Method for measuring surface roughness of celestial body based on surface echoes of surround radar

Abstract

The invention provides a celestial body surface roughness measuring method based on surrounding device radar surface echoes. The celestial body surface roughness measuring method can be applied to the technical field of celestial body roughness detection. The method includes, in response to input radar echo data, determining a coherence component of the radar echo data, the coherence component characterizing quasi-specular reflection energy having a fixed phase. The input roughness parameter is responded, a scatter diagram of the roughness parameter and the coherent component is constructed, the roughness parameter represents the surface roughness of the celestial body under any preset measurement scale, and the preset measurement scale is between the ten-meter scale and the hectometer scale. And performing linear fitting on the scatter diagram to obtain a fitting line slope. And determining a Hurst parameter according to the slope of the fitting line so as to measure the roughness of the surface of the celestial body. The invention further provides a celestial body surface roughness measuring device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] This disclosure relates to the field of celestial roughness detection technology, and more specifically to a method, apparatus, and electronic device for measuring celestial surface roughness based on orbiter radar surface echoes. Background Technology

[0002] Measuring the surface roughness of celestial bodies is a complex and challenging task, mainly due to the special environment and inaccessibility of celestial surfaces.

[0003] One approach is to use remote sensing technologies, such as radar or laser rangefinders. However, the accuracy of this method is limited due to the complexity and uncertainty of celestial surfaces. Furthermore, small-scale roughness can only be calculated using meter-level resolution elevation data obtained from stereo imaging with high-resolution cameras. However, the coverage of such high-resolution elevation data is very limited, restricting the study of small-scale roughness. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a method, apparatus and electronic equipment for measuring the surface roughness of celestial bodies based on the surface echo of an orbiter radar to improve the accuracy of celestial body surface roughness measurement, and to at least partially solve the above technical problems.

[0005] According to a first aspect of this disclosure, a method for measuring the surface roughness of an celestial body based on orbiter radar surface echoes is provided, comprising: determining a coherent component of the radar echo data in response to input radar echo data, the coherent component representing quasi-specular reflection energy with a fixed phase; constructing a scatter plot of the roughness parameter and the coherent component in response to input roughness parameters, the roughness parameter representing the surface roughness of the celestial body at an arbitrary preset measurement scale, the preset measurement scale being between the tens of meters and hundreds of meters; performing linear fitting on the scatter plot to obtain the slope of the fitted line; and determining the Hearst parameter based on the slope of the fitted line to measure the roughness of the celestial body surface.

[0006] According to embodiments of this disclosure, determining coherent components in response to input radar echo data includes: in response to input radar echo data within a preset range, fitting the radar echo data using a zero-difference K-distribution to obtain a first fitted curve. The coherent components are then determined based on the values ​​corresponding to the peak values ​​of the first fitted curve.

[0007] According to embodiments of this disclosure, the longitude and latitude ranges corresponding to the preset range area are both 0.25° to 0.5°.

[0008] According to embodiments of this disclosure, constructing a scatter plot of roughness parameters and coherent components in response to input roughness parameters includes: determining target root-mean-square height data corresponding to a preset range area in response to input root-mean-square height data, wherein the measurement scale of the root-mean-square height data is on the order of hundreds of meters; and constructing a scatter plot based on the target root-mean-square height data and coherent components.

[0009] According to embodiments of this disclosure, the method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes further includes: determining data points corresponding to the target root mean square height data and coherent components; filtering outliers from the data points according to a preset point density threshold to obtain target scatter points; and constructing a scatter plot based on the target scatter points.

[0010] According to embodiments of this disclosure, linear fitting of a scatter plot to obtain the slope of the fitted line includes: obtaining the median scatter point based on the median of the scatter points corresponding to the coherent components; and performing linear fitting of the scatter plot based on the median scatter point to obtain the slope of the fitted line.

[0011] According to embodiments of this disclosure, determining the Hearst parameter based on the slope of the fitted line includes: determining the scale ratio of the radar wavelength corresponding to the radar echo data to a preset measurement scale. The Hearst parameter is then determined based on the slope of the fitted line and the scale ratio.

[0012] According to embodiments of this disclosure, a method for measuring the surface roughness of celestial bodies based on the surface echo of an orbiter radar is applied to determine the dielectric constant of a material on the surface of a celestial body.

[0013] A second aspect of this disclosure provides a device for measuring the surface roughness of an celestial body based on the surface echo of an orbiter radar, comprising: a first determining module, configured to determine the coherent components of the radar echo data in response to input radar echo data, wherein the coherent components characterize quasi-specular reflection energy with a fixed phase; a constructing module, configured to construct a scatter plot of the roughness parameters and the coherent components in response to input roughness parameters, wherein the roughness parameters characterize the surface roughness of the celestial body at any preset measurement scale, the preset measurement scale being between the tens of meters and hundreds of meters; a fitting module, configured to perform linear fitting on the scatter plot to obtain the slope of the fitted line; and a second determining module, configured to determine the Hearst parameter based on the slope of the fitted line, in order to measure the roughness of the celestial body surface.

[0014] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the methods of any of the above embodiments.

[0015] Compared with the prior art, the celestial surface roughness measurement method, apparatus, and electronic equipment based on orbiter radar surface echoes provided in this disclosure have at least the following advantages:

[0016] (1) The method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes disclosed herein can, for example, obtain the surface roughness of the celestial body by fitting a scatter plot of the echo data from the orbiter subsurface sounding radar of the celestial body being measured and the roughness parameters under larger-scale measurement conditions obtained from a laser altimeter. This eliminates the need for meter-resolution elevation data to calculate roughness at different scales, thereby determining the Hearst parameter based on the trend of roughness increasing with scale. The method disclosed herein improves the measurement accuracy and efficiency of celestial body surface roughness.

[0017] (2) The celestial surface roughness measurement method based on the surface echo of the orbiter radar disclosed herein combines the fitting method of zero difference K distribution to process the radar echo data of the preset range area. While ensuring sufficient radar echo data, it reduces the amount of data processing and further improves the efficiency of celestial surface roughness measurement.

[0018] (3) The method for measuring the surface roughness of celestial bodies based on the surface echo of the orbiter radar disclosed herein specifically uses root mean square height data to construct a scatter plot with a scale of hundreds of meters. Considering the Hearst parameter, quantitative analysis becomes feasible, thus expanding the research on roughness analysis based on radar. Attached Figure Description

[0019] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0020] Figure 1 A flowchart illustrating a method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes according to an embodiment of the present disclosure is shown.

[0021] Figure 2 A flowchart illustrating a method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes according to another embodiment of the present disclosure is shown.

[0022] Figure 3 A schematic diagram illustrates the structure of a celestial body surface roughness measurement device based on orbiter radar surface echoes according to an embodiment of the present disclosure; and

[0023] Figure 4 A block diagram schematically illustrates an electronic device suitable for implementing a method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes, according to an embodiment of the present disclosure. Detailed Implementation

[0024] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0028] Figure 1 A flowchart illustrating a method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes according to an embodiment of the present disclosure is shown.

[0029] According to embodiments of this disclosure, such as Figure 1 As shown, the method for measuring the surface roughness of celestial bodies based on the surface echo of the orbiter radar in this embodiment includes, for example, operations S110 to S140, which can be executed by a computer program on corresponding computer hardware.

[0030] In operation S110, in response to the input radar echo data, the coherent component of the radar echo data is determined, and the coherent component characterizes the quasi-mirror reflection energy with a fixed phase.

[0031] In operation S120, in response to the input roughness parameters, a scatter plot of the roughness parameters and coherent components is constructed. The roughness parameters characterize the surface roughness of celestial bodies at any preset measurement scale, which is between the ten-meter and hundred-meter scales.

[0032] In operation S130, a linear fit is performed on the scatter plot to obtain the slope of the fitted line.

[0033] In operation S140, the Hearst parameter is determined based on the slope of the fitted line in order to measure the roughness of the celestial surface.

[0034] In some embodiments, taking Mars as an example, specific implementations of the celestial surface roughness measurement method based on orbiter radar surface echoes are as follows:

[0035] First, using radar probes in Mars orbit, radar signals are transmitted to the Martian surface, and the returned radar echo data is received. This data, which consists of signals reflected from the Martian surface, contains information about the surface's morphology and roughness.

[0036] Next, the radar echo data is processed to determine the coherent components. Coherent components refer to quasi-specular reflection energy with a fixed phase, typically originating from relatively smooth or flat areas of the Martian surface. By analyzing these coherent components, a preliminary understanding of the reflectivity of the Martian surface can be obtained.

[0037] Then, based on the known root-mean-square (RMS) height of the Martian surface, a scatter plot of the square of the RMS height and its coherent components is constructed. These roughness parameters (RMS height) are obtained in advance through other measurement methods (such as on-site measurements by the Mars rover, high-resolution image analysis, MOLA laser altimeter data, etc.), and they can characterize the roughness of the Martian surface at any preset measurement scale (such as the ten-meter or hundred-meter scale).

[0038] Subsequently, a linear fit was performed on the constructed scatter plot to obtain the slope of the fitted line. This slope reflects the linear relationship between the square of the root mean square height and the coherent components, which can help to further understand the distribution and variation of Martian surface roughness.

[0039] Finally, the Hearst parameter can be determined based on the slope of the fitted line. The Hearst parameter is an important indicator for describing surface roughness characteristics, and it can help to quantitatively measure and evaluate the roughness of the Martian surface. By comparing the Hearst parameters of different regions, we can understand the spatial distribution and differences in Martian surface roughness, which helps to further study Martian geological structure, climate environment, and potential signs of life.

[0040] It is understood that, in addition to Mars, the celestial surface roughness measurement method based on orbiter radar surface echoes in this disclosure can also be applied to other celestial bodies such as Venus, the Moon, and Jupiter's moons, where radar echo data can be obtained using orbiter subsurface sounding radars.

[0041] According to embodiments of this disclosure, coherent components are determined, for example, by operating S211 to S212 in response to input radar echo data.

[0042] In operation S211, in response to the input radar echo data of a preset range area, the radar echo data is fitted using a zero-difference K-distribution to obtain the first fitted curve.

[0043] In operation S212, the coherent components are determined based on the values ​​corresponding to the peak values ​​of the first fitted curve.

[0044] In some embodiments, radar echo data of a predetermined area on the Martian surface (such as a specific Martian landform region or terrain unit) is first collected. This data, transmitted and received by radar probes in Mars orbit, contains signal information reflected from the Martian surface.

[0045] Next, the collected radar echo data is fitted using the homodyned-K distribution statistical model. The homodyned-K distribution is a general statistical model suitable for analyzing ultrasonic echo envelope signals or similar radar echo data. It can effectively describe the statistical characteristics of the echo signal amplitude caused by rough surfaces.

[0046] During the fitting process, the parameters of the zero-difference K-distribution are adjusted to make the distribution curve match the actual radar echo data as closely as possible. This process may require multiple iterations and optimizations to obtain the best fitting result.

[0047] After fitting, a first fitted curve is obtained, which describes the statistical characteristics of the radar echo data. Then, the peak value of this fitted curve is found, and the square of the value corresponding to the peak value represents the magnitude of the coherent component.

[0048] The magnitude of the coherence component reflects the intensity of energy reflected by the quasi-specular surfaces of Mars with a fixed phase. A larger coherence component indicates that the surface of that region is relatively smooth and the reflected energy is more concentrated; a smaller coherence component indicates that the surface is relatively rough and the reflected energy is more dispersed.

[0049] This method allows for the determination of the coherent components of the Martian surface based on radar echo data, enabling the analysis of its roughness characteristics. This provides crucial information for studying Mars' geological structure, geomorphological features, and potential resource distribution.

[0050] According to embodiments of this disclosure, the longitude and latitude ranges corresponding to the preset range area are, for example, both 0.25° to 0.5°.

[0051] In some embodiments, a specific region on the Martian surface is first selected and divided into numerous sub-regions, with both longitude and latitude ranging from 0.25° to 0.5°. This range is chosen based on a preliminary understanding of the Martian surface features and exploration needs, ensuring that the selected regions are representative and small enough for detailed analysis.

[0052] Next, using radar probes in Mars orbit, radar signals were transmitted to this specific region, and the returned radar echo data was received. This data contained information about the reflections of the Martian surface in that area.

[0053] Then, these radar echo data are processed. A zero-difference K-distribution is used to fit the radar echo amplitude distribution in each sub-region to obtain a first fitting curve. The zero-difference K-distribution is a statistical model suitable for analyzing ultrasonic echo envelope signals, and it can also be applied to the statistical characteristic analysis of radar echo data. By fitting the data, patterns and characteristics can be extracted.

[0054] Next, the coherent components can be determined based on the values ​​corresponding to the peak values ​​of the first fitted curve. Coherent components refer to quasi-specular reflection energy with a fixed phase, typically originating from relatively smooth or flat regions of the Martian surface. By analyzing the peak values ​​of the fitted curve, these coherent components can be identified, further revealing the reflective properties of the Martian surface.

[0055] This method allows for the determination of the coherent components of the Martian surface within a specific latitude and longitude range, enabling further analysis of the region's roughness. This approach not only improves the accuracy and reliability of the analysis but also contributes to a deeper understanding of the Martian surface's geological structure, geomorphological features, and potential scientific value.

[0056] It should be noted that the process of determining coherent components can be affected by various factors, such as the detector's attitude, operating orbit altitude, and solar altitude angle. Therefore, in practical applications, appropriate preprocessing and correction of the data may be necessary to improve the accuracy and reliability of the analysis.

[0057] According to embodiments of this disclosure, for example, a scatter plot of roughness parameters and coherent components is constructed in response to input roughness parameters by operating S321 to S322.

[0058] In operation S321, in response to the input root mean square height data, the target root mean square height data corresponding to the preset range area is determined, and the measurement scale of the root mean square height data is on the order of hundreds of meters.

[0059] In operation S322, a scatter plot is constructed based on the target root mean square height data and coherent components.

[0060] In some embodiments, taking Mars as an example, the scatter plot of roughness parameters and coherent components can be constructed based on root mean square (RMS) height data, which is an important parameter for measuring the surface roughness of Mars. The measurement scale of RMS height data can be, for example, on the order of hundreds of meters, because at this scale, enough data points can be obtained to construct the scatter plot while maintaining an effective description of the surface roughness of Mars.

[0061] Preferably, the measurement scale for the root mean square height data can be, for example, 10 to 200 meters, such as 15 meters, 75 meters, 100 meters, etc.

[0062] Next, a specific region on the Martian surface is selected and divided into numerous sub-regions, whose latitude and longitude ranges correspond to the preset range mentioned in the above embodiments. Then, using on-the-ground measurement equipment such as probes or rovers in Mars orbit, the root mean square altitude data of this region is obtained. This data reflects the roughness distribution of the Martian surface within this region.

[0063] Simultaneously, the coherent component of the region is determined according to the method mentioned in the above embodiments. The coherent component is obtained by analyzing radar echo data, and it represents the quasi-specular reflection energy with a fixed phase on the Martian surface.

[0064] Once you have the root mean square height data and the coherent components, you can construct a scatter plot. Scatter plots can be constructed using data analysis software or programming languages. In a scatter plot, each data point represents a measurement location, with the x-axis representing the square of the root mean square height and the y-axis representing the corresponding coherent component.

[0065] By observing the distribution of the scatter plot, we can gain a preliminary understanding of the relationship between Martian surface roughness and coherent components. If the data points in the scatter plot show a clear linear relationship, it indicates a strong correlation between the root mean square height and the coherent components, which is helpful for further analysis of the physical properties and geological structure of the Martian surface.

[0066] Furthermore, by performing linear fitting or other statistical analysis methods on the scatter plot, parameters such as the slope and intercept of the fitted line can be obtained. These parameters can be used to quantify the relationship between Martian surface roughness and coherent components, and provide important reference for subsequent Mars exploration and research.

[0067] It should be noted that due to the complexity and unique characteristics of the Martian environment, the construction of scatter plots may be affected by various factors, such as data noise and measurement errors. Therefore, in practical applications, a series of measures can be taken to ensure the accuracy and reliability of the data, such as data preprocessing and filtering, and selecting appropriate measurement equipment and parameters.

[0068] By combining root mean square height data and coherent components to construct a scatter plot, we can gain a deeper understanding of the roughness distribution on the Martian surface and provide strong support for subsequent Mars exploration and research.

[0069] According to embodiments of the present disclosure, the method for measuring the surface roughness of celestial bodies based on the surface echo of an orbiter radar in this embodiment further includes, for example, operations S410 to S430.

[0070] In operation S410, the data points corresponding to the target root mean square height data and coherent components are determined.

[0071] In operation S420, outlier filtering is performed on data points based on a preset point density threshold to obtain the target scatter plot.

[0072] When operating the S430, a scatter plot is constructed based on the target scatter points.

[0073] In some embodiments, taking Mars as an example, the step of constructing a scatter plot in the method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes can be specifically combined with the following methods:

[0074] First, root-mean-square height data and coherent components for specific regions of the Martian surface have been acquired. These data points provide a preliminary reflection of the surface roughness of Mars. Next, it is necessary to identify target data points within these data points. Target data points refer to those effective data points that accurately reflect the relationship between Martian surface roughness and coherent components.

[0075] After obtaining the target data points, a scatter plot is constructed. The scatter plot can be constructed using data analysis software or a programming language. During the construction process, the target data points need to be plotted according to the correspondence between the root mean square height data and the coherent components. Each data point in the scatter plot represents a specific measurement location, with its horizontal axis corresponding to the square of the root mean square height and its vertical axis corresponding to the coherent components.

[0076] After constructing the scatter plot, further filtering and processing of the data points are necessary. Due to factors such as measurement errors and noise interference, some outliers may exist in the data, which can interfere with subsequent analysis. Therefore, it is necessary to filter outliers based on a preset point density threshold. The point density threshold is set according to the data distribution and actual needs, and is used to determine whether a data point is an outlier. By filtering out these outliers, more accurate and reliable target scatter plots can be obtained.

[0077] By observing the constructed scatter plot, the relationship between Martian surface roughness and coherent components can be clearly seen. If the target points in the scatter plot show a clear clustering trend or a linear relationship, it indicates a strong correlation between the square of the root mean square height and the coherent components.

[0078] By analyzing and interpreting scatter plots, we can gain a deeper understanding of the physical properties and geological structure of the Martian surface, providing important reference data for subsequent Mars exploration and research. Simultaneously, this method can also be applied to the measurement and analysis of surface roughness on other celestial bodies, helping to gain a more profound understanding of the mysteries of the universe.

[0079] According to embodiments of this disclosure, for example, a linear fit is performed on a scatter plot by operations S531 to S532 to obtain the slope of the fitted line.

[0080] In operation S531, the median scatter point is obtained based on the median of the scatter points corresponding to the coherent components.

[0081] In operation S532, a linear fit is performed on the scatter plot based on the median scatter point to obtain the slope of the fitted line.

[0082] In some embodiments, taking Mars as an example, after constructing the scatter plot, in order to analyze the relationship between Martian surface roughness and coherent components in greater depth, it is necessary to perform linear fitting on the scatter plot to obtain the slope of the fitted line. This slope can quantify the strength of the linear relationship between the root mean square height (representing roughness) and the coherent components.

[0083] First, determine the median of the scatter points corresponding to the coherent components. The median is a statistic that represents the value in the middle of a dataset. Here, we calculate the median of the coherent components of all points in the scatter plot and find the corresponding root mean square height data; this point is called the median scatter point.

[0084] Determining the median scatter plot helps avoid the influence of extreme values ​​on linear fit, because the median is more representative of the central tendency of the data than the mean, especially when the data is unevenly distributed or there are outliers.

[0085] Next, using the median scatter plot as a reference point, a linear fit is performed on the entire scatter plot. Linear fitting is a mathematical method that finds an optimal straight line by minimizing the perpendicular distance (i.e., the residual) between the data points and the fitted line. This line best describes the overall distribution trend of the data points in the scatter plot.

[0086] When performing linear fitting, mathematical methods or statistical software are typically used to automatically calculate the parameters of the fitted line, including the slope and intercept. The slope represents the rate of change of the coherent component with the square of the root mean square height, reflecting the strength of the linear relationship between Martian surface roughness and the coherent component.

[0087] By performing linear fitting, a fitted line can be obtained, and its slope can be calculated. This slope value can help quantify the linear relationship between Martian surface roughness and coherent components, thereby providing a deeper understanding of the physical properties and geological structure of the Martian surface.

[0088] It should be noted that linear fitting is only an approximate method for describing data relationships, and it may not fully capture all the complexity and nonlinear characteristics of the data. Therefore, when interpreting fitting results, its applicability and limitations need to be carefully considered, and a comprehensive judgment should be made in conjunction with other analytical methods.

[0089] Through the above steps, the slope of the fitted line was obtained. This slope provides important information about the relationship between Martian surface roughness and coherent components, providing valuable reference for subsequent Mars exploration and research.

[0090] According to embodiments of this disclosure, for example, the Hearst parameter is determined based on the slope of the fitted line by operating S641 to S642.

[0091] In operation S641, the ratio of the radar wavelength corresponding to the radar echo data to the preset measurement scale is determined.

[0092] In operation S642, the Hearst parameters are determined based on the slope of the fitted line and the scale ratio.

[0093] In some embodiments, taking Mars as an example, after obtaining the slope of the fitted line, the Hurst parameter can be further determined. The Hurst parameter is an important indicator for describing surface roughness characteristics, which can help to gain a deeper understanding of the physical properties and geological structure of the Martian surface.

[0094] First, determine the radar wavelength corresponding to the radar echo data. The radar wavelength is the wavelength used by a radar system to transmit and receive electromagnetic waves; it determines the radar's detection capability and resolution. For Mars exploration missions, selecting an appropriate radar wavelength is crucial for achieving optimal detection results.

[0095] Simultaneously, the specific values ​​of the preset measurement scale are also required. The preset measurement scale is the unit of measurement or range used when measuring the surface roughness of celestial bodies; it determines the level of precision in the observation and analysis of surface roughness. In Mars exploration, the preset measurement scale is typically determined based on the mission requirements and the performance of the radar system.

[0096] Next, the scale ratio of the radar wavelength to the preset measurement scale is calculated. The scale ratio is a dimensionless number that represents the relative magnitude between the radar wavelength and the measurement scale. By calculating the scale ratio, we can understand the proportion of the radar wavelength relative to the measurement scale, which is of great significance for subsequently determining the Hearst parameters.

[0097] After obtaining the slope and scale ratio of the fitted line, the Hearst parameter can be determined based on a certain mathematical model or empirical formula. There is a functional relationship between the Hearst parameter and the slope and scale ratio of the fitted line; by substituting specific values, an estimate of the Hearst parameter can be obtained.

[0098] By following the steps above, the Hearst parameters can be determined based on the slope of the fitted line, thus providing a more comprehensive understanding of the roughness characteristics of the Martian surface. The Hearst parameters provide crucial information about the physical properties of the Martian surface, contributing to further exploration of Martian geological structure, climate, and potential signs of life.

[0099] Figure 2 A flowchart illustrating a method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes according to another embodiment of this disclosure is shown.

[0100] According to embodiments of this disclosure, such as Figure 2 As shown, the method for measuring the surface roughness of celestial bodies based on the surface echo of the orbiter radar in this embodiment includes, for example, steps 1 to 3.

[0101] Step 1: Estimate the coherent components based on the statistical distribution of radar surface echo amplitude.

[0102] Since the coherent component has a fixed phase determined by the distance from the radar to the surface, while the phase of the incoherent component generated by scattering is random, the echo amplitude obtained by adding the two together has certain statistical characteristics.

[0103] The inventors discovered that the radar echo amplitude distribution generated by a smooth surface follows a Rice distribution, which can estimate the coherent components. In contrast, the radar echo amplitude distribution generated by a rough surface (non-static surface) follows a K-distribution, whose parameters primarily reflect the incoherent components. The Rice and K-distributions can be combined into a zero-difference K-distribution. By fitting the surface echo amplitude distribution using the zero-difference K-distribution, both coherent and incoherent components can be estimated simultaneously. The expression for the zero-difference K-distribution is:

[0104]

[0105] Where A is the signal amplitude, a is a constant amplitude superimposed on the random walk, controlling the peak position of the zero-difference K-distribution, s is the scale parameter of the HK distribution, representing the scattering intensity of a single scatterer and controlling the peak width, μ is the clustering parameter used to simulate the two-dimensional random walk related to scattering, u is the integral variable, J0(x) is a class of zero-order Bayesian equations, and the coherence component P c With incoherent component P n The parameters of the fitted zero-difference K-distribution can be estimated as follows:

[0106] P c =a 2 (2)

[0107] P n =2s 2 μ (3)

[0108] Fitting the distribution of radar surface echo amplitude requires a sufficient amount of SHARAD radar data to demonstrate clear statistical distribution characteristics. Furthermore, the number of points involved in the scatter linear fitting of the coherent component versus the square of the root mean square height in step 2 must be considered. Therefore, each echo amplitude distribution fitting uses radar data within a 0.25° × 0.25° range on the Martian surface, and this range can be adjusted according to actual conditions.

[0109] Step 2: Compare with the root mean square height at a larger scale (such as hundreds of meters) and draw a scatter plot. Then, perform linear fitting based on the scatter distribution to obtain the slope of the coherent component as a function of the square of the root mean square height.

[0110] Currently, the smallest horizontally determined root mean square (RMS) height of the entire fire system is the RMS height retrieved from the pulse width of the laser received by the MOLA laser altimeter. Its scale is considered equal to the laser irradiation footprint of the MOLA (75 meters), but larger than the SHARAD radar wavelength of 15 meters. This RMS data is divided according to the data range used in each fitting of the SHARAD radar surface echo amplitude distribution (e.g., 0.25° × 0.25°), and the average value is taken.

[0111] After obtaining the average root mean square height data and SHARAD coherent echo component intensity, both of which are n*m ​​in size, a scatter plot is drawn with the square of the average root mean square height data as the horizontal axis and the SHARAD coherent echo component intensity as the vertical axis.

[0112] Due to issues such as data quality and uneven distribution of sampling points, there may be many outliers. Therefore, the point density of the obtained scatter plot can be calculated, and a point density threshold can be set to filter out outliers and obtain the target scatter plot.

[0113] To fit the target scatter points, we can calculate the median of these scatter points along the vertical axis (coherent components) at certain intervals. After obtaining the median scatter points, we can perform a linear fit on them and obtain the slope l of the fitting result.

[0114] Step 3: Invert the roughness scale parameters (Hurst parameters) of the region based on the slope.

[0115] The expression for the coherent components of radar surface echoes is:

[0116]

[0117] in, The Fresnel reflection coefficient is the factor by which the dielectric constant ε of the surface material affects the intensity of the echo. t Total transmit power, G is antenna gain, h is orbital altitude, k is wave number. Roughness influence factor is: σ h The root-mean-square height is the radar wavelength scale, which is 15 meters in the application scenario of SHARAD radar. Treating elements independent of the surface dielectric constant and roughness as constants, the expression is converted to decibel form:

[0118]

[0119] Right now,

[0120]

[0121] The coherent component of the radar echo in decibels can be considered as the square of the root-mean-square height σ. h 2 The linear function has a slope of -3.048. When the surface satisfies the self-affine assumption, the root-mean-square height increases with scale.

[0122]

[0123] Where H is the Hearst parameter, σ L1 σ is the root mean square height measured on a radar wavelength scale. L2 This is the root mean square (RMS) height measured at the scale of the laser-irradiated footprint of MOLA. When using the MMS elevation derived from the MOLA pulse width with SHARAD radar data, L1 is, for example, 15 meters, and L0 is, for example, 75 meters. Substituting the expression for the MMS height increasing with scale into the decibel form of the radar coherent component expression:

[0124]

[0125] Right now,

[0126]

[0127] It can be observed that the coherent components of the SHARAD radar also satisfy a linear function of the root mean square height squared at a 75-meter scale, but the slope is no longer a constant (-3.048), but rather a function of the scale ratio (0.2) and the Hearst parameter (H). Therefore, the Hearst parameter H can be inverted using the slope l fitted in step 2:

[0128]

[0129] Understandably, this method can also be used in roughness analysis of other radars, and can be combined with roughness data at different scales, simply by changing the scale ratio.

[0130] According to embodiments of this disclosure, a method for measuring the surface roughness of celestial bodies based on the surface echo of an orbiter radar is applied to determine the dielectric constant of a material on the surface of a celestial body.

[0131] In some embodiments, the celestial surface roughness measurement method based on orbiter radar surface echoes not only helps in understanding the physical properties of the surface, but can also be further applied to determining the dielectric constant of celestial surface materials and selecting landing zones. The dielectric constant is a physical quantity that measures the response of a material to an electric field; it reflects the charge distribution within the material and the propagation characteristics of the electric field. Understanding the dielectric constant allows for a deeper understanding of the material composition and properties of celestial surfaces.

[0132] Taking Mars as an example, the composition and properties of the Martian surface are crucial for understanding its climate, geological activity, and potential for life. By employing a surface roughness measurement method based on orbiter radar surface echoes, we can obtain roughness data of the Martian surface and further analyze the relationship between these data and the dielectric constant of the Martian surface materials.

[0133] First, radar or other remote sensing equipment is used to acquire radar echo data of the Martian surface. This data contains information on the reflection and scattering of Martian surface materials, which is closely related to the surface roughness.

[0134] Next, roughness parameters of the Martian surface can be obtained using contact or non-contact measurement techniques. These parameters describe the characteristics and distribution of the Martian surface's microstructure, such as the root mean square height.

[0135] Then, by combining the known radar wavelength and measurement scale, the scale ratio can be calculated. This ratio reflects the relative relationship between the radar wavelength and the measurement scale of Martian surface roughness, and is crucial for subsequent analysis.

[0136] With the roughness parameter and scale ratio, the Hearst parameter can be further determined. There is a certain relationship between the Hearst parameter and the dielectric constant; through analysis and modeling, the dielectric constant of Martian surface materials can be estimated using the Hearst parameter.

[0137] Finally, by comparing experimental data, theoretical models, and previous research results, the estimated value of the dielectric constant can be verified and corrected, thereby obtaining more accurate and reliable information on the dielectric constant of Martian surface materials.

[0138] It should be noted that due to the complexity and diversity of the Martian surface, various challenges and uncertainties may be encountered in practical applications. Therefore, when determining the dielectric constant using celestial surface roughness measurement methods based on orbiter radar surface echoes, multiple factors need to be comprehensively considered, and appropriate measures should be taken to improve measurement accuracy and reliability.

[0139] Based on the above-described method for measuring celestial surface roughness using orbiter radar surface echoes, this disclosure also provides a device for measuring celestial surface roughness using orbiter radar surface echoes. The following will be combined with... Figure 3 This invention provides a detailed description of the celestial surface roughness measurement device based on the surface echo of the orbiter radar.

[0140] Figure 3 A schematic block diagram of a celestial surface roughness measurement device based on orbiter radar surface echoes according to an embodiment of the present disclosure is shown.

[0141] like Figure 3 As shown, the celestial surface roughness measurement device 300 based on the surface echo of the orbiter radar in this embodiment includes, for example, a first determination module 310, a construction module 320, a fitting module 330, and a second determination module 340.

[0142] The first determining module 310 is used to determine the coherent component of the radar echo data in response to the input radar echo data. The coherent component represents the quasi-specular reflection energy with a fixed phase. In one embodiment, the first determining module 310 can be used to perform the operation S110 described above, which will not be repeated here.

[0143] The construction module 320 is used to construct a scatter plot of the roughness parameters and coherent components in response to the input roughness parameters. The roughness parameters characterize the surface roughness of a celestial body at any preset measurement scale, which is between the tens of meters and hundreds of meters. In one embodiment, the construction module 320 can be used to perform the operation S120 described above, which will not be repeated here.

[0144] The fitting module 330 is used to perform linear fitting on the scatter plot to obtain the slope of the fitted line. In one embodiment, the fitting module 330 can be used to perform the operation S130 described above, which will not be repeated here.

[0145] The second determining module 340 is used to determine the Hearst parameter based on the slope of the fitted line, in order to measure the roughness of the celestial surface. In one embodiment, the second determining module 340 may be used to perform the operation S140 described above, which will not be repeated here.

[0146] According to embodiments of this disclosure, any plurality of modules among the first determining module 310, the building module 320, the fitting module 330, and the second determining module 340 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the first determining module 310, the building module 320, the fitting module 330, and the second determining module 340 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the first determining module 310, the construction module 320, the fitting module 330, and the second determining module 340 may be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0147] Figure 4 A block diagram schematically illustrates an electronic device suitable for implementing a method for measuring the surface roughness of celestial bodies based on orbiter radar surface echoes, according to an embodiment of the present disclosure.

[0148] like Figure 4As shown, an electronic device 400 according to an embodiment of the present disclosure includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage portion 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0149] RAM 403 stores various programs and data required for the operation of electronic device 400. Processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Processor 401 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 402 and / or RAM 403. It should be noted that the programs may also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0150] According to embodiments of this disclosure, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to a bus 404. The electronic device 400 may also include one or more of the following components connected to the I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0151] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the celestial surface roughness measurement method based on orbiter radar surface echoes according to embodiments of this disclosure.

[0152] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 402 and / or RAM 403 and / or one or more memories other than ROM 402 and RAM 403 described above.

[0153] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the celestial surface roughness measurement method based on orbiter radar surface echoes provided in embodiments of this disclosure.

[0154] When the computer program is executed by the processor 401, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0155] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 409, and / or installed from removable medium 411. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0156] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by processor 401, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0157] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0158] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0159] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0160] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for measuring the surface roughness of celestial bodies based on the surface echo of an orbiter radar, characterized in that, include: In response to input radar echo data, the coherent component of the radar echo data is determined, the coherent component representing the quasi-specular reflection energy with a fixed phase; In response to the input roughness parameter, a scatter plot of the roughness parameter and the coherent component is constructed. The roughness parameter represents the surface roughness of a celestial body at any preset measurement scale, which is between the ten-meter and hundred-meter scales. Perform a linear fit on the scatter plot to obtain the slope of the fitted line; as well as The Hearst parameter is determined based on the slope of the fitted line in order to measure the roughness of the celestial body's surface.

2. The method according to claim 1, characterized in that, The determination of coherent components in response to input radar echo data includes: In response to radar echo data within a preset input range, the radar echo data is fitted using a zero-difference K-distribution to obtain a first fitting curve; The coherent component is determined based on the value corresponding to the peak value of the first fitted curve.

3. The method according to claim 2, characterized in that, The longitude and latitude ranges corresponding to the preset range area are both 0.25° to 0.5°.

4. The method according to claim 2, characterized in that, The step of constructing a scatter plot of the roughness parameter and the coherent component in response to the input roughness parameter includes: In response to the input root mean square height data, a target root mean square height data corresponding to the preset range area is determined, wherein the measurement scale of the root mean square height data is on the order of hundreds of meters; The scatter plot is constructed based on the target root mean square height data and the coherent components.

5. The method according to claim 4, characterized in that, The method further includes: Determine the data points corresponding to the target root mean square height data and the coherent components; Outlier filtering is performed on the data points according to a preset point density threshold to obtain target scattered points; and Based on the target scatter points, construct the scatter plot.

6. The method according to claim 1, characterized in that, The linear fitting of the scatter plot to obtain the slope of the fitted line includes: The median scatter points are obtained based on the median of the scatter points corresponding to the coherent components. The slope of the fitted line is obtained by linearly fitting the scatter plot based on the median scatter point.

7. The method according to any one of claims 1 to 6, characterized in that, The step of determining the Hearst parameters based on the slope of the fitted line includes: Determine the ratio of the radar wavelength corresponding to the radar echo data to the preset measurement scale; The Hearst parameter is determined based on the slope of the fitted line and the scale ratio.

8. The method according to claim 1, characterized in that, The method is applied to determine the dielectric constant of materials on the surface of celestial bodies.

9. A device for measuring the surface roughness of celestial bodies based on the surface echo of an orbiter radar, characterized in that, include: The first determining module is used to determine the coherent component of the radar echo data in response to the input radar echo data, wherein the coherent component represents the quasi-specular reflection energy with a fixed phase. A construction module is used to construct a scatter plot of the roughness parameter and the coherent component in response to the input roughness parameter, wherein the roughness parameter represents the surface roughness of a celestial body at any preset measurement scale, and the preset measurement scale is between the ten-meter and hundred-meter scales. The fitting module is used to perform linear fitting on the scatter plot to obtain the slope of the fitted line. as well as The second determining module is used to determine the Hearst parameter based on the slope of the fitted line in order to measure the roughness of the celestial body surface.

10. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 8.

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