunamis.

[0004] Therefore, it is urgent to establish a space-ground coordinated GNSS-S sea surface wind and wave detection system and an on-orbit intelligent real-time inversion method. Summary of the invention

[0005] The purpose of the present invention is to provide a space-ground coordinated GNSS-S sea surface wind and wave detection system and an on-orbit intelligent real-time inversion method to solve the problem that existing GNSS-R satellites or payloads cannot obtain sea surface wind and wave parameters through real-time inversion on orbit, improve the timeliness of wind and wave parameter information acquisition, and enhance the potential for application of GNSS-S technology in the field of global hurricane, tsunami and other large-scale marine disaster warning.

[0006] In order to achieve the above-mentioned object, on the one hand, the present invention provides a space-ground coordinated GNSS-S sea surface wind and wave detection system, characterized in that it includes:

[0007] GNSS-S satellite, used to capture and track the sea surface scattered signal of the navigation satellite in the detection area, and process the signal in real time in combination with the detection system state parameters to obtain a delay Doppler map, and regularly transmit the system state parameters and the first observation data of the delay Doppler map to the ground processing station; and receive the mapping model of the observation data characteristics uploaded by the ground processing station and the actual sea surface wind and wave information, and obtain the global sea surface wind and wave information in real time through the mapping model according to the on-orbit observation data, and broadcast the sea surface wind and wave information to global users through the satellite-to-ground link;

[0008] The marine buoy is used to continuously measure the sea surface element information and its corresponding spatiotemporal information, and upload the second observation data including the sea surface element information and the spatiotemporal information to the ground processing station through the ground-sea link; wherein the sea surface element information includes sea surface wind speed, wind direction and wave height; the spatiotemporal information includes data collection time and data collection location;

[0009] The ground processing station is used to periodically receive the first observation data transmitted from the GNSS-S satellite and the second observation data uploaded from the offshore buoy; perform spatiotemporal matching on the first observation data and the second observation data to form a training set, and continuously train the mapping relationship between the GNSS-S observation data feature values ​​and the measured sea surface wind and wave values ​​through a deep learning network based on the training set, and periodically upload the trained mapping model to the GNSS-S satellite through a satellite-to-ground link;

[0010] The global users are used to receive the sea surface wind and wave information inverted in real time by the GNSS-S satellite in orbit.

[0011] Optionally, the GNSS-S satellites include:

[0012] The onboard GNSS-S software receiver is used to capture, track and resolve the GNSS direct signal and the corresponding scattering information, and obtain the GNSS satellite status information, scattering point location information and scattering signal delay Doppler map;

[0013] An onboard computer is used to extract GNSS-S observation data feature values ​​in real time on orbit, and to complete sea surface wind and wave information inversion in real time on orbit based on a mapping model between the observation data features regularly uploaded by the ground processing station and actual sea surface wind and wave information;

[0014] An onboard power supply module, connected to the onboard GNSS-S software receiver and the onboard computer, and used to supply power to the onboard GNSS-S software receiver and the onboard computer;

[0015] The satellite-to-ground link is used to transmit the first observation data to the ground processing station and receive the mapping model trained by the deep learning network from the ground processing station.

[0016] Optionally, the onboard GNSS-S software receiver includes:

[0017] A right-hand circularly polarized phased array antenna is used to simultaneously receive GNSS direct signals of three carrier frequencies: L1, L2, and L5; wherein the corresponding L1 carrier frequency is 1575.42 MHz, the L2 carrier frequency is 1227.60 MHz, and the L5 carrier frequency is 1176.45 MHz, and the effective bandwidths of the signals under the carrier frequencies are 20.46 MHz, 20.46 MHz, and 24 MHz, respectively; the scanning beam of the right-hand circularly polarized phased array antenna is dynamically pointed upward toward the GNSS satellite, and the antenna form is microstrip, the polarization form is right-hand circular polarization, and the antenna normal phase gain is greater than 8 dB;

[0018] A left-hand circularly polarized phased array antenna is used to simultaneously receive GNSS scattered signals of three carrier frequencies, L1, L2 and L5; wherein the corresponding L1 carrier frequency is 1575.42MHz, the L2 carrier frequency is 1227.60MHz, the L5 carrier frequency is 1176.45MHz, and the effective bandwidths of the signals under the carrier frequencies are 20.46MHz, 20.46MHz and 24MHz respectively; the scanning beam of the left-hand circularly polarized phased array antenna is dynamically pointed downward to the GNSS signal sea surface scattering point, and the antenna form is array type, the polarization form is left-hand circular polarization, and the antenna normal gain is greater than 20dB;

[0019] A radio frequency front end, used for filtering and amplifying the received GNSS direct signal and the GNSS scattered signal;

[0020] A multi-channel high-speed A / D converter, used for digitizing the GNSS direct signal and the GNSS scattered signal captured and received by the RF front end;

[0021] An FPGA multi-channel signal processor is used to process the digitized GNSS direct and scattered digital intermediate frequency signals to achieve the GNSS-S satellite positioning and satellite status solution; and configure the GNSS scattered signal channel based on the processing result of the GNSS direct signal, control the delay and obtain the relevant amplitude and relevant power value of the GNSS scattered signal under different delays;

[0022] A data storage unit is used to store the sampled GNSS direct signal, the GNSS scattered signal, the processing result and the detection system status information.

[0023] Optionally, the scanning beam pointing direction of the right-hand circularly polarized phased array antenna is obtained through real-time ephemeris of the GNSS satellite; the scanning beam pointing direction of the left-hand circularly polarized phased array antenna is determined by the position of the sea surface scattering point calculated by an onboard computer.

[0024] Optionally, the offshore buoy includes:

[0025] Wind sensor, used to collect actual wind speed and direction on the sea surface;

[0026] A six-axis sensor, which includes three-axis acceleration and three-axis angular acceleration, is used to collect the actual wave height of the sea surface;

[0027] Satellite positioning and timing module, used to determine the actual position of the offshore buoy and the time of sea surface data collection;

[0028] A ground-sea link, used to upload the collected sea surface element information and the corresponding spatiotemporal information to the ground processing station;

[0029] A data storage device, used to store data information collected by the wind sensor, the six-axis sensor, and the satellite positioning and timing module;

[0030] Solar power modules to continuously power the payload on the buoy;

[0031] a buoy body for providing a mounting platform for a payload on the buoy;

[0032] The anchor chain is used to limit the buoy body to move within a specified range of the installation point.

[0033] Furthermore, the plurality of offshore buoys are evenly distributed in the oceans around the world.

[0034] Optionally, the ground processing station includes:

[0035] A high-performance server, configured to perform spatiotemporal matching on the first observation data and the second observation data to form a training set, and continuously train a mapping relationship between a GNSS-S observation data feature value and a measured sea surface wind and wave value through a deep learning network based on the training set;

[0036] A satellite-to-ground link, configured to receive the first observation data and the system status parameters thereof transmitted by the GNSS-S satellite, and to upload the mapping model trained by the deep learning network to the GNSS-S satellite;

[0037] The ground-sea link is used to receive second observation data measured by the offshore buoy.

[0038] Optionally, the ground processing station is specifically used to construct a training set based on the first observation data and the second observation data accumulated over a preset period of time, and continuously train the mapping relationship between the characteristic values ​​of the GNSS-S observation data and the measured sea surface wind and wave values ​​through a deep learning network based on the training set to obtain the mapping model and upload it to the GNSS-S satellite; and continuously receive the first observation data of the GNSS-S satellite and the second observation data of the offshore buoy, and regularly update the mapping model and upload it to the GNSS-S satellite.

[0039] On the other hand, the present invention also provides an on-orbit intelligent real-time inversion method implemented by the space-ground collaborative GNSS-S sea surface wind and wave detection system described in any one of the above items, comprising the steps of:

[0040] Obtaining a delay Doppler map after on-orbit processing of the GNSS-S signal, and preprocessing the delay Doppler map based on a signal-to-noise ratio;

[0041] Normalizing the delay-Doppler image and extracting typical features to use them as a model input main vector;

[0042] Obtain the longitude and latitude, scattering angle, and scanning beam gain information of the downward-looking phased array antenna of the sea surface scattering point as the model input slave vector;

[0043] The input main vector and the input slave vector are input into a pre-trained mapping model to perform wind speed, wind direction and significant wave height inversion to obtain corresponding inversion results.

[0044] Optionally, the on-orbit processing of the GNSS-S signal and the pre-processing of the delay-Doppler map are realized by a multi-channel processor implemented based on FPGA;

[0045] The delay-Doppler graph is normalized and typical features are extracted from the leading edge slope of the delay waveform, the trailing edge slope of the delay waveform, the average value of the delay and Doppler power, the signal-to-noise ratio, and the effective scattering cross-sectional area.

[0046] The space-ground collaborative GNSS-S sea surface wind and wave detection system described in the present invention has added marine buoys compared to the traditional GNSS-S system, and realizes space-ground collaborative inversion of sea surface wind speed, wind direction and effective wave height through satellite-to-ground links, which greatly improves the reliability of GNSS-S sea surface wind and wave parameter inversion; the present invention also provides an on-orbit intelligent real-time inversion method based on the space-ground collaborative GNSS-S sea surface wind and wave detection system, which can continuously train the scattered signal data transmitted by GNSS-S and the measured wind and wave data transmitted by marine buoys through a deep learning network at a ground processing station, and regularly upload the trained and updated mapping model to the onboard computer, which has higher inversion accuracy and timeliness than the traditional GNSS-R wind and wave detection system and inversion method. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A system composition diagram of the space-ground collaborative GNSS-S sea surface wind and wave detection system provided by an embodiment of the present invention is shown;

[0048] Figure 2 A diagram showing an application scenario of the space-ground collaborative GNSS-S sea surface wind and wave detection system provided by an embodiment of the present invention is shown;

[0049] Figure 3 A system composition diagram of the GNSS-S satellite of the space-ground coordinated GNSS-S sea surface wind and wave detection system provided by an embodiment of the present invention is shown;

[0050] Figure 4 A system composition diagram of the marine buoy of the space-ground collaborative GNSS-S sea surface wind and wave detection system provided by an embodiment of the present invention is shown;

[0051] Figure 5 A system composition diagram of the ground processing station of the space-ground collaborative GNSS-S sea surface wind and wave detection system provided by an embodiment of the present invention is shown;

[0052] Figure 6 A flowchart showing the steps of the on-orbit intelligent real-time inversion method provided by one embodiment of the present invention is shown;

[0053] Figure 7 A schematic diagram showing the system flow of the on-orbit intelligent real-time inversion method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] It should be noted that references to "one embodiment", "embodiment", "example embodiment", etc. in this specification refer to the embodiment described, which may include specific features, structures or characteristics, but not every embodiment must include these specific features, structures or characteristics. In addition, such expressions do not refer to the same embodiment. Furthermore, when describing specific features, structures or characteristics in conjunction with an embodiment, whether or not there is an explicit description, it has been shown that it is within the knowledge of those skilled in the art to combine such features, structures or characteristics into other embodiments.

[0056] In addition, certain words are used in the specification and subsequent claims to refer to specific components or parts. Those with ordinary knowledge in the relevant field should understand that manufacturers can use different nouns or terms to refer to the same component or part. This specification and subsequent claims do not use differences in names as a way to distinguish components or parts, but use differences in the functions of components or parts as the criteria for distinction. "Including" and "including" mentioned throughout the specification and subsequent claims are open-ended terms and should be interpreted as "including but not limited to". In addition, the word "connected" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connection through other devices.

[0057] Embodiment 1

[0058] Figure 1 The figure shows a space-ground collaborative GNSS-S sea surface wind and wave detection system provided by the first embodiment of the present invention, which can be applied to the research on high-precision space-based GNSS-R sea surface wind and wave parameter detection on orbit, and specifically includes a GNSS-S satellite 10, an offshore buoy 20, a ground processing station 30 and a global user 40; wherein:

[0059] The GNSS-S satellite 10 is used to capture and track the sea surface scattering signal of the navigation satellite in the detection area, and to process the signal in real time in combination with the detection system state parameters to obtain a delay Doppler map, and to regularly transmit the system state parameters and the first observation data of the delay Doppler map to the ground processing station 30; and to receive a mapping model of the observation data characteristics uploaded from the ground processing station 30 and the actual sea surface wind and wave information, and to obtain the global sea surface wind and wave information in real time through the mapping model according to the on-orbit observation data, and to broadcast the sea surface wind and wave information to global users through the satellite-to-ground link. The sea buoy 20 is used to continuously measure the sea surface element information and its corresponding space-time information, and to transmit the sea surface element information and space-time information to global users 40 through the satellite-to-ground link. The second observation data is uploaded to the ground processing station 30 through the ground-sea link; wherein the sea surface element information includes sea surface wind speed, wind direction and wave height; the spatiotemporal information includes data collection time and data collection location; the ground processing station 30 is used to regularly receive the first observation data transmitted down from the GNSS-S satellite 10, and receive the second observation data uploaded from the offshore buoy 20; the first observation data and the second observation data are spatiotemporally matched to form a training set, and based on the training set, the mapping relationship between the GNSS-S observation data feature values ​​and the measured sea surface wind and wave values ​​is continuously trained through a deep learning network, and the trained mapping model is regularly uploaded to the GNSS-S satellite 10 through the satellite-ground link; the global user 40 is used to receive the sea surface wind and wave information inverted in real time by the GNSS-S satellite 10 on orbit.

[0060] That is, in this embodiment, the ground processing station 30 is used to respectively receive the first observation data from the GNSS-S satellite and the second observation data uploaded by the offshore buoy, and the delay Doppler map and other information in the first observation data and the actually measured sea surface element information in the second observation data are used to construct a training set through time-space matching; each pair of training samples in the training set is the characteristic value of the GNSS-S observation data and the actual sea surface wind and wave value with the same time-space matching; the corresponding mapping relationship is trained by deep learning technology with this training set to obtain a mapping model that can be used to predict sea wind and wave information; that is, the mapping model after training can use the on-orbit measurement data of the GNSS-S satellite as the model input, and output the corresponding inversion result through matching of the mapping model, that is, the corresponding sea surface wind and wave information is inverted by the model according to the on-orbit measurement data of the satellite.

[0061] The ground processing station 30 of this embodiment is specifically used to build a training set based on the first observation data and the second observation data collected over a preset time, and continuously train the mapping relationship between the GNSS-S observation data feature value and the measured sea surface wind and wave value through a deep learning network based on the training set to obtain a mapping model and upload it to the GNSS-S satellite 10; and continuously receive the first observation data of the GNSS-S satellite 10 and the second observation data of the offshore buoy 20 and regularly update the mapping model before uploading it to the GNSS-S satellite 10. Figure 2 In specific implementation, after the GNSS-S satellite 10 is launched into orbit, the GNSS sea surface scattered signal detection is carried out in the early stage and the data is regularly transmitted to the ground processing station 30. At the same time, several sea buoys 20 evenly distributed in the global ocean regularly upload the measured time, position, sea surface wind speed, wind direction and effective wave height and other information to the ground processing station 30; the ground processing station 30 pre-processes the received GNSS-S data and the sea buoy data and performs time-space matching. After accumulating for a certain period of time (such as half a year), it can be trained through a deep learning network to obtain GNSS-S A mapping model is constructed between the typical characteristics of the observed data and the measured wind and wave data, and the trained mapping model is uploaded to the GNSS-S satellite. In this way, the GNSS-S satellite can have the function of on-orbit intelligent real-time inversion, so that the wind and wave results can be inverted in real time based on the model and on-orbit measurement data and broadcast to global users 40. The ground processing station 30 can also continuously train the mapping model based on the continuously received GNSS-S data and offshore buoy data, and regularly upload the trained and updated mapping model to the GNSS-S satellite, so as to continuously improve the on-orbit inversion accuracy of sea surface wind and wave information.

[0062] See also Figure 3 In an optional implementation, the GNSS-S satellite 10 includes an onboard GNSS-S software receiver, an onboard computer 102, an onboard power module 103, and a satellite-to-ground link 104, wherein:

[0063] The onboard GNSS-S software receiver 101 is used to capture, track and solve the GNSS direct signal and the corresponding scattering information, and obtain the GNSS satellite status information, the scattering point location information and the scattering signal delay Doppler diagram; the onboard computer 102 is used to extract the GNSS-S observation data characteristic values ​​in real time on orbit, and complete the sea surface wind and wave information inversion in real time on orbit based on the mapping model of the observation data characteristics and the actual sea surface wind and wave information regularly uploaded by the ground processing station 30; the onboard power supply module 103 is connected to the onboard GNSS-S software receiver 101 and the onboard computer 102, and is used to power the onboard GNSS-S software receiver 101 and the onboard computer 102; the satellite-to-ground link 104 is used to transmit the first observation data and system status parameters to the ground processing station 30, and receive the mapping model trained by the deep learning network from the ground processing station 30.

[0064] Preferably, the onboard GNSS-S software receiver 101 includes a right-hand circularly polarized phased array antenna, a left-hand circularly polarized phased array antenna, a radio frequency front end, a multi-channel high-speed A / D converter, an FPGA multi-channel signal processor, and a data storage unit, wherein:

[0065] The right-hand circularly polarized phased array antenna is used to simultaneously receive GNSS direct signals of three carrier frequencies: L1, L2, and L5. The corresponding L1 carrier frequency is f L1 is 1575.42MHz, L2 carrier frequency f L2 is 1227.60MHz, L5 carrier frequency f L5 is 1176.45MHz, and the effective bandwidth of the signal under the carrier frequency is B L1 :20.46MHz、B L2 :20.46MHz and B L5 :24MHz; the scanning beam is dynamically pointed upward to the GNSS satellite, and the antenna form is microstrip, the polarization form is right-hand circular polarization, and the antenna normal gain G R Greater than 8dB; the scanning beam pointing of the right-hand circularly polarized phased array antenna is obtained through the real-time ephemeris of the GNSS satellite, and 8 to 16 beams can be generated simultaneously according to the number of visible satellites.

[0066] The left-hand circularly polarized phased array antenna is used to simultaneously receive GNSS scattered signals of three carrier frequencies: L1, L2, and L5; the corresponding L1 carrier frequency f′ L1 is 1575.42MHz, L2 carrier frequency f′ L2 is 1227.60MHz, L5 carrier frequency f′ L5 is 1176.45MHz, and the effective bandwidth of the signal under the carrier frequency is B′ L1 :20.46MHz、B′ L2:20.46MHz and B′ L5 :24MHz; the scanning beam is dynamically pointed downward to the GNSS signal sea surface scattering point, and the antenna form is array type, the polarization form is left-hand circular polarization, and the antenna normal gain G L Greater than 20dB; the scanning beam direction of the left-hand circularly polarized phased array antenna is determined by the position of the sea surface scattering point calculated by the on-board computer 102, and 8 to 16 beams can be generated simultaneously according to the number of visible satellites.

[0067] The RF front end is used to filter and amplify the received GNSS direct signal and GNSS scattered signal; the RF front end specifically includes modules such as low noise amplifier, mixer, phase-locked loop and intermediate frequency filter. The input frequencies of the three carriers L1, L2 and L5 are 1575.42±10.23MHz, 1227.60±10.23MHz and 1176.45±12MHz respectively.

[0068] The multi-channel high-speed A / D converter is used to digitize the GNSS direct signal and GNSS scattered signal captured and received by the RF front end; according to the observation needs and the number of visible GNSS satellites, the number of channels of the multi-channel high-speed A / D converter can be set to 24 to 48.

[0069] The FPGA multi-channel signal processor is used to process the digitized GNSS direct and scattered digital intermediate frequency signals to achieve GNSS-S satellite positioning and satellite status solution; and based on the processing results of the GNSS direct signal, the GNSS scattered signal channel is configured to control the delay and obtain the relevant amplitude and power value of the GNSS scattered signal under different delays.

[0070] The data storage unit is used to store sampled GNSS direct signals, GNSS scattered signals, processing results and detection system status information.

[0071] See also Figure 4 In an optional embodiment, the marine buoy 20 includes a wind sensor 201, a six-axis sensor 202, a satellite positioning and timing module 203, a land-sea link 204, a data storage device 205, a solar power module 206, a buoy body 207 and an anchor chain 208, wherein:

[0072] The wind sensor 201 is used to collect the actual wind speed and direction of the sea surface; the six-axis sensor 202 includes a three-axis acceleration and a three-axis angular acceleration to collect the actual wave height of the sea surface; the satellite positioning and timing module 203 is used to determine the actual position of the offshore buoy and the time of collecting sea surface data; the satellite positioning and timing module 203 of this embodiment can simultaneously receive signals from GPS, BDS-3 (Beidou-3), GALILEO (Galileo Satellite Navigation System) and GLONASS (Russian Global Satellite Navigation System) satellites to determine the actual position of the offshore buoy and the time of collecting sea surface data; the ground-sea link 204 is used to upload the collected The acquired sea surface element information and its corresponding space-time information are transmitted to the ground processing station 30; the data storage device 205 is used to store the data information collected by the wind sensor 201, the six-axis sensor 202 and the satellite positioning and timing module 203, specifically storing the collected time, position, sea surface wind speed, wind direction and wave height and other information; the solar power supply module 206 is used to continuously supply power to the payload on the buoy; the buoy body 207 is used to provide an installation platform for the payload on the buoy; the anchor chain 208 is used to limit the buoy body to move within the specified range of the installation point, specifically through the anchor chain 208 to ensure that the offshore buoy 20 moves within 1km of the installation point.

[0073] Preferably, a plurality of offshore buoys 20 are evenly distributed in the oceans around the world; according to demand, an offshore buoy 20 is set every 5° to 15° of longitude and latitude, and the total number of offshore buoys to be installed worldwide is approximately 50 to 358.

[0074] See also Figure 5 In an optional embodiment, the ground processing station 30 includes a high-performance server 301, a satellite-to-ground link 302, and a ground-to-sea link 303, wherein:

[0075] The high-performance server 301 is used to match the first observation data and the second observation data in time and space to form a training set, and continuously train the mapping relationship between the characteristic values ​​of the GNSS-S observation data and the measured sea surface wind and wave values ​​through a deep learning network based on the training set; the satellite-to-ground link 302 is used to receive the first observation data and the system status parameters thereof transmitted down by the GNSS-S satellite 10, and upload the mapping model trained by the deep learning network to the GNSS-S satellite 10; the ground-sea link 303 is used to receive the second observation data measured by the offshore buoy 20, that is, to receive the time, position, sea surface wind speed, wind direction and wave height and other information measured by the offshore buoy 20.

[0076] The ground-to-space collaborative GNSS-S sea surface wind and wave detection system provided in this embodiment has added marine buoys compared to the traditional GNSS-S system, and realizes ground-to-space collaborative inversion of sea surface wind speed, wind direction and effective wave height through satellite-to-ground links, greatly improving the reliability of GNSS-S sea surface wind and wave parameter inversion.

[0077] Embodiment 2

[0078] Figure 6 The on-orbit intelligent real-time inversion method provided by the second embodiment of the present invention is shown. The method is implemented based on the ground-ground collaborative GNSS-S sea surface wind and wave detection system of the first embodiment, and specifically includes the following steps:

[0079] S101: Obtain a delay-Doppler map after on-orbit processing of the GNSS-S signal, and pre-process the delay-Doppler map based on a signal-to-noise ratio.

[0080] S102: normalizing the delay-Doppler image and extracting typical features to serve as the model input main vector; that is, the typical features extracted from the delay-Doppler image are used as the input main vector of the wind speed, wind direction and significant wave height inversion model.

[0081] S103: Acquire the longitude and latitude, scattering angle, and scanning beam gain information of the downward-looking phased array antenna of the sea surface scattering point as a model input slave vector;

[0082] S104: Inputting the input main vector and the input slave vector into a pre-trained mapping model to perform wind speed, wind direction and significant wave height inversion to obtain corresponding inversion results.

[0083] Furthermore, in step S101, a multi-channel processor based on FPGA is used to realize on-orbit processing of GNSS-S signals and preprocessing of the delay-Doppler map. Specifically, a multi-channel dedicated correlator is implemented based on FPGA to complete the correlation operation of the received GNSS scattered signal with the local copy to obtain the delay-Doppler map and the original observation quantity of the direct signal to realize orbit determination.

[0084] In this embodiment, the delay-Doppler graph is normalized and typical features are extracted from parameters such as the leading edge slope of the delay waveform, the trailing edge slope of the delay waveform, the average value of the delay and Doppler power, the signal-to-noise ratio, and the effective scattering cross-sectional area.

[0085] In step S103 , the longitude and latitude of the sea surface scattering point and the scattering angle are determined according to the positions of the GNSS-S satellite and the GNSS satellite, and the scanning beam gain of the downward-looking phased array antenna is obtained according to the phased array antenna radiation pattern and the scanning angle.

[0086] Furthermore, step S104 may be performed by an onboard computer on the main satellite of the GNSS-S satellite, and the wind speed, wind direction, significant wave height results and corresponding inversion performance may be broadcast to global users via a satellite-to-ground broadcast link.

[0087] See also Figure 7 This embodiment provides an on-orbit intelligent real-time inversion method, which can continuously train the scattered signal data transmitted by GNSS-S and the measured wind and wave data transmitted by offshore buoys through a deep learning network at the ground processing station, and regularly upload the trained and updated mapping model to the on-board computer. Compared with the traditional GNSS-R wind and wave detection system and inversion method, it has higher inversion accuracy and timeliness.

[0088] In summary, the space-ground collaborative GNSS-S sea surface wind and wave detection system provided by the present invention performs spatiotemporal matching of the observation data of the GNSS-S satellite within a period of time with the wind and wave data measured by the offshore buoy through the ground processing station, and then trains the mapping model of the GNSS-S observation features and the measured wind and wave data through deep learning, and uploads the trained mapping model to the on-board computer carried by the GNSS-S satellite, so that the on-orbit intelligent real-time inversion of the sea surface wind and wave information can be realized based on the mapping model and real-time observation data, and the inversion information can be broadcast to global users. In this way, the present invention has the advantages of high sea surface wind and wave parameter inversion accuracy, strong real-time performance, wide coverage, high reliability and low system cost.

[0089] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A space-ground coordinated GNSS-S sea surface wind and wave detection system, characterized in that: Included are: GNSS-S satellite, used to capture and track the sea surface scattered signal of the navigation satellite in the detection area, and process the signal in real time in combination with the detection system state parameters to obtain a delay Doppler map, and regularly transmit the system state parameters and the first observation data of the delay Doppler map to the ground processing station; and receive the mapping model of the observation data characteristics uploaded by the ground processing station and the actual sea surface wind and wave information, and obtain the global sea surface wind and wave information in real time through the mapping model according to the on-orbit observation data, and broadcast the sea surface wind and wave information to global users through the satellite-to-ground link; The marine buoy is used to continuously measure the sea surface element information and its corresponding spatiotemporal information, and upload the second observation data including the sea surface element information and the spatiotemporal information to the ground processing station through the ground-sea link; wherein the sea surface element information includes sea surface wind speed, wind direction and wave height; the spatiotemporal information includes data collection time and data collection location; The ground processing station is used to periodically receive the first observation data transmitted from the GNSS-S satellite and the second observation data uploaded from the offshore buoy; perform spatiotemporal matching on the first observation data and the second observation data to form a training set, and continuously train the mapping relationship between the GNSS-S observation data feature values ​​and the measured sea surface wind and wave values ​​through a deep learning network based on the training set, and periodically upload the trained mapping model to the GNSS-S satellite through a satellite-to-ground link; The global users are used to receive the sea surface wind and wave information inverted in real time by the GNSS-S satellite in orbit.

2. The space-ground coordinated GNSS-S sea surface wind and wave detection system according to claim 1 is characterized in that: The GNSS-S satellites include: The onboard GNSS-S software receiver is used to capture, track and resolve the GNSS direct signal and the corresponding scattering information, and obtain the GNSS satellite status information, scattering point location information and scattering signal delay Doppler map; An onboard computer is used to extract GNSS-S observation data feature values ​​in real time on orbit, and to complete sea surface wind and wave information inversion in real time on orbit based on a mapping model between the observation data features regularly uploaded by the ground processing station and actual sea surface wind and wave information; An onboard power supply module, connected to the onboard GNSS-S software receiver and the onboard computer, and used to supply power to the onboard GNSS-S software receiver and the onboard computer; The satellite-to-ground link is used to transmit the first observation data to the ground processing station and receive the mapping model trained by the deep learning network from the ground processing station.

3. The space-ground coordinated GNSS-S sea surface wind and wave detection system according to claim 2 is characterized in that: The onboard GNSS-S software receiver includes: A right-hand circularly polarized phased array antenna is used to simultaneously receive GNSS direct signals of three carrier frequencies: L1, L2, and L5; wherein the corresponding L1 carrier frequency is 1575.42 MHz, the L2 carrier frequency is 1227.60 MHz, and the L5 carrier frequency is 1176.45 MHz, and the effective bandwidths of the signals under the carrier frequencies are 20.46 MHz, 20.46 MHz, and 24 MHz, respectively; the scanning beam of the right-hand circularly polarized phased array antenna is dynamically pointed upward toward the GNSS satellite, and the antenna form is microstrip, the polarization form is right-hand circular polarization, and the antenna normal phase gain is greater than 8 dB; A left-hand circularly polarized phased array antenna is used to simultaneously receive GNSS scattered signals of three carrier frequencies, L1, L2 and L5; wherein the corresponding L1 carrier frequency is 1575.42MHz, the L2 carrier frequency is 1227.60MHz, the L5 carrier frequency is 1176.45MHz, and the effective bandwidths of the signals under the carrier frequencies are 20.46MHz, 20.46MHz and 24MHz respectively; the scanning beam of the left-hand circularly polarized phased array antenna is dynamically pointed downward to the GNSS signal sea surface scattering point, and the antenna form is array type, the polarization form is left-hand circular polarization, and the antenna normal gain is greater than 20dB; A radio frequency front end, used for filtering and amplifying the received GNSS direct signal and the GNSS scattered signal; A multi-channel high-speed A / D converter, used for digitizing the GNSS direct signal and the GNSS scattered signal captured and received by the RF front end; An FPGA multi-channel signal processor is used to process the digitized GNSS direct and scattered digital intermediate frequency signals to achieve the GNSS-S satellite positioning and satellite status solution; and configure the GNSS scattered signal channel based on the processing result of the GNSS direct signal, control the delay and obtain the relevant amplitude and relevant power value of the GNSS scattered signal under different delays; A data storage unit is used to store the sampled GNSS direct signal, the GNSS scattered signal, the processing result and the detection system status information.

4. The space-ground coordinated GNSS-S sea surface wind and wave detection system according to claim 3 is characterized in that: The scanning beam pointing direction of the right-hand circularly polarized phased array antenna is obtained through the real-time ephemeris of the GNSS satellite; the scanning beam pointing direction of the left-hand circularly polarized phased array antenna is determined by the position of the sea surface scattering point calculated by the on-board computer.

5. The space-ground coordinated GNSS-S sea surface wind and wave detection system according to claim 1 is characterized in that: The offshore buoy includes: Wind sensor, used to collect actual wind speed and direction on the sea surface; A six-axis sensor, which includes three-axis acceleration and three-axis angular acceleration, is used to collect the actual wave height of the sea surface; Satellite positioning and timing module, used to determine the actual position of the offshore buoy and the time of sea surface data collection; A ground-sea link, used to upload the collected sea surface element information and the corresponding spatiotemporal information to the ground processing station; A data storage device, used to store data information collected by the wind sensor, the six-axis sensor, and the satellite positioning and timing module; Solar power modules to continuously power the payload on the buoy; a buoy body for providing a mounting platform for a payload on the buoy; The anchor chain is used to limit the buoy body to move within a specified range of the installation point.

6. The space-ground coordinated GNSS-S sea surface wind and wave detection system according to claim 5 is characterized in that: A plurality of said marine buoys are evenly distributed in the oceans around the globe.

7. The space-ground coordinated GNSS-S sea surface wind and wave detection system according to claim 1 is characterized in that: The ground processing station includes: A high-performance server, configured to perform spatiotemporal matching on the first observation data and the second observation data to form a training set, and continuously train a mapping relationship between a GNSS-S observation data feature value and a measured sea surface wind and wave value through a deep learning network based on the training set; A satellite-to-ground link, configured to receive the first observation data and the system status parameters thereof transmitted by the GNSS-S satellite, and to upload the mapping model trained by the deep learning network to the GNSS-S satellite; The ground-sea link is used to receive second observation data measured by the offshore buoy.

8. The space-ground coordinated GNSS-S sea surface wind and wave detection system according to claim 1, characterized in that: The ground processing station is specifically used to construct a training set based on the first observation data and the second observation data accumulated over a preset period of time, and continuously train the mapping relationship between the characteristic values ​​of the GNSS-S observation data and the measured sea surface wind and wave values ​​through a deep learning network based on the training set to obtain the mapping model and upload it to the GNSS-S satellite; and continuously receive the first observation data of the GNSS-S satellite and the second observation data of the offshore buoy, and regularly update the mapping model and upload it to the GNSS-S satellite.

9. An on-orbit intelligent real-time inversion method based on the space-ground collaborative GNSS-S sea surface wind and wave detection system according to any one of claims 1 to 8, characterized in that: Includes steps: Obtaining a delay Doppler map after on-orbit processing of the GNSS-S signal, and preprocessing the delay Doppler map based on a signal-to-noise ratio; Normalizing the delay-Doppler image and extracting typical features to use them as a model input main vector; Obtain the longitude and latitude, scattering angle, and scanning beam gain information of the downward-looking phased array antenna of the sea surface scattering point as the model input slave vector; The input main vector and the input slave vector are input into a pre-trained mapping model to perform wind speed, wind direction and significant wave height inversion to obtain corresponding inversion results.

10. The on-orbit intelligent real-time inversion method according to claim 9, characterized in that: Implementing on-orbit processing of the GNSS-S signal and preprocessing of the delay-Doppler map through a multi-channel processor implemented based on FPGA; The delay-Doppler graph is normalized and typical features are extracted from the leading edge slope of the delay waveform, the trailing edge slope of the delay waveform, the average value of the delay and Doppler power, the signal-to-noise ratio, and the effective scattering cross-sectional area.

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