CN116976091B - Wave environment factor influence analysis method suitable for microwave remote sensing satellite - Google Patents
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Abstract
The invention relates to a sea wave environmental factor influence analysis method suitable for a microwave remote sensing satellite, which comprises the following steps: s1, constructing a simulation task scene; s2, determining a visible window set between a satellite load and a target, and between a satellite and a ground station, and forming a satellite observation window set according to the visible window set; s3, setting environmental factors of the simulation task sea area; s4, calculating a backscattering coefficient of the sea clutter according to the wave height and the radar inherent parameters; s5, solving the signal-to-clutter ratio of echo signals according to the backscattering coefficient of sea clutter, and screening the satellite observation window set by a threshold comparison method; and S6, performing index calculation and analysis on the satellite observation window set according to an evaluation criterion to obtain an observation capability evaluation result. The invention can avoid repeated execution of the visible window and the task plan in the large sample test, improves the efficiency of analysis and evaluation of the environmental impact factors, and expands the coverage of the evaluation of the collaborative observation capability of the satellite system.
Description
Technical Field
The invention relates to the technical field of aerospace, in particular to the technical field of digital simulation of a complex earth observation satellite system, and particularly relates to a sea wave environmental factor influence analysis method suitable for a microwave remote sensing satellite.
Background
With the development of space technology, earth observation tasks are gradually developed from independent execution of satellites by means of single detection means to cooperative execution of satellite composition observation systems by multiple detection means. The synergistic observation system composed of various satellites can fully exert the respective advantages of different satellites to achieve the effect of 1+1> 2. For example, the optical remote sensing satellite image has rich details, is easy for human eyes to recognize and read, but the imaging quality is greatly influenced by meteorological conditions such as cloud layers and the like, and can only be used in daytime; the microwave remote sensing satellite has no limit, can meet the use requirements of all weather and all time, but has single observation result detail information, and increases the difficulty of effective information extraction. The success rate of recognition and interpretation can be obviously improved by comprehensively utilizing two observation means.
The general steps for evaluating the earth observation capability of a satellite system by using a digital simulation analysis method are as follows: first, determining simulation time and simulation entity element information (including satellite, target, ground station, etc.), and constructing a simulation scene. Secondly, calculating visible windows between the satellite load and the target and between the satellite and the ground station according to the satellite load observation range and the antenna transmission range; then, classifying, sorting, combining and conflict resolution are carried out on different types of visible windows according to task planning rules to form a satellite observation window set; and finally, performing index calculation and analysis on the observation window set by using an evaluation criterion to obtain an observation capability evaluation result.
In the process, the traditional satellite system earth observation capability evaluation generally brings environmental factors such as cloud cover, sea wave and the like into a task planning rule to participate in screening of a visible window, and a large sample simulation test set is generated. Because each test sample is independently operated, the method has respective visible window calculation and task planning links. In large sample tests that only consider environmental impact factors, a fixed, constant visible window can be calculated separately in each test sample, resulting in wasted computational resources. In order to improve the efficiency of analysis of environmental impact factors, it is necessary to study the analysis and evaluation method that environmental factors are directly applied to satellite observation window set screening.
The load of the microwave remote sensing satellite detects and positions the target in a mode of radiating electromagnetic waves and detecting echo signals. Because of less observation result detail information, the method is mainly used for target observation in the marine background. In the process of irradiating the sea surface by microwaves, backward echoes (sea clutter) formed by the scattering of microwave signals by sea waves formed by sea wind are main factors affecting the microwave observation capability of the marine environment (see Rashmi Mital: an Improved Empirical Model for Radar Sea Clutter Reflectivity, naval Research Laboratory). The addition of sea clutter reduces the signal-to-clutter ratio of the echo information, thereby reducing the performance of microwave observation.
Disclosure of Invention
In view of the above, the present invention aims to solve the above problems, and provides a method for analyzing the influence of wave environmental factors suitable for a microwave remote sensing satellite, which uses a signal-to-noise ratio threshold as a judgment criterion to examine the influence of wave environmental factors on the observation capability of the microwave remote sensing satellite, so as to improve the efficiency of analyzing and evaluating the environmental influence factors.
The embodiment of the invention provides a sea wave environmental factor influence analysis method suitable for a microwave remote sensing satellite, which comprises the following steps:
s1, constructing a simulation task scene;
s2, determining a visible window set between a satellite load and a target, and between a satellite and a ground station, and forming a satellite observation window set according to the visible window set;
s3, setting environmental factors of the simulation task sea area;
s4, calculating a backscattering coefficient of the sea clutter according to the wave height and the radar inherent parameters;
s5, solving the signal-to-clutter ratio of echo signals according to the backscattering coefficient of sea clutter, and screening the satellite observation window set by a threshold comparison method;
and S6, performing index calculation and analysis on the satellite observation window set according to an evaluation criterion to obtain an observation capability evaluation result.
Further, the step S1 includes:
and determining simulation time of the simulation task, satellite orbit and load parameters, ground station and target positions and a simulation task planning rule, thereby constructing a simulation task scene.
Further, the step S2 includes:
in a simulation time interval, solving a boundary point set meeting preset precision and constraint conditions through a numerical iteration method, summarizing and sorting to form the visible window set, and classifying, sorting, combining and conflict resolution the visible window set according to a task planning rule to form the satellite observation window set;
the constraint conditions comprise satellite load-to-target visibility calculation and satellite-to-ground station visibility calculation, wherein the satellite load-to-target visibility calculation comprises earth shielding, target and load field visibility and target solar altitude angle, and the satellite-to-ground station visibility calculation comprises earth shielding and ground station and satellite antenna visibility.
Further, the task orchestration rules include a minimum time or a maximum number of times.
Further, the environmental factors of the sea area in S3 include wave height and wind speed;
considering a fully developed sea surface, a sea surface altitude field is generated using Perlin noise to simulate sea wave altitude, which is constructed as follows:
wherein the function z is a function 3p 2 -2p 3 Finishing the three-time harmonic interpolation on the sea wave control points in the x direction and the y direction respectively; 2 i Is the frequency of the control points and is related to the distance between the noise control points; input parameter with seed being Perlin noiseA number.
Further, in the step S4, the backscattering coefficient of the sea clutter is calculated by:
wherein alpha is sea rubbing angle, SS is sea state grade, f is radar frequency, c 1 ～c 5 Is a constant value.
Further, in the step S5, the signal-to-noise ratio of the echo signal is:
wherein sigma t For the target RCS, sigma c Is sea clutter RCS, alpha is sea wiping angle, sigma 0 Is the backscattering coefficient of sea clutter, R is the detection distance, theta 3dB Is the 3dB beam width of the antenna, S r Is the distance resolution;
and calculating the signal-to-noise ratio of the echo signals for each satellite observation window in the satellite observation window set, and discarding the corresponding observation window when any signal-to-noise ratio is smaller than a set threshold value.
Further, the step S6 includes:
taking two indexes of the time of the earth observation satellite system for the observation and revisiting of the ocean target and the tracking time of the ocean target into consideration:
a, maximum observed revisit time:
b, tracking duration: t (T) fo ＝T ie -T 1s ；
Wherein the satellite observation window set is provided with N observation windows, and the ith window is [ T ] is ，T ie ]，T is For window start time, T ie Is the window end time; the window following the ith window is the (i+1) th window [ T ] i+1，s ，T i+1，e ]，T i+1，s For window start time, T i+1，e For window end time, T 1s Is the start time of the first window.
In summary, in the method for analyzing the influence of the sea wave environmental factors, in the collaborative observation capability evaluation of a complex satellite system, the influence of the sea wave environmental factors on the microwave remote sensing satellite is introduced, and the signal-to-noise ratio threshold is used as a judgment criterion to directly screen a satellite observation window set, so that the repeated implementation of a visible window and task planning in a large sample test is avoided, the efficiency of the environmental influence factor analysis and evaluation is improved, and the coverage of the satellite system collaborative observation capability evaluation is expanded.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for analyzing influence of sea wave environmental factors according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a method for analyzing influence of sea wave environmental factors according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the method for performing satellite collaborative observation to complete migration tracking of marine animals according to an embodiment of the present invention;
FIG. 4 is a schematic view of a scene of a rule for judging that a target is blocked by the earth in the method according to the embodiment of the invention;
FIG. 5 is a schematic diagram of a judgment rule for calculating the visibility of a target and a load field in the method according to the embodiment of the present invention;
FIG. 6 is a schematic diagram of a rule for determining visibility of a range between a ground station and a satellite antenna according to the method of the present invention;
FIG. 7 is a schematic diagram of a method for calculating a target solar altitude in the method according to the embodiment of the invention;
fig. 8 is a schematic view of a typical sea wave environment.
Detailed Description
The description of the embodiments of this specification should be taken in conjunction with the accompanying drawings, which are a complete description of the embodiments. In the drawings, the shape or thickness of the embodiments may be enlarged and indicated simply or conveniently. Furthermore, portions of the structures in the drawings will be described in terms of separate descriptions, and it should be noted that elements not shown or described in the drawings are in a form known to those of ordinary skill in the art.
Any references to directions and orientations in the description of the embodiments herein are for convenience only and should not be construed as limiting the scope of the invention in any way. The following description of the preferred embodiments will refer to combinations of features, which may be present alone or in combination, and the invention is not particularly limited to the preferred embodiments. The scope of the invention is defined by the claims.
As shown in fig. 1 and 2, the method for analyzing the influence of the sea wave environmental factors according to the embodiment of the invention comprises the following steps of
S1, constructing a simulation task scene.
The method mainly comprises the steps of determining simulation time of a simulation task, satellite orbit and load parameters, ground station and target positions and simulation task planning rules.
As shown in fig. 3, taking high resolution optical imaging and microwave imaging to earth observation satellite for cooperative marine animal migration tracking observation as an example, the main flow of constructing a simulation task scene is as follows: when the mission is started, the ground is used for uploading the observation area to an optical/microwave imaging satellite which is about to pass through the mission area through a ground station according to the grasped marine animal position information. The satellite completes earth-directed observation tasks according to the plan and downloads the observations to the ground station. Analyzing the ground surface observation result, if the target marine animal is found, updating the position information of the target marine animal for later observation tasks, and repeating the process; if the target is not found, the target is considered to be lost, and the task fails.
Because marine animals are in a continuous motion state in the migration process, the visible range of a high-resolution imaging satellite is usually smaller, once an observation target is lost, the recovery is difficult to complete in a wide-area search mode, and the task is failed. Therefore, the microwave imaging satellite is used for executing a night tracking observation plan, and the optical imaging satellite is used for filling the loss of the microwave imaging satellite observation capability caused by sea waves, so that the reliability of an observation result can be effectively improved.
S2, determining a visible window set between the satellite load and the target, and between the satellite and the ground station, and forming a satellite observation window set according to the visible window set.
The visible window set needs to be calculated first. The satellite load visibility calculation needs to consider earth shielding, the object is located in the load view field range and the object sun altitude angle, and the satellite visibility calculation needs to consider earth shielding and the ground station is located in the satellite antenna range. The constraint calculation mode is as follows:
a) Earth shielding: as shown in fig. 4, if the load and the target line of sight are not blocked by the earth, it is necessary to satisfy: geocentric to satellite vectorVector from earth center to target->The included angle alpha between the two is not more than 90 degrees.
b) Target and load field of view visibility: as shown in fig. 5, if the target is located within the load field of view, then it is necessary to satisfy the target and satellite link and plane S H Is not greater than triangle delta SAB And plane S H A dihedral angle v formed, and a connection between the target and the satellite and a plane S V Is not greater than triangle delta SAD And plane S V The dihedral angle H formed.
c) Ground station and satellite antenna visibility: as shown in fig. 6, if the ground station is located in the antenna range, γ < α needs to be satisfied, where γ is the angle between the connection line of the satellite and the target and the central axis of the antenna, and α is the half cone angle of the antenna range.
d) Target solar altitude: for optical remote sensing satellites it is necessary to calculate the solar altitude of the target. As shown in figure 7 of the drawings,beta is the target solar altitude, which is the target vector to the sun.
And in the task time interval, solving a boundary point set meeting given precision and the constraint conditions through a numerical iteration method, and forming a visible window set through summarizing and sorting. And classifying, sorting, combining and conflict resolution are carried out on the visible window set according to a task planning criterion (the shortest time/the highest time) to form a satellite observation window set.
S3, setting environmental factors of the simulation task sea area.
Environmental factors in the sea area include wave height and wind speed.
The sea surface of the simulation mission area is assumed to be well developed, i.e. the wind speed is related to the mean sea wave height, while the Perlin noise is used to generate a height field of the sea surface to simulate the sea wave height. Perlin noise is widely used for simulating textures of terrains, water surfaces and cloud layers, and is characterized by the correspondence between results and input parameters. That is, if the same parameters are input, the noise results generated will be the same. The form of the Perlin noise function is not unique, and a suitable function may be constructed as needed, for example, using the Perlin noise function as follows:
a) For any point (x, y), 4 adjacent noise control points are set as (x 0 ，y 0 )、(x 0 ，y 1 )、(x 1 ，y 0 ) And (x) 1 ，y 1 ) The influence value of each control point on (x, y) is
b) Using a function 3p 2 -2p 3 Triple harmonic interpolation of s and t, u and v in the x-direction, respectively
c) Similarly, three times of harmonic interpolation are carried out on the a and the b in the y direction, and the output of the Perlin noise is obtained
d) Fractal superposition of the functions results in sea surface height h of
Wherein the first function of the function z is the frequency of the control points, related to the distance between the noise control points, and seed is the input parameter of the Perlin noise.
By selecting a proper seed value, a height field close to the actual sea surface and corresponding sea wave height and wind speed information can be generated in the task area.
And S4, calculating the backscattering coefficient of the sea clutter according to the wave height and the radar inherent parameters.
The backscattering coefficient of the sea clutter, which is a main factor of the sea wave affecting the ability of the microwave load to observe the marine environment, can be calculated based on the improved NRL model through wave height and radar intrinsic parameters, and described by using the backscattering coefficient of the sea clutter. The radar cross-sectional area is equal to the radar cross-sectional area of a unit area in value, and the average intensity of the microwave back scattering capability of the microwave irradiation area of the unit area is represented. The NRL model is a backscattering coefficient model proposed by Vilhelm and Rashmi in 2009. Is suitable for wiping microwave load with sea angle from 0.1, 0.3, 1.0, 3.0, 10.0, 30.0 to 60.0 degrees and frequency from 0.5GHz to 35 GHz.
In calculating the backscattering coefficient of sea clutter using the modified NRL model, it is necessary to first determine the sea state class from the sea wave altitude look-up table 1.
TABLE 1 sea wave height and sea condition rating Table
The backscattering coefficient of the sea clutter is calculated by:
wherein alpha is sea rubbing angle, SS is sea state grade, f is radar frequency (unit is GHz), c 1 ～c 5 Is constant, c 1 ～c 5 See table 2 for values of (c):
TABLE 2 empirical norms for NRL models
It should be noted that for different polarizations, c 1 ～c 5 The values of (2) are different and are selected according to the needs in actual use.
S5, solving the signal-to-clutter ratio of echo signals according to the backscattering coefficient of sea clutter, and screening the satellite observation window set by a threshold comparison method;
the signal-to-clutter ratio of the echo signals is solved based on the backscattering coefficient of the sea clutter is as follows:
wherein sigma t For the target RCS, sigma c Is sea clutter RCS, alpha is sea wiping angle, sigma 0 Is the backscattering coefficient of sea clutter, R is the detection distance, theta 3dB Is the 3dB beam width of the antenna, S r Is the distance resolution;
and calculating the signal-to-noise ratio of the echo signals for each satellite observation window in the satellite observation window set, and discarding the corresponding observation window when any signal-to-noise ratio is smaller than a set threshold value.
And S6, performing index calculation and analysis on the satellite observation window set according to an evaluation criterion to obtain an observation capability evaluation result.
And (5) checking two indexes of the time of revisiting the observation of the marine target and the time of tracking the marine target by the earth observation satellite system to evaluate the earth observation capability of the satellite system. The set of observation windows is recorded as:
n observation windows are added, and the ith window is [ T ] is ，T ie ]，T is For its window start time, T ie For its window end time. The calculation method of the definition index is as follows:
a) Observe revisit time T re : the revisit time is defined as the next start time minus the previous end time of the two adjacent windows. Including maximum, minimum, and average.
b) Tracking duration T fo : let i window be the last window of observed target, its window end time be T ie The first observation window starts at a time T ls Then:
T fo ＝T ie -T ls
in summary, the invention has the following beneficial effects:
according to the invention, in the collaborative observation capability evaluation of a complex satellite system, the influence of sea wave environmental factors on a microwave remote sensing satellite is introduced, and the satellite observation window set is directly screened by using the signal-to-noise ratio threshold as a judgment criterion, so that the repeated implementation of visible window and task planning in a large sample test is avoided, the efficiency of environmental influence factor analysis and evaluation is improved, and the coverage of satellite system collaborative observation capability evaluation is expanded.
The following describes the sea wave environmental factor influence analysis method in detail by taking a simulation task as marine animal tracking:
s1, determining scene time, satellite orbit and load parameters, ground station and target positions, task planning rules and the like, and constructing a simulation task scene;
simulation times were 2023, 5, 12, 03:00:00 to 2023, 5, 13, 03:00:00 (UTC).
The optical imaging satellite is a Walker constellation consisting of 9 satellites, three orbit surfaces are all provided, and the detailed orbit elements of the first satellite are as follows:
table 3 optically imaged satellite orbital elements
The maneuvering range of the satellite is-45 degrees to +45 degrees, and the breadth is 200km.
The microwave imaging satellite is a Walker constellation consisting of 9 satellites, three orbit surfaces are all provided, and the detailed orbit elements of the first satellite are shown in table 4:
table 4 microwave imaging satellite orbital elements
The satellite imaging range is 12-48 degrees, and the instantaneous breadth is 200km.
The target marine animal to be tracked is initially located in a south sea area, the longitude and latitude coordinates are (115, 15), the movement speed is 20 km/h, and the ground station selects three stations.
S2, calculating visible windows between the satellite load and the target, and between the satellite and the ground station, and sorting the visible window set according to the task planning rule to form a satellite observation sequence.
Counting satellite observation sequences, and observing the optical satellites 22 times with a maximum interval of 51324.5 seconds; the microwave star was observed 46 times with a maximum interval of 10618.9 seconds.
S3, setting environmental factors such as wave height, wind speed and the like of the task sea area.
The typical sea state grade of the mission area is shown in fig. 8, and the darker the color is, the higher the sea state grade is.
S4, calculating the backscattering coefficient of the sea clutter based on the improved NRL model passing wave height and radar inherent parameters.
The microwave satellite adopts an X wave band with the frequency of 10GHz, the included angle between a connecting line of the satellite and a target and the horizontal plane is obtained when the sea angle is observed, and when the included angle is 32 degrees, the backscattering coefficients of sea conditions at all levels are shown in the table 5:
table 5 backscattering coefficient values under typical scenarios
S5, solving the signal-to-noise ratio of the echo signals based on the backscattering coefficient of the sea clutter, and determining the stay of the satellite observation window set in a threshold comparison mode.
Let the signal-to-noise ratio threshold be 12dB, the range resolution be 2 meters, the target RCS be 20 square meters, and the 3dB beamwidth of the antenna be 7 degrees.
And S6, performing index calculation and analysis on the observation window set by using an evaluation criterion to obtain an observation capability evaluation result.
Under different maximum sea condition grades, the two index results of the time of the earth observation satellite system for the observation revisit of the ocean target and the time of the ocean target tracking are as follows:
table 6 revisit time and tracking duration results
The satellite observation system can complete marine animal tracking tasks under the sea condition below level 4.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (4)
1. The method for analyzing the influence of the wave environmental factors suitable for the microwave remote sensing satellite is characterized by comprising the following steps of:
s1, constructing a simulation task scene;
s2, determining a visible window set between a satellite load and a target, and between a satellite and a ground station, and forming a satellite observation window set according to the visible window set;
s3, setting environmental factors of the simulation task sea area;
s4, calculating a backscattering coefficient of the sea clutter according to the wave height and the radar inherent parameters;
s5, solving the signal-to-clutter ratio of echo signals according to the backscattering coefficient of sea clutter, and screening the satellite observation window set by a threshold comparison method;
s6, performing index calculation and analysis on the satellite observation window set according to an evaluation criterion to obtain an observation capability evaluation result;
the step S2 comprises the following steps:
in a simulation time interval, solving a boundary point set meeting preset precision and constraint conditions through a numerical iteration method, summarizing and sorting to form the visible window set, and classifying, sorting, combining and conflict resolution the visible window set according to a task planning rule to form the satellite observation window set;
the constraint conditions comprise satellite load visibility calculation on a target and satellite ground station visibility calculation, wherein the satellite load visibility calculation on the target comprises earth shielding, target and load field visibility and target solar altitude angle, and the satellite ground station visibility calculation comprises earth shielding and ground station and satellite antenna visibility;
in the step S3, the environmental factors of the sea area comprise wave height and wind speed;
considering a fully developed sea surface, a sea surface altitude field is generated using Perlin noise to simulate sea wave altitude, which is constructed as follows:
wherein the function z is a function 3p 2 -2p 3 Finishing the three-time harmonic interpolation on the sea wave control points in the x direction and the y direction respectively; 2 i Is the frequency of the control points and is related to the distance between the noise control points; seed is an input parameter of the Perlin noise;
in the step S4, the back scattering coefficient of the sea clutter is calculated by:
wherein alpha is sea rubbing angle, SS is sea state grade, f is radar frequency, c 1 ～c 5 Is a constant value;
in the step S5, the signal-to-noise ratio of the echo signal is:
wherein sigma t For the target RCS, sigma c Is sea clutter RCS, alpha is sea wiping angle, sigma 0 Is the backscattering coefficient of sea clutter, R is the detection distance, theta 3dB Is the 3dB beam width of the antenna, S r Is the distance resolution;
and calculating the signal-to-noise ratio of the echo signals for each satellite observation window in the satellite observation window set, and discarding the corresponding observation window when any signal-to-noise ratio is smaller than a set threshold value.
2. The method for analyzing influence of sea wave environmental factors applicable to microwave remote sensing satellites according to claim 1, wherein the S1 comprises:
and determining simulation time of the simulation task, satellite orbit and load parameters, ground station and target positions and a simulation task planning rule, thereby constructing a simulation task scene.
3. The method for analyzing the influence of sea wave environmental factors applicable to the microwave remote sensing satellite according to claim 1, wherein the task planning rule comprises the shortest time or the greatest number of times.
4. The method for analyzing influence of sea wave environmental factors applicable to microwave remote sensing satellites according to claim 1, wherein the step S6 comprises:
taking two indexes of the time of the earth observation satellite system for the observation and revisiting of the ocean target and the tracking time of the ocean target into consideration:
a, maximum observed revisit time:
b, tracking duration: t (T) fo ＝T ie -T 1s ；
Wherein the satellite observation window set is provided with N observation windows, and the ith window is [ T ] is ，T ie ]，T is For window start time, T ie Is the window end time; the window following the ith window is the (i+1) th window [ T ] i+1，s ，T i+1，e ]，T i+1，s For window start time, T i+1，e For window end time, T 1s Is the start time of the first window.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4748448A (en) * | 1982-06-29 | 1988-05-31 | Agence Spatiale Europeenne | Remote sensing apparatus for satellites |
| CN101833090A (en) * | 2010-03-12 | 2010-09-15 | 中国科学院遥感应用研究所 | Airborne ocean microwave remote sensing system utilizing signal sources of global satellite positioning system |
| CN106610491A (en) * | 2016-12-21 | 2017-05-03 | 广州市气象台 | Spaceborne SAR backscattering coefficient test method and device |
| KR102223991B1 (en) * | 2020-06-29 | 2021-03-08 | 세종대학교산학협력단 | Apparatus for detecting sea fog based on satellite observation in visible and near-infrared bands and method thereof |
| WO2021218424A1 (en) * | 2020-04-30 | 2021-11-04 | 江苏科技大学 | Rbf neural network-based method for sea surface wind speed inversion from marine radar image |
| CN113962525A (en) * | 2021-09-23 | 2022-01-21 | 北京市遥感信息研究所 | Remote sensing satellite task decision method based on configurable criteria |
| CN114488107A (en) * | 2022-04-13 | 2022-05-13 | 南方海洋科学与工程广东省实验室(广州) | Method and device for sea clutter space-time distribution and influence grading product manufacturing |
| CN115984505A (en) * | 2023-01-09 | 2023-04-18 | 北京环境特性研究所 | High-precision calculation method for average backscattering coefficient of sea clutter in high sea condition |
-
2023
- 2023-06-29 CN CN202310789563.2A patent/CN116976091B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4748448A (en) * | 1982-06-29 | 1988-05-31 | Agence Spatiale Europeenne | Remote sensing apparatus for satellites |
| CN101833090A (en) * | 2010-03-12 | 2010-09-15 | 中国科学院遥感应用研究所 | Airborne ocean microwave remote sensing system utilizing signal sources of global satellite positioning system |
| CN106610491A (en) * | 2016-12-21 | 2017-05-03 | 广州市气象台 | Spaceborne SAR backscattering coefficient test method and device |
| WO2021218424A1 (en) * | 2020-04-30 | 2021-11-04 | 江苏科技大学 | Rbf neural network-based method for sea surface wind speed inversion from marine radar image |
| KR102223991B1 (en) * | 2020-06-29 | 2021-03-08 | 세종대학교산학협력단 | Apparatus for detecting sea fog based on satellite observation in visible and near-infrared bands and method thereof |
| CN113962525A (en) * | 2021-09-23 | 2022-01-21 | 北京市遥感信息研究所 | Remote sensing satellite task decision method based on configurable criteria |
| CN114488107A (en) * | 2022-04-13 | 2022-05-13 | 南方海洋科学与工程广东省实验室(广州) | Method and device for sea clutter space-time distribution and influence grading product manufacturing |
| CN115984505A (en) * | 2023-01-09 | 2023-04-18 | 北京环境特性研究所 | High-precision calculation method for average backscattering coefficient of sea clutter in high sea condition |
Non-Patent Citations (2)
| Title |
|---|
| Vilhelm Gregers-Hansen ; Rashmi Mital ; .An Improved Empirical Model for Radar Sea Clutter Reflectivity.《: IEEE Transactions on Aerospace and Electronic Systems》.2012,第3512-3524页. * |
| 光学卫星对海上移动目标揭示能力分析;高越;李轩;温志军;;电子测量技术(第02期);第1-4页 * |
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