Land and sea clutter classification method and device for airborne radar echo data

Abstract

The present disclosure provides a land and sea clutter classification method and apparatus for airborne radar echo data. The method comprises: preprocessing echo data acquired from the airborne radar to obtain a range-azimuth data matrix, wherein the echo data is obtained by the airborne radar detecting and scanning a moving target in a land or sea area; determining a clutter amplitude curve based on the range-azimuth data matrix; determining a clutter standard deviation based on the clutter amplitude curve; and determining a category of the echo data corresponding to the clutter standard deviation based on a clutter intensity threshold and the clutter standard deviation, wherein the category includes at least one of the following: sea clutter, land and sea clutter, and land clutter.

Description

Technical Field

[0001] The present disclosure relates to the field of radar technology, and more particularly to a method for classifying land and sea clutter of airborne radar echo data and a device for classifying land and sea clutter of airborne radar echo data. Background Art

[0002] In wide-area moving target surveillance radars, clutter is a major factor affecting moving target detection and tracking. The strength of the clutter is closely related to the ground topography. Generally, clutter on the sea surface is relatively weak at wide viewing angles, while clutter on land, including islands, or at narrow viewing angles is very strong. Therefore, clutter suppression is essential for moving target detection.

[0003] Related technologies for classifying land and sea clutter include sea and land mapping and terrain detection. However, these methods are complex, have low accuracy, are not conducive to on-board processing, and cannot identify sea clutter that becomes stronger due to small angles of incidence. They also require external database resources, making them unsuitable for engineering applications. Summary of the Invention

[0004] In view of this, the present disclosure directly processes the echo signals acquired by the airborne radar to obtain a range-azimuth data matrix, determines a clutter amplitude curve and its clutter standard deviation based on the range-azimuth data matrix, and determines the category of the echo data by comparing the clutter intensity threshold with the clutter standard deviation. This avoids the problem of complex data processing caused by the use of auxiliary measurement clutter or external data. At the same time, the land and sea clutter classification method disclosed in the present disclosure has a small amount of calculation and good robustness. Therefore, the present disclosure provides a land and sea clutter classification method for airborne radar echo data and a land and sea clutter classification device for airborne radar echo data.

[0005] One aspect of an embodiment of the present disclosure provides a method for classifying land and sea clutter in airborne radar echo data, comprising:

[0006] Preprocessing the echo data obtained from the airborne radar to obtain a range-azimuth data matrix, wherein the echo data is obtained by the airborne radar detecting and scanning a moving target in a land or sea area;

[0007] Based on the above range-azimuth data matrix, the clutter amplitude curve is determined by range averaging;

[0008] According to the above clutter amplitude curve, determine the clutter standard deviation;

[0009] According to the clutter intensity threshold and the clutter standard deviation, a category of the echo data corresponding to the clutter standard deviation is determined, wherein the category includes at least one of the following: sea clutter, sea and land clutter, and land clutter.

[0010] According to an embodiment of the present disclosure, the echo data obtained from the airborne radar is preprocessed to obtain a range-azimuth data matrix, including:

[0011] performing range migration compensation on the echo data to obtain compensated echo data;

[0012] Performing a fast Fourier transform in azimuth direction on the compensated echo data to obtain the range-azimuth data matrix.

[0013] According to an embodiment of the present disclosure, before performing distance migration compensation, the method further includes:

[0014] The above echo data are processed using the correlation function method to obtain the Doppler center;

[0015] Based on the parameters of the Doppler center, pulse compression processing is performed on the Doppler center in the range direction to obtain compressed echo data.

[0016] According to an embodiment of the present disclosure, the clutter amplitude curve is determined by distance averaging based on the range-azimuth data matrix, including:

[0017] Determine the echo amplitude based on the above range-azimuth data matrix;

[0018] According to the echo amplitude, the clutter amplitude curve is determined by distance averaging.

[0019] According to an embodiment of the present disclosure, the echo data includes echo sub-data corresponding to different numbers of pulses; the echo amplitude includes multiple echo sub-amplitudes corresponding to different echo sub-data;

[0020] The clutter amplitude curve is determined by distance averaging based on the echo amplitude, including:

[0021] Performing logarithmic processing on each of the above echo sub-amplitudes to obtain multiple logarithmic results;

[0022] For each of the logarithmic results, differential detection is performed on the logarithmic result and the associated logarithmic result to obtain the clutter amplitude curve.

[0023] According to an embodiment of the present disclosure, performing differential detection on the logarithmic result and the associated logarithmic result to obtain the clutter amplitude curve includes:

[0024] Determine differential data based on the above logarithmic results and the associated logarithmic results;

[0025] Performing sequential detection on the differential data to determine transition point data in the differential data, wherein the transition point data is determined based on a transition point threshold;

[0026] The above-mentioned jump point data is eliminated from the above-mentioned differential data to obtain the above-mentioned clutter amplitude curve.

[0027] According to an embodiment of the present disclosure, the echo amplitude is shown in the first formula, the logarithmic result is shown in the second formula, the calculation of the differential data is shown in the third formula, and the clutter amplitude curve is shown in the fourth formula:

[0028]

[0029] S DB (m) = α × log β (S AZ (m)), m=1, 2, ..., N a

[0030] S D (i) = S DB (i+1)-S DB (i), i=1, 2, ..., N a -1

[0031]

[0032] Among them, N r Indicates the number of distance units, N a represents the number of pulses, S(m,n) is the amplitude of the element in the mth row and nth column of the range-azimuth data matrix S, η is the trip point threshold, and α and β are both constants.

[0033] According to an embodiment of the present disclosure, determining the clutter standard deviation based on the clutter amplitude curve includes:

[0034] The clutter standard deviation corresponding to the clutter amplitude curve is determined according to the amplitude value of each non-jump point in the clutter amplitude curve.

[0035] According to an embodiment of the present disclosure, the determining of the category of the echo data corresponding to the clutter standard deviation based on the clutter intensity threshold and the clutter standard deviation includes:

[0036] When the clutter standard deviation is less than or equal to the lower limit of the clutter intensity threshold, determining the echo data as the sea clutter;

[0037] When the clutter standard deviation is greater than the upper limit of the clutter intensity threshold, determining the echo data as the land clutter;

[0038] When the clutter standard deviation is between the upper limit and the lower limit of the clutter intensity threshold, the echo data is determined to be the sea and land clutter.

[0039] Another aspect of the embodiments of the present disclosure provides a land and sea clutter classification device for airborne radar echo data, comprising:

[0040] a preprocessing module for preprocessing the echo data obtained from the airborne radar to obtain a range-azimuth data matrix, wherein the echo data is obtained by detecting a moving target in a land or sea area by the airborne radar;

[0041] A first determining module is configured to determine a clutter amplitude curve by distance averaging based on the range-azimuth data matrix;

[0042] A second determining module is used to determine the clutter standard deviation based on the clutter amplitude curve;

[0043] A classification module is configured to determine a category of echo data corresponding to the clutter standard deviation based on a clutter intensity threshold and the clutter standard deviation, wherein the category includes at least one of the following: sea clutter, sea-land clutter, and land clutter.

[0044] According to an embodiment of the present disclosure, echo data acquired by an airborne radar is converted into a range-azimuth data matrix, the clutter standard deviation of the clutter amplitude curve is determined based on the range-azimuth data matrix, and the category of the echo data is determined based on a clutter intensity threshold. This avoids the problem of complex data processing caused by the use of auxiliary measurement clutter or external data. At the same time, the land and sea clutter classification method disclosed in the present disclosure has a small computational load, good robustness, and is easy to integrate into an airborne radar to realize the airborne radar's real-time automatic identification and classification of land and sea clutter. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0046] Figure 1 A flowchart of a method for classifying land and sea clutter of airborne radar echo data according to an embodiment of the present disclosure is schematically shown;

[0047] Figure 2 A schematic diagram schematically illustrates an echo amplitude curve according to an embodiment of the present disclosure;

[0048] Figure 3 A schematic diagram schematically shows a logarithmic clutter curve according to an embodiment of the present disclosure;

[0049] Figure 4 Schematically shows a curve diagram of the jump point data according to an embodiment of the present disclosure;

[0050] Figure 5A schematic diagram schematically illustrates a curve of trip point data according to an embodiment of the present disclosure; and

[0051] Figure 6 A block diagram of a device for classifying land and sea clutter of airborne radar echo data according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0052] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0053] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0054] 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 should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0055] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0056] Embodiments of the present disclosure provide a method and device for classifying land and sea clutter for airborne radar echo data. The method includes preprocessing echo data acquired from the airborne radar to obtain a range-azimuth data matrix, wherein the echo data is obtained by the airborne radar performing a reconnaissance scan of a moving target located in a land or sea area; determining a clutter amplitude curve based on the range-azimuth data matrix by distance averaging; determining a clutter standard deviation based on the clutter amplitude curve; and determining a category of the echo data corresponding to the clutter standard deviation based on a clutter intensity threshold and the clutter standard deviation, wherein the category includes at least one of the following: sea clutter, land and sea clutter, and land clutter.

[0057] Figure 1 A flowchart of a method for classifying land and sea clutter of airborne radar echo data according to an embodiment of the present disclosure is schematically shown.

[0058] like Figure 1 As shown, the method includes operations S101 to S104.

[0059] In operation S101, echo data acquired from an airborne radar is preprocessed to obtain a range-azimuth data matrix, wherein the echo data is obtained by the airborne radar detecting and scanning a moving target in a land or sea area.

[0060] In operation S102 , a clutter amplitude curve is determined by range averaging according to the range-azimuth data matrix.

[0061] In operation S103 , a clutter standard deviation is determined according to the clutter amplitude curve.

[0062] In operation S104 , a category of the echo data corresponding to the clutter standard deviation is determined according to the clutter intensity threshold and the clutter standard deviation, wherein the category includes at least one of the following: sea clutter, sea and land clutter, and land clutter.

[0063] According to an embodiment of the present disclosure, when an aircraft equipped with a radar detects or tracks a moving target in a land or sea area, the radar's operating mode can be a wide-area moving target monitoring mode, and its operating parameters can be: the band is X-band, and the number of pulses is 512.

[0064] According to the embodiments of the present disclosure, the clutter intensity threshold is determined by the staff based on a large amount of prior knowledge according to different geographical environments. In the embodiments of the present disclosure, the clutter intensity threshold is 1.4 to 1.6 for exemplary description, and does not limit the protection scope of the present disclosure to only the clutter intensity threshold within the above range.

[0065] According to an embodiment of the present disclosure, echo data received by a radar is preprocessed so that a corresponding range-azimuth data matrix is ​​generated based on the echo data. A clutter amplitude curve corresponding to the echo data can be determined based on the range-azimuth data matrix. The clutter standard deviation is determined based on the clutter amplitude curve, and the clutter standard deviation is compared with a preset clutter intensity threshold to obtain the category of the echo data.

[0066] According to an embodiment of the present disclosure, echo data acquired by an airborne radar is converted into a range-azimuth data matrix, the clutter standard deviation of the clutter amplitude curve is determined based on the range-azimuth data matrix, and the category of the echo data is determined based on a clutter intensity threshold. This avoids the complex data processing problems caused by the use of auxiliary measurement clutter or external data. At the same time, the land and sea clutter classification method disclosed in the present disclosure has a small computational load and good robustness. It has been integrated into an airborne radar to enable the airborne radar to automatically identify and classify land and sea clutter in real time.

[0067] According to an embodiment of the present disclosure, the echo data acquired from the airborne radar is preprocessed to obtain a range-azimuth data matrix, including the following operations:

[0068] Perform range migration compensation on the echo data to obtain compensated echo data. Perform azimuth fast Fourier transform on the compensated echo data to obtain a range-azimuth data matrix.

[0069] According to the embodiments of the present disclosure, since the airborne radar is in motion, it is necessary to perform range migration compensation on the echo data based on the speed of the airborne radar to obtain compensated echo data. This compensated echo data is then subjected to a fast Fourier transform in azimuth to obtain a Doppler beam sharpening (DBS) image, i.e., a range-azimuth data matrix. The clutter standard deviation of the clutter amplitude curve is determined based on this range-azimuth data matrix, and the category of the echo data is then determined based on this clutter standard deviation.

[0070] According to an embodiment of the present disclosure, after performing range migration compensation, azimuth parameter accumulation processing may be performed on the compensated echo data to obtain new echo data for fast Fourier transform.

[0071] According to an embodiment of the present disclosure, before performing distance migration compensation, the following operations are also included:

[0072] The echo data is processed using the correlation function method to obtain the Doppler center. Based on the parameters of the Doppler center, pulse compression processing is performed on the Doppler center in the range direction to obtain compressed echo data.

[0073] According to an embodiment of the present disclosure, the correlation function method is used to describe the degree of correlation between the values ​​of two signals at any two different times s and t.

[0074] According to embodiments of the present disclosure, the Doppler center of the echo data can be determined using a correlation function method. Based on the Doppler center parameters, pulse compression processing is then performed on the Doppler center in the range direction to obtain compressed echo data. Range migration compensation and a fast Fourier transform are then performed on the compressed echo data to obtain a range-azimuth data matrix.

[0075] Figure 2 The figure schematically shows a schematic diagram of an echo amplitude curve according to an embodiment of the present disclosure.

[0076] According to an embodiment of the present disclosure, determining a clutter amplitude curve by distance averaging based on a range-azimuth data matrix includes the following operations:

[0077] Determine the echo amplitude based on the range-azimuth data matrix. Based on the echo amplitude, determine the clutter amplitude curve by range averaging.

[0078] According to an embodiment of the present disclosure, by performing statistics on the range-azimuth data matrix, the echo amplitude shown in formula (1) can be obtained.

[0079]

[0080] Among them, N r Indicates the number of distance units, N a represents the number of pulses, and S(m,n) is the amplitude of the element in the mth row and nth column of the range-azimuth data matrix S.

[0081] According to an embodiment of the present disclosure, after determining the echo amplitude, the following can be generated: Figure 2 The echo amplitude curve shown is used to determine the clutter amplitude curve based on the echo amplitude through distance averaging.

[0082] According to an embodiment of the present disclosure, Figure 2 (a) is the echo amplitude curve of the echo data corresponding to land clutter. Figure 2 (b) is the echo amplitude curve of the echo data corresponding to sea clutter. Figure 2 (c) is the echo amplitude curve of the echo data corresponding to sea and land clutter.

[0083] It should be noted that Figures 2 to 5 (a), (b), and (c) are data curve graphs corresponding to land clutter, sea clutter, and sea and land clutter, respectively. The clutter types determined in the above figures are the types finally determined using the method disclosed in the present invention. Different types are described in the figures only to more clearly show the differences between different types of echo data.

[0084] Figure 3 The figure schematically shows a logarithmic clutter curve according to an embodiment of the present disclosure.

[0085] According to an embodiment of the present disclosure, the echo data includes echo sub-data corresponding to different numbers of pulses; and the echo amplitude includes multiple echo sub-amplitudes corresponding to different echo sub-data.

[0086] According to an embodiment of the present disclosure, determining a clutter amplitude curve by distance averaging based on the echo amplitude includes the following operations:

[0087] Logarithm processing is performed on each echo sub-amplitude to obtain multiple logarithmized results. For each logarithmized result, differential detection is performed on the logarithmized result and the associated logarithmized result to obtain a clutter amplitude curve.

[0088] According to the embodiment of the present disclosure, multiple echo sub-amplitudes of each clutter are logarithmically processed to obtain multiple logarithmic results. The multiple logarithmic results can be formed as follows: Figure 3 The clutter curve shown in FIG, wherein the logarithmic result is shown in formula (2).

[0089] S DB (m) = α × log β (S AZ (m)), m=1, 2, ..., N a (2)

[0090] Wherein, α and β are both preset constants, for example, α may be 20, and β may be 10.

[0091] According to an embodiment of the present disclosure, differential detection is performed on the logarithmic result and the associated logarithmic result to obtain a clutter amplitude curve, wherein the calculation formula of the differential detection is as shown in formula (3).

[0092] S D (i) = S DB (i+1)-S DB (i), i=1, 2, ..., N a -1 (3)

[0093] Figure 4 A schematic diagram of a curve of jump point data according to an embodiment of the present disclosure is schematically shown.

[0094] According to an embodiment of the present disclosure, performing differential detection on a logarithmic result and an associated logarithmic result to obtain a clutter amplitude curve includes the following operations:

[0095] Differential data is determined based on the logarithmic result and the associated logarithmic result. Sequential detection is performed on the differential data to determine transition point data within the differential data, where the transition point data is determined based on a transition point threshold. The transition point data is removed from the differential data to obtain a clutter amplitude curve.

[0096] According to an embodiment of the present disclosure, the jump point threshold is determined based on the moving target or static target in the radar usage environment, where the moving target can refer to a target that can move, such as an aircraft that needs to be tracked, and the static target can refer to an object such as a lighthouse that affects the clutter classification.

[0097] According to an embodiment of the present disclosure, according to the logarithmic result S DB (i) and the associated logarithmic result S DB (i+1), determine the differential data S D (i) After that, the differential data S D (i) Perform sequential detection to determine the jump point data in the differential data. The jump point data is as follows: Figure 4 shown.

[0098] According to the embodiment of the present disclosure, the jump point data is removed from the differential data to obtain the following Figure 4 The clutter amplitude curve shown in Figure 2 is as follows: The calculation of is shown in formula (4).

[0099]

[0100] Wherein, η is the trip point threshold, which is determined according to the actual use environment of the airborne radar.

[0101] Figure 5 A schematic diagram of a curve of jump point data according to an embodiment of the present disclosure is schematically shown.

[0102] According to an embodiment of the present disclosure, determining the clutter standard deviation according to the clutter amplitude curve includes:

[0103] According to the amplitude value of each non-jump point in the clutter amplitude curve, the clutter standard deviation corresponding to the clutter amplitude curve is determined.

[0104] According to an embodiment of the present disclosure, when determining the clutter amplitude curve, the clutter standard deviation S corresponding to the clutter amplitude curve is determined according to the amplitude value of each non-jump point in the clutter amplitude curve. std , clutter amplitude curve and clutter standard deviation S std like Figure 5 shown.

[0105] According to an embodiment of the present disclosure, determining the category of echo data corresponding to the clutter standard deviation according to the clutter intensity threshold and the clutter standard deviation includes the following operations:

[0106] If the clutter standard deviation is less than or equal to the lower limit of the clutter intensity threshold, the echo data is determined to be sea clutter. If the clutter standard deviation is greater than the upper limit of the clutter intensity threshold, the echo data is determined to be land clutter. If the clutter standard deviation is between the upper and lower limits of the clutter intensity threshold, the echo data is determined to be sea and land clutter.

[0107] In an exemplary embodiment, the clutter intensity threshold γ may be 1.4 to 1.6. stdWhen the clutter intensity threshold γ is less than or equal to the lower limit value 1.4, the echo data is determined to be sea clutter. std If the clutter intensity threshold value γ is greater than 1.6, the echo data is determined to be land clutter. std When the echo data is between the upper limit of 1.6 and the lower limit of 1.4 of the clutter intensity threshold, the echo data is determined to be sea and land clutter, as shown in Table 1.

[0108]

[0109]

[0110] Figure 6 A block diagram of a device for classifying land and sea clutter of airborne radar echo data according to an embodiment of the present disclosure is schematically shown.

[0111] like Figure 6 As shown, the land and sea clutter classification device 600 for airborne radar echo data includes a pre-processing module 610 , a first determination module 620 , a second determination module 630 and a classification module 640 .

[0112] The pre-processing module 610 is used to pre-process the echo data obtained from the airborne radar to obtain a range-azimuth data matrix, wherein the echo data is obtained by the airborne radar detecting and scanning a moving target in a land or sea area.

[0113] The first determining module 620 is configured to determine a clutter amplitude curve by distance averaging according to the range-azimuth data matrix.

[0114] The second determining module 630 is configured to determine the clutter standard deviation according to the clutter amplitude curve.

[0115] The classification module 640 is configured to determine a category of the echo data corresponding to the clutter standard deviation based on the clutter intensity threshold and the clutter standard deviation, wherein the category includes at least one of the following: sea clutter, sea and land clutter, and land clutter.

[0116] According to an embodiment of the present disclosure, echo data acquired by an airborne radar is converted into a range-azimuth data matrix, the clutter standard deviation of the clutter amplitude curve is determined based on the range-azimuth data matrix, and the category of the echo data is determined based on a clutter intensity threshold. This avoids the problem of complex data processing caused by the use of auxiliary measurement clutter or external data. At the same time, the land and sea clutter classification device of the present disclosure has a low computational complexity, good robustness, and is easy to integrate into an airborne radar to realize the airborne radar's real-time automatic identification and classification of land and sea clutter.

[0117] According to an embodiment of the present disclosure, the pre-processing module 610 includes a compensation sub-module and a transformation sub-module.

[0118] The compensation submodule is used to perform range migration compensation on the echo data to obtain compensated echo data.

[0119] The transformation submodule is used to perform azimuth fast Fourier transform on the compensated echo data to obtain a range-azimuth data matrix.

[0120] According to an embodiment of the present disclosure, the pre-processing module 610 further includes a processing sub-module and a compression sub-module.

[0121] The processing submodule is used to process the echo data using the correlation function method to obtain the Doppler center.

[0122] The compression submodule is used to perform pulse compression processing on the Doppler center in the range direction based on the parameters of the Doppler center to obtain compressed echo data.

[0123] According to an embodiment of the present disclosure, the first determining module 620 includes a first determining submodule and a second determining submodule.

[0124] The first determination submodule is configured to determine the echo amplitude according to the range-azimuth data matrix.

[0125] The second determining submodule is configured to determine a clutter amplitude curve by distance averaging according to the echo amplitude.

[0126] According to an embodiment of the present disclosure, the echo data includes echo sub-data corresponding to different numbers of pulses; and the echo amplitude includes multiple echo sub-amplitudes corresponding to different echo sub-data.

[0127] According to an embodiment of the present disclosure, the second determination submodule includes a logarithmic unit and a differential unit.

[0128] The logarithmic unit is used to perform logarithmic processing on each echo sub-amplitude to obtain multiple logarithmic results.

[0129] The differential unit is used for performing differential detection on each logarithmic result and an associated logarithmic result to obtain a clutter amplitude curve.

[0130] According to an embodiment of the present disclosure, the difference unit includes a first determination subunit, a sequence detection subunit, and a second determination subunit.

[0131] The first determining subunit is configured to determine differential data according to the logarithmic result and the associated logarithmic result.

[0132] The sequential detection subunit is used to perform sequential detection on the differential data to determine the transition point data in the differential data, wherein the transition point data is determined based on the transition point threshold.

[0133] The second determining subunit is used to remove the jump point data from the differential data to obtain a clutter amplitude curve.

[0134] According to an embodiment of the present disclosure, the second determining module 630 includes a third determining submodule.

[0135] The third determining submodule is configured to determine the clutter standard deviation corresponding to the clutter amplitude curve according to the amplitude value of each non-jump point in the clutter amplitude curve.

[0136] According to an embodiment of the present disclosure, the classification module 640 includes a fourth determination submodule, a fifth determination submodule, and a sixth determination submodule.

[0137] The fourth determining submodule is configured to determine the echo data as sea clutter when the clutter standard deviation is less than or equal to a lower limit of the clutter intensity threshold.

[0138] The fifth determination submodule is configured to determine the echo data as land clutter when the clutter standard deviation is greater than an upper limit of the clutter intensity threshold.

[0139] The sixth determination submodule is configured to determine the echo data as sea and land clutter when the clutter standard deviation is between an upper limit and a lower limit of the clutter intensity threshold.

[0140] According to the module of the embodiment of the present disclosure, submodule, unit, subunit, any multiple, or at least part of the function of any multiple thereof can be realized in one module. According to the module of the embodiment of the present disclosure, submodule, unit, subunit, any one or more can be split into multiple modules to realize. According to the module of the embodiment of the present disclosure, submodule, unit, subunit, any one or more can be at least partially realized as a hardware circuit, 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 a package, an application specific integrated circuit (ASIC), or can be realized by hardware or firmware of any other reasonable way of integrating or encapsulating the circuit, or by any one of the three implementation modes of software, hardware and firmware or by a suitable combination of any of them. Or, according to the module of the embodiment of the present disclosure, submodule, unit, subunit, one or more can be at least partially realized as a computer program module, which can execute the corresponding function when the computer program module is run.

[0141] For example, any multiple of the pre-processing module 610, the first determination module 620, the second determination module 630, and the classification module 640 can be combined into one module / unit / sub-unit for implementation, or any one of the modules / sub-modules / units / sub-units can be split into multiple modules / sub-modules / units / sub-units. Alternatively, at least part of the functions of one or more modules / sub-modules / units / sub-units in these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / sub-module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the pre-processing module 610, the first determination module 620, the second determination module 630, and the classification module 640 can be at least partially implemented as a hardware circuit, 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 a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the preprocessing module 610 , the first determination module 620 , the second determination module 630 and the classification module 640 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0142] It should be noted that the land and sea clutter classification device for airborne radar echo data in the embodiments of the present disclosure corresponds to the land and sea clutter classification method for airborne radar echo data in the embodiments of the present disclosure. For a description of the land and sea clutter classification device for airborne radar echo data, please refer to the land and sea clutter classification method for airborne radar echo data, and will not be repeated here.

[0143] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for classifying land and sea clutter from airborne radar echo data, comprising: Preprocessing the echo data acquired from the airborne radar to obtain a range-azimuth data matrix, wherein the echo data is acquired by the airborne radar performing a reconnaissance scan on a moving target in a land or sea area; determining a clutter amplitude curve by range averaging according to the range-azimuth data matrix; determining a clutter standard deviation according to the clutter amplitude curve; determining, according to a clutter intensity threshold and the clutter standard deviation, a category of echo data corresponding to the clutter standard deviation, wherein the category includes at least one of the following: sea clutter, sea and land clutter, and land clutter; The preprocessing of the echo data obtained from the airborne radar to obtain a range-azimuth data matrix includes: performing range migration compensation on the echo data to obtain compensated echo data; Perform azimuth parameter accumulation processing on the compensated echo data to obtain new echo data; Performing azimuth fast Fourier transform on the new echo data to obtain the range-azimuth data matrix.

2. The method according to claim 1, wherein Before distance migration compensation is made, it also includes: Processing the echo data using a correlation function method to obtain a Doppler center; Based on the parameters of the Doppler center, pulse compression processing is performed on the Doppler center in the range direction to obtain compressed echo data.

3. The method according to claim 1, wherein Determining the clutter amplitude curve by distance averaging according to the range-azimuth data matrix includes: determining an echo amplitude based on the range-azimuth data matrix; The clutter amplitude curve is determined by distance averaging according to the echo amplitude.

4. The method according to claim 3, wherein the echo data includes echo sub-data corresponding to different numbers of pulses; and the echo amplitude includes a plurality of echo sub-amplitudes corresponding to different echo sub-data; in, The step of determining the clutter amplitude curve by distance averaging according to the echo amplitude includes: Performing logarithmic processing on each of the echo sub-amplitudes to obtain a plurality of logarithmic results; For each of the logarithmic results, differential detection is performed on the logarithmic result and the associated logarithmic result to obtain the clutter amplitude curve.

5. The method according to claim 4, wherein The performing differential detection on the logarithmic result and the associated logarithmic result to obtain the clutter amplitude curve includes: Determining differential data based on the logarithmic result and the associated logarithmic result; performing sequential detection on the differential data to determine transition point data in the differential data, wherein the transition point data is determined based on a transition point threshold; The jump point data is removed from the differential data to obtain the clutter amplitude curve.

6. The method according to claim 4, wherein: The echo amplitude is shown in formula (1), the logarithmic result is shown in formula (2), the calculation of differential data is shown in formula (3), and the clutter amplitude curve is shown in formula (4): （1） （2） （3） （4） in, Indicates the number of distance units, Indicates the number of pulses, is the distance-azimuth data matrix The magnitude of the element in the mth row and nth column of is the trip point threshold, and α and β are preset constants.

7. The method according to any one of claims 1 to 6, wherein Determining the clutter standard deviation according to the clutter amplitude curve includes: The clutter standard deviation corresponding to the clutter amplitude curve is determined according to the amplitude value of each non-jump point in the clutter amplitude curve.

8. The method according to any one of claims 1 to 6, wherein The determining, based on the clutter intensity threshold and the clutter standard deviation, the category of the echo data corresponding to the clutter standard deviation includes: When the clutter standard deviation is less than or equal to the lower limit of the clutter intensity threshold, determining the echo data as the sea clutter; When the clutter standard deviation is greater than an upper limit of the clutter intensity threshold, determining the echo data as the land clutter; When the clutter standard deviation is between the upper limit and the lower limit of the clutter intensity threshold, the echo data is determined to be the sea and land clutter.

9. A device for classifying land and sea clutter from airborne radar echo data, comprising: a preprocessing module, configured to preprocess the echo data acquired from the airborne radar to obtain a range-azimuth data matrix, wherein the echo data is acquired by the airborne radar detecting a moving target in a land or sea area; A first determining module is configured to determine a clutter amplitude curve by distance averaging based on the range-azimuth data matrix; A second determining module is used to determine a clutter standard deviation according to the clutter amplitude curve; a classification module, configured to determine a category of echo data corresponding to the clutter standard deviation based on a clutter intensity threshold and the clutter standard deviation, wherein the category includes at least one of the following: sea clutter, sea and land clutter, and land clutter; Wherein, the preprocessing module includes: The compensation submodule is used to perform range migration compensation on the echo data to obtain compensated echo data; perform azimuth parameter accumulation processing on the compensated echo data to obtain new echo data; The transformation submodule is used to perform azimuth fast Fourier transform on the new echo data to obtain a range-azimuth data matrix.

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