SoOP-R water body detection method and system using Internet satellite opportunity signals

Abstract

The invention provides a SoOP-R water body detection method and system using Internet satellite opportunity signals, and the method comprises the steps: employing an airborne SoOP-R water body detection system constructed by the invention, employing two real-time spectrum analyzers to receive direct opportunity signals of Internet satellites and earth surface reflection signals of the Internet satellites respectively, capturing and calculating the average signal-to-noise ratio of beacon signals, and carrying out the calculation of the average signal-to-noise ratio of the beacon signals; and performing normalization processing on the average signal-to-noise ratio of the beacon signal of the reflection signal by using the direct signal, and finally determining whether the surface area corresponding to the reflection point of the reflection signal mirror is a water body or not through threshold judgment. According to the method, the advantages of high coverage rate, high time resolution and the like of Internet satellites are utilized, and the defects of low coverage rate and low time resolution caused by insufficient GNSS satellites in a traditional GNSS-R water body detection method are overcome. The method can be applied to the fields of water resource management, flood disaster early warning and the like, and the potential of the Internet satellite opportunity signal SoOP-R remote sensing method in the field of earth surface parameter inversion is shown.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and particularly to a SoOP-R water body detection method and system using Internet satellite opportunity signals. Background Art

[0002] Detecting water bodies existing on the earth's surface is an important task in the fields of environmental protection, water resource management, disaster warning, etc. Accurate water body detection not only helps to evaluate the distribution and changes of water resources, but also provides key data support for tasks such as flood warning and drought monitoring.

[0003] Currently, the main methods for water body detection are as follows, and they have various advantages and disadvantages. Traditional water body detection methods mainly rely on ground station observations, but they have certain limitations in terms of coverage and real-time performance; while remote sensing technology is an important means for large-scale water body detection. Mainstream remote sensing water body detection methods include optical remote sensing water body detection, synthetic aperture radar (SAR) remote sensing water body detection, and global navigation satellite system reflectometry (GNSS-R) remote sensing water body detection, etc. Some scholars use optical remote sensing images for water body detection. This method analyzes the spectral reflection characteristics of water bodies and land in different bands, segments the optical remote sensing images, selects water body object samples and trains the SVM support vector machine, and can effectively identify water bodies (Patent CN201310359358.9). However, optical remote sensing is affected by weather conditions such as clouds and fog and the limitation of night observations, and cannot achieve all-weather water body monitoring.

[0004] Some scholars use SAR remote sensing data to detect water bodies such as floods. Through the preprocessing, registration and temporal image analysis of SAR remote sensing images, large-scale flood water body extraction based on classification trees can be achieved (Patent CN117274809A). However, the revisit period of the SAR remote sensing satellite constellation is generally long, and its observation range is limited by the number of SAR satellites, making it difficult to achieve real-time monitoring on a global scale. Currently, some scholars have carried out research on using GNSS-R remote sensing data for water body detection (Wang Juntao. Research on Spaceborne GNSS-R Water Body Detection Method Based on CYGNSS Data [D]. Anhui: Hefei University of Technology, 2021). This method can effectively invert water bodies such as floods by analyzing the surface reflectivity values calculated from GNSS-R data and combining the established grid. In addition to maintaining the advantages of penetration characteristics and initiative, GNSS-R remote sensing also has the advantages of high coverage rate, low cost and high time resolution. However, due to the limited number of navigation satellites, its spatial resolution and coverage range have certain limitations.

[0005] With the rapid development of Internet satellite constellations (such as Starlink, OneWeb, etc.), the number of Internet satellites has far exceeded that of traditional navigation satellites, and their signal bandwidth is significantly higher than GNSS signals. This provides a new opportunity signal for GNSS-R remote sensing technology, namely the communication satellite opportunity reflectometry (SoOP-R). Similar to GNSS-R, SoOP-R can obtain information about the reflection surface from the amplitude, phase, and spectral characteristics of the reflected signal. SoOP-R remote sensing uses the surface reflection signals of Internet satellites to invert surface parameters. On the basis of retaining the advantages of GNSS-R remote sensing technology, it also has the advantages of higher signal bandwidth, global coverage, and real-time performance, enabling the SoOP-R remote sensing method to carry out high spatio-temporal resolution remote sensing monitoring tasks globally, especially in areas where traditional remote sensing technology is difficult to cover. Summary of the Invention

[0006] To overcome the limitations of the prior art, the present invention proposes a water body detection method using Internet satellite opportunity signals and their reflected signals. This method constructs a SoOP-R water body detection system carried by an unmanned aerial vehicle (UAV). The system uses two real-time spectrum analyzers to simultaneously receive the direct signal of the Internet satellite and its surface reflection signal through the zenith antenna and the nadir antenna facing the ground respectively. The computer connected to the spectrum analyzer finds multiple beacon signals in the direct opportunity signal of the Internet satellite and its surface reflection signal, calculates their average signal-to-noise ratio, normalizes the beacon signal average signal-to-noise ratio of the direct opportunity signal of the Internet satellite to the surface reflection signal, and makes a threshold judgment on the obtained normalized result to determine whether the corresponding surface area is a water body.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A SoOP-R water body detection method using Internet satellite opportunity signals, comprising the following steps:

[0009] Step 1: Based on the flight path of the airborne SoOP-R water body detection system, determine the current position of the UAV airborne receiver, download the TLE ephemeris data of the used Internet satellite and calculate the current position of the Internet satellite, determine whether the Internet satellite is available, and iteratively invert the coordinates of the signal surface specular reflection point of the available Internet satellite;

[0010] Step 2: Collect the data of the direct signal and the reflected signal captured under the current position of the UAV airborne receiver, calculate the signal-to-noise ratio of the direct signal of the Internet satellite and the signal-to-noise ratio of the reflected signal, and draw the time-frequency signal-to-noise ratio waterfall diagram of the direct signal and the reflected signal;

[0011] Step 3: Capture the beacon signals in the time-frequency signal-to-noise ratio waterfall diagrams of the direct signal and the reflected signal of the Internet satellite respectively, and calculate the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of a single Internet satellite;

[0012] Step 4: Based on the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of the Internet satellite, obtain the normalized average signal-to-noise ratio of the reflected beacon signal;

[0013] Step 5: Compare the normalized average signal-to-noise ratio of the reflected beacon signal with a set threshold to determine whether the coordinate of the specular reflection point on the ground of the signal corresponding to the average signal-to-noise ratio of the reflected beacon signal is water;

[0014] Step 6: Traverse all available Internet satellites and determine whether the sub-satellite point area is water.

[0015] Further, the airborne SoOP-R water body detection system in the step 1 includes: an unmanned aerial vehicle, a zenith antenna and a nadir antenna mounted on the unmanned aerial vehicle, wherein the zenith antenna receives the direct signal S of the Internet satellite d , and the nadir antenna receives the ground reflection signal S r . The direct signal and the reflected signal are respectively sent to a direct signal real-time spectrum analyzer and a reflected signal real-time spectrum analyzer through cables to perform data acquisition, and the acquired signals are sent to a computer for spectrum analysis and water body discrimination.

[0016] Further, the step 1 includes:

[0017] Calculate the current coordinates of the Internet satellite according to the TLE ephemeris data of the Internet satellite;

[0018] Obtain the current position of the airborne receiver of the unmanned aerial vehicle through the on-board navigator on the unmanned aerial vehicle;

[0019] Calculate the elevation angle of the Internet satellite relative to the airborne receiver based on the current coordinates of the Internet satellite and the current position of the airborne receiver;

[0020] Determine whether the Internet satellite is available based on the magnitude of the elevation angle and the elevation angle threshold;

[0021] Unify the calculated current coordinates of the Internet satellite and the current position of the receiver to the same coordinate system, and use the principle that the reflection angle and the incident angle are equal to iteratively invert the coordinates of the specular reflection point on the ground of the signal.

[0022] Further, the step 2 includes:

[0023] The direct signal real-time spectrum analyzer and the reflected signal real-time spectrum analyzer respectively process the direct opportunity signal of the Internet satellite received by the zenith antenna and the reflected opportunity signal received by the nadir antenna;

[0024] The computer calculates the beacon signal power of the direct opportunity signal of the Internet satellite, the noise power of the direct opportunity signal, the beacon signal power of the reflected opportunity signal, and the noise power of the reflected opportunity signal, and calculates the signal-to-noise ratio of the direct signal of the Internet satellite based on the beacon signal power and the noise power of the direct opportunity signal; calculates the signal-to-noise ratio of the reflected signal of the Internet satellite based on the beacon signal power and the noise power of the reflected opportunity signal;

[0025] Taking the frequency where the beacon signal is located as the abscissa and time as the ordinate, the time-frequency signal-to-noise ratio waterfall diagrams of the direct signal and the reflected signal are respectively plotted.

[0026] Furthermore, the beacon signals in the time-frequency signal-to-noise ratio waterfall diagrams of the direct signal and the reflected signal of the Internet satellite captured in step 3 include:

[0027] In the time-frequency signal-to-noise ratio waterfall diagrams of the direct signal and the reflected signal, continuously stable narrowband signals with the signal-to-noise ratio of the direct signal and the reflected signal significantly higher than the background and having a certain Doppler effect are respectively found, and the signal-to-noise ratio of each beacon signal and the total number of beacon signals that can be received are respectively recorded, so as to calculate the average signal-to-noise ratio of the direct beacon signal in the direct opportunity signal and the average signal-to-noise ratio of the reflected beacon signal in the reflected opportunity signal.

[0028] Furthermore, the normalized average signal-to-noise ratio of the reflected beacon signal in step 4 is:

[0029]

[0030] where is the average signal-to-noise ratio of the reflected beacon signal; is the average signal-to-noise ratio of the direct beacon signal.

[0031] Furthermore, step 5 includes:

[0032] Comparing the normalized average signal-to-noise ratio of the reflected beacon signal with a set threshold. If is higher than the set threshold, it is determined that the coordinate of the specular reflection point on the ground of the signal corresponding to the average signal-to-noise ratio of the reflected beacon signal is water.

[0033] Furthermore, the set threshold is the average signal-to-noise ratio threshold of the normalized beacon signal of water established through prior knowledge.

[0034] Furthermore, step 6 includes:

[0035] Traverse all available satellites with zenith elevation angles higher than the threshold, and determine whether the sub-satellite point area is water. For non-overlapping sub-satellite points, they are judged separately. For overlapping sub-satellite points, if at least one satellite is judged as water, it is judged as water.

[0036] On the other hand, the present invention provides a SoOP-R water body detection system using Internet satellite opportunity signals, including:

[0037] Reflection point coordinate acquisition module: It is used to determine the current position of the UAV-borne receiver based on the flight path of the airborne SoOP-R water body detection system, download the TLE ephemeris data of the used Internet satellite and calculate the current position of the Internet satellite, determine whether the Internet satellite is available, and iteratively invert the signal ground mirror reflection point coordinates of the available Internet satellite;

[0038] Signal-to-noise ratio waterfall diagram acquisition module: It is used to collect the direct signal and reflected signal data calculated and captured at the current position of the UAV-borne receiver, calculate the signal-to-noise ratio of the Internet satellite direct signal and the reflected signal, and draw the time-frequency signal-to-noise ratio waterfall diagram of the direct signal and the reflected signal;

[0039] Average signal-to-noise ratio acquisition module: It is used to capture the beacon signals in the time-frequency signal-to-noise ratio waterfall diagrams of the Internet satellite direct signal and the reflected signal respectively, and calculate the average signal-to-noise ratio of the Internet satellite direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal;

[0040] Normalized average signal-to-noise ratio acquisition module: It is used to obtain the normalized average signal-to-noise ratio of the reflected beacon signal based on the average signal-to-noise ratio of the Internet satellite direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal;

[0041] Water body judgment module: It is used to compare the normalized average signal-to-noise ratio of the reflected beacon signal with the set threshold, and judge whether the signal ground mirror reflection point coordinates corresponding to the average signal-to-noise ratio of the reflected beacon signal are water;

[0042] Traversal module: It is used to traverse all available Internet satellites and determine whether the sub-satellite point area is water;

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention first proposes to use the average signal-to-noise ratio of beacon signals in Internet satellite opportunity signals and their surface reflection signals for water body detection, and constructs an airborne SoOP-R water body detection system. Since the current research on using the SoOP-R remote sensing method of Internet satellite opportunity signals for surface parameter inversion is very limited, using this method can utilize a large number of Internet low-earth orbit satellites while maintaining the advantages of the GNSS-R water body detection method, thereby effectively overcoming the disadvantages of low coverage and time resolution caused by insufficient GNSS satellites in the traditional GNSS-R water body detection method. This method can be applied to fields such as water resource management and flood disaster warning. The implementation of the present invention can demonstrate the potential of the Internet satellite opportunity signal SoOP-R remote sensing method in the field of surface parameter inversion and expand the application scope of Internet satellite opportunity signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a flowchart for calculating the normalized average signal-to-noise ratio of satellite beacon signals and water body detection in the implementation of the present invention.

[0047] Figure 2 It is a schematic diagram of the scenario in the implementation of the present invention.

[0048] Figure 3 It is a flowchart for calculating the visibility of satellite signals and the coordinates of specular reflection points in the implementation of the present invention.

[0049] Figure 4 It is a schematic diagram of the direct beacon signal in the satellite beacon signal spectrum waterfall diagram in the implementation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] In order to make the above-mentioned objects, features, and advantages of the present application more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present application with reference to the drawings. Many specific details are set forth in the following description in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein. Those skilled in the art can make similar improvements without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0051] As Figure 1 shown, this embodiment provides a SoOP-R water body detection method using Internet satellite opportunity signals, including the following steps:

[0052] Step 1: Based on the flight path of the airborne SoOP-R water body detection system, determine the current position of the airborne receiver of the UAV, download the TLE ephemeris data of the used Internet satellite, calculate the current position of the Internet satellite, determine whether the Internet satellite is available, and iteratively invert the coordinates of the signal ground mirror reflection point of the available Internet satellite;

[0053] As Figure 2 shown, the airborne SoOP-R water body detection system includes: a UAV 4, a zenith antenna 2 and a nadir antenna 3 mounted on the UAV. Among them, the zenith antenna 2 receives the direct signal S d of the Internet satellite 1, and the nadir antenna receives the ground reflection signal S r . The direct signal and the reflection signal are respectively sent to the direct signal real-time spectrum analyzer 5 and the reflection signal real-time spectrum analyzer 6 through cables for data acquisition. The acquired signals are sent to the computer 7 through a USB3.0 cable for spectrum analysis and water body discrimination.

[0054] When performing water body discrimination, the ground mirror reflection point of the Internet satellite reflection signal, that is, the sub-satellite point coordinate P r is required. In order to evaluate in advance the number of Internet satellite opportunity signals S n that can be received during the flight mission of performing water body detection tasks and the coordinates P r of the ground mirror reflection point of the received reflection signal, and to determine the source satellite of the received signal when the structure of the Internet satellite opportunity signal is unknown. As Figure 3 shown, before performing the water body detection task, the airborne SoOP-R water body detection system needs to make preparations according to the pre-determined flight path and time data before receiving the Internet satellite opportunity signal, that is, to give the position of the Internet satellite and the position of the receiver.

[0055] Calculating the position of the Internet satellite includes:

[0056] Calculating the satellite coordinates according to the latest two-line orbital parameter data provided by the Internet satellite website; the basic process is to download the two-line orbital parameters TLE of the Starlink satellite from a professional website (such as Space-Track or Celestrak), combine the Coordinated Universal Time (UTC) time at the time of observation, and use the orbital SGP4 model to calculate the position r s of the satellite in the Earth-Centered Earth-Fixed (ECEF) coordinate system, with coordinates (X s , Y s , Z s ).

[0057] Obtaining the position of the airborne receiver includes:

[0058] The longitude (L), latitude (B), and altitude (H) of the aircraft in the geodetic coordinate system are obtained through the on-board navigator, and then converted to the Earth-centered Earth-fixed coordinate system to obtain the position r R , and the coordinates are (X R , Y R , Z R ). The conversion formula is as follows:

[0059]

[0060] where N is the radius of the prime vertical circle at that point, e 2 =(a 2 -b 2 ) / a 2 , and a, b, and e are the semi-major axis, semi-minor axis, and first eccentricity of the corresponding ellipsoid in the geodetic coordinate system, respectively.

[0061] The satellites that can be used are judged as follows:

[0062] After obtaining the position r of the Internet satellite at the current moment S and the current position r of the on-board receiver R , by calculating the elevation angle θ of the Internet satellite relative to the receiver, the elevation angle calculation formula for the satellites that can receive signals can be obtained as:

[0063]

[0064] where Z RS is the component of the relative position vector r RS in the direction of the normal of the ground plane. After calculating the elevation angle θ, judge the relationship between the elevation angle and the elevation angle threshold. If the elevation angle is greater than the threshold, it is judged that the signal of this satellite can be received by the antenna.

[0065] After judging the satellites that the receiver can receive the opportunity signal, through the position r of the Internet satellite at the current moment calculated before s and the current position r of the receiver R , and unifying their coordinate systems to the ECEF coordinate system, using the principle that the reflection angle is equal to the incident angle, the coordinates P of the specular reflection point on the ground surface of the signal can be iteratively inverted r , and it is determined that this satellite is available.

[0066] Step 2: Collect the data of the direct signal and the reflected signal captured by the on-board receiver of the UAV at the current position, calculate the signal-to-noise ratio of the direct signal and the reflected signal of the Internet satellite, and draw the time-frequency signal-to-noise ratio waterfall diagram of the direct signal and the reflected signal;

[0067] In this embodiment, Figure 2After the zenith antenna 2 receives the direct opportunity signal of the Internet satellite, and the nadir antenna 3 receives the reflected signal, the signal is received and processed by two spectrum analyzers (such as SignalHound's BB60C real-time spectrum analyzer) 5 and 6 connected to the antenna, and the signal power P of the Internet satellite opportunity signal beacon can be calculated and output in the processing computer 7. signal and noise power P noise , the signal-to-noise ratio (SNR) of the Internet satellite opportunity signal can be calculated by processing the frequency domain signal output by the BB60C real-time spectrum analyzer with the Spike software installed in 7. The calculation formula is as follows:

[0068] SNR = P signal / P noise

[0069] like Figure 4 As shown in the figure, the signal-to-noise ratio (SNR) of the direct opportunity signal of the Internet satellite is calculated by Spike software. direct After that, a time-frequency signal-to-noise ratio waterfall chart can be drawn; the horizontal axis of the chart is the frequency of the beacon signal, the vertical axis is the time, and the color represents the power of the signal and the noise floor. It can be seen from the figure that the power of the beacon signal is stronger than the power of the background noise. Similarly, the signal-to-noise ratio of the opportunity signal reflected by the Internet satellite is calculated.

[0070] Step 3: Capture the beacon signals in the time-frequency signal-to-noise ratio waterfall diagram of the direct signal and the reflected signal of the Internet satellite respectively, and calculate the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of a single Internet satellite;

[0071] In this embodiment, a continuous stable narrowband signal with a signal-to-noise ratio significantly higher than the background is searched in the waterfall diagram, and the frequency of the narrowband signal will drift to a certain extent due to the Doppler effect caused by the movement of the Internet satellite. These multiple high signal-to-noise ratio narrowband signals parallel to each other in the waterfall diagram received at the same time are the beacon signals transmitted by an Internet satellite. Figure 4 It can be seen that the direct signal has 9 equally spaced beacon signals. Figure 1 As shown in the figure, the beacon signal is captured in the direct signal waterfall diagram, and the signal-to-noise ratio (SNR) of each beacon signal is recorded separately. i Calculate the average signal-to-noise ratio of the beacon signal in the direct opportunity signal by comparing it with the total number of beacon signals n that can be received. The calculation formula is as follows:

[0072]

[0073] In the reflection signal waterfall diagram at the same time, find the beacon signal of the direct signal corresponding to the reflected signal, and use the same method to calculate the average signal-to-noise ratio of the reflected signal beacon signal

[0074] Step 4: Based on the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of the Internet satellite, obtain the normalized average signal-to-noise ratio of the reflected beacon signal;

[0075] By receiving the direct opportunity signal of the Internet satellite and its corresponding reflected signal, capture the beacon signal in the direct signal and calculate its average signal-to-noise ratio And find the corresponding beacon signal in the reflected signal at the same time, and calculate the average signal-to-noise ratio In order to eliminate the influence of the direct signal intensity on the reflected signal intensity, so as to judge whether the specular reflection point area of the signal ground is water through the signal-to-noise ratio intensity of the reflected signal, it is necessary to normalize the average signal-to-noise ratio of the reflected beacon signal with the average signal-to-noise ratio of the direct beacon signal, and calculate the normalized average signal-to-noise ratio of the reflected beacon signal The calculation formula is as follows:

[0076]

[0077] Step 5: Compare the normalized average signal-to-noise ratio of the reflected beacon signal with the set threshold to judge whether the signal ground specular reflection point coordinates corresponding to the average signal-to-noise ratio of the reflected beacon signal are water;

[0078] According to the knowledge of electromagnetic scattering, if the normalized average signal-to-noise ratio is higher, it means that the reflected signal is stronger. If the reflecting surface is water, the signal-to-noise ratio of the reflected signal will be significantly higher than that of non-water. Combine electromagnetic knowledge to establish the normalized average signal-to-noise ratio threshold SNR of the beacon signal in the water area in the ground experiment th , after calculating the normalized average signal-to-noise ratio of the beacon signal in the reflected opportunity signal of the Internet satellite , by comparing with the normalized average signal-to-noise ratio threshold of the water body beacon signal, if is higher than the threshold SNR th , then it is determined that the specular reflection area of the signal is water.

[0079] Step 6: Traverse all available Internet satellites and determine whether the sub-satellite point area is water.

[0080] In this embodiment, traverse all available satellites with zenith direction elevation angles higher than the threshold, and determine whether the sub-satellite point area is water. The non-overlapping sub-satellite points are discriminated separately, and as long as one of the overlapping satellites is discriminated as water, it is discriminated as water.

[0081] Embodiment 2

[0082] This embodiment provides a SoOP-R water body detection system using the opportunity signal of the Internet satellite, including:

[0083] Reflection point coordinate acquisition module: It is used to determine the current position of the UAV-borne receiver based on the flight path of the airborne SoOP-R water body detection system, download the TLE ephemeris data of the used Internet satellite and calculate the current position of the Internet satellite, determine whether the Internet satellite is available, and iteratively invert the surface mirror reflection point coordinates of the signals of the available Internet satellites;

[0084] Signal-to-noise ratio waterfall plot acquisition module: It is used to collect the direct signal and reflected signal data captured and calculated at the current position of the UAV-borne receiver, calculate the signal-to-noise ratio of the direct signal and the reflected signal of the Internet satellite, and draw the time-frequency signal-to-noise ratio waterfall plot of the direct signal and the reflected signal;

[0085] Average signal-to-noise ratio acquisition module: It is used to capture the beacon signals in the time-frequency signal-to-noise ratio waterfall plots of the direct signal and the reflected signal of the Internet satellite respectively, and calculate the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of a single Internet satellite;

[0086] Normalized average signal-to-noise ratio acquisition module: It is used to obtain the normalized average signal-to-noise ratio of the reflected beacon signal based on the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of the Internet satellite;

[0087] Water body judgment module: It is used to compare the normalized average signal-to-noise ratio of the reflected beacon signal with the set threshold, and judge whether the surface mirror reflection point coordinates of the signal corresponding to the average signal-to-noise ratio of the reflected beacon signal are water bodies;

[0088] Traversal module: It is used to traverse all available Internet satellites and determine whether the sub-satellite point area is a water body.

[0089] As mentioned above, it is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the technical field of the present application within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

[0090] It should be understood that the parts not elaborated in detail in this specification belong to the prior art.

[0091] It should be understood that the above description of the preferred embodiment is relatively detailed, and it should not be considered as a limitation to the protection scope of the present invention patent. Under the inspiration of the present invention, those of ordinary skill in the art can also make substitutions or deformations without departing from the protection scope defined by the claims of the present invention, and all fall within the protection scope of the present invention. The scope of protection requested by the present invention shall be subject to the appended claims.

Claims

1. A SoOP-R water body detection method using Internet satellite opportunity signals, characterized in that, Including the following steps: Step 1: Based on the flight path of the airborne SoOP-R water body detection system, determine the current position of the airborne receiver of the UAV, download the TLE ephemeris data of the used Internet satellite and calculate the current position of the Internet satellite at the current moment, determine whether the Internet satellite is available, and iteratively invert the coordinates of the signal ground mirror reflection point of the available Internet satellite; Step 2: Collect the direct signal and reflected signal data captured under the current position of the airborne receiver of the UAV, calculate the signal-to-noise ratio of the direct signal and the reflected signal of the Internet satellite, and draw the time-frequency signal-to-noise ratio waterfall diagram of the direct signal and the reflected signal; Step 3: Capture the beacon signals in the time-frequency signal-to-noise ratio waterfall diagrams of the direct signal and the reflected signal of the Internet satellite respectively, and calculate the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of a single Internet satellite; Step 4: Based on the average signal-to-noise ratio of the direct beacon signal and the average signal-to-noise ratio of the reflected beacon signal of the Internet satellite, obtain the normalized average signal-to-noise ratio of the reflected beacon signal; Step 5: Compare the normalized average signal-to-noise ratio of the reflected beacon signal with the set threshold to determine whether the signal ground mirror reflection point coordinates corresponding to the average signal-to-noise ratio of the reflected beacon signal are a water body; Step 6: Traverse all available Internet satellites and determine whether the sub-satellite point area is a water body.

2. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 1, wherein In the above-mentioned step 1, the airborne SoOP-R water body detection system includes: a drone, a zenith antenna and a nadir antenna mounted on the drone. Among them, the zenith antenna receives the direct signal S of the Internet satellite d , and the nadir antenna receives the surface reflection signal S r . The direct signal and the reflection signal are respectively sent to the direct signal real-time spectrum analyzer and the reflection signal real-time spectrum analyzer through cables to perform data acquisition, and the acquired signals are sent to a computer for spectrum analysis and water body discrimination.

3. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 1, characterized in that The said Step 1 includes: Calculate the current moment coordinates of the Internet satellite according to the TLE ephemeris data of the Internet satellite; Obtain the current position of the airborne receiver of the UAV through the airborne navigator on the UAV; Calculate the elevation angle of the Internet satellite relative to the airborne receiver based on the current moment coordinates of the Internet satellite and the current position of the airborne receiver; Determine whether the Internet satellite is available based on the magnitude of the elevation angle and the elevation angle threshold; Unify the calculated current moment coordinates of the Internet satellite and the current position of the receiver into the same coordinate system, and use the principle that the reflection angle and the incident angle are equal to iteratively invert the coordinates of the signal ground mirror reflection point.

4. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 2, characterized in that, The said Step 2 includes: The direct signal real-time spectrum analyzer and the reflected signal real-time spectrum analyzer respectively process the direct opportunity signal of the Internet satellite received by the zenith antenna and the reflected opportunity signal received by the nadir antenna; The computer calculates and outputs the beacon signal power of the direct opportunity signal of the Internet satellite, the noise power of the direct opportunity signal, the beacon signal power of the reflected opportunity signal, and the noise power of the reflected opportunity signal. Calculate the signal-to-noise ratio of the direct signal of the Internet satellite based on the beacon signal power of the direct opportunity signal and the noise power of the direct opportunity signal; Calculate the signal-to-noise ratio of the reflected signal of the Internet satellite based on the beacon signal power of the reflected opportunity signal and the noise power of the reflected opportunity signal; Taking the frequency where the beacon signal is located as the abscissa and time as the ordinate, draw the time-frequency signal-to-noise ratio waterfall diagrams of the direct signal and the reflected signal respectively.

5. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 1, characterized in that, The capturing of the beacon signals in the time-frequency signal-to-noise ratio waterfall diagrams of the direct signal and the reflected signal of the Internet satellite in the said Step 3 includes: In the time-frequency signal-to-noise ratio waterfall diagram of the direct signal and the reflected signal, respectively find continuous and stable narrowband signals where the signal-to-noise ratio of the direct signal and the reflected signal is significantly higher than the background and there is a certain Doppler effect. Record the signal-to-noise ratio of each beacon signal and the total number of beacon signals that can be received, so as to calculate the average signal-to-noise ratio of the direct beacon signals in the direct opportunity signal and the average signal-to-noise ratio of the reflected beacon signals in the reflected opportunity signal.

6. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 1, characterized in that, The normalized average signal-to-noise ratio of the reflected beacon signal in step 4 is as follows: wherein, is the average signal-to-noise ratio of the reflected beacon signal; is the average signal-to-noise ratio of the direct beacon signal.

7. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 6, wherein, The said step 5 includes: Normalize the average signal-to-noise ratio of the reflected beacon signal Compare it with the set threshold. If it is higher than the set threshold, then it is determined that the coordinate of the signal ground mirror reflection point corresponding to the average signal-to-noise ratio of the reflected beacon signal is water 8. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 7, characterized in that, The set threshold is the average signal-to-noise ratio threshold of the water body normalized beacon signal established through prior knowledge.

9. The SoOP-R water body detection method using Internet satellite opportunity signals according to claim 1, characterized in that, The said step 6 includes: Traverse all available satellites with zenith elevation angles higher than the threshold, and determine whether the sub-satellite point area is a water body. For non-overlapping sub-satellite points, make separate judgments. For overlapping sub-satellite points, if at least one satellite is judged to be a water body, then it is judged to be a water body.

10. A SoOP-R water body detection system using Internet satellite opportunity signals, characterized in that, Includes: Reflection point coordinate acquisition module: It is used to determine the current position of the UAV airborne receiver based on the flight path of the airborne SoOP-R water body detection system, download the TLE ephemeris data of the used Internet satellite and calculate the current position of the Internet satellite at the current moment, determine whether the Internet satellite is available, and iteratively invert the signal ground mirror reflection point coordinates of the available Internet satellite; Signal-to-noise ratio waterfall diagram acquisition module: It is used to collect the direct signal and reflected signal data calculated and captured at the current position of the UAV airborne receiver, calculate the signal-to-noise ratio of the Internet satellite direct signal and the reflected signal, and draw the time-frequency signal-to-noise ratio waterfall diagram of the direct signal and the reflected signal; Average signal-to-noise ratio acquisition module: It is used to capture the beacon signals in the time-frequency signal-to-noise ratio waterfall diagrams of the Internet satellite direct signal and the reflected signal respectively, and calculate the average signal-to-noise ratio of the direct beacon signals and the average signal-to-noise ratio of the reflected beacon signals of a single Internet satellite; Normalized average signal-to-noise ratio acquisition module: It is used to obtain the normalized average signal-to-noise ratio of the reflected beacon signals based on the average signal-to-noise ratio of the Internet satellite direct beacon signals and the average signal-to-noise ratio of the reflected beacon signals; Water body judgment module: It is used to compare the normalized average signal-to-noise ratio of the reflected beacon signals with the set threshold to judge whether the signal ground mirror reflection point coordinates corresponding to the average signal-to-noise ratio of the reflected beacon signals are a water body; Traversal module: It is used to traverse all available Internet satellites and determine whether the sub-satellite point area is a water body; The SoOP-R water body detection system using the Internet satellite opportunity signal is used to execute the steps in the SoOP-R water body detection method using the Internet satellite opportunity signal described in any one of claims 1-9.

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