A method for removing strong sidelobe clutter of airborne multi-faceted array radar

Abstract

The present application belongs to the technical field of airborne distributed array signal processing, and particularly relates to a method for removing strong sidelobe clutter of an airborne multi-faceted array radar; according to the layout of the multi-faceted array radar, the present application simulates the directional diagram of the multi-faceted array in a large-angle direction in a sinusoidal space; then the relative position of the strong sidelobe and the main lobe and the range of the strong sidelobe are determined in the sinusoidal space; finally, the sinusoidal space position of the strong sidelobe is projected to the range-Doppler position in the radar spectrum, so as to remove the detected clutter false alarm. The present application can quickly and accurately estimate the position of the strong sidelobe clutter in the radar spectrum, effectively remove the false alarm targets caused by the strong sidelobe clutter, and improve the target detection performance.

Description

Technical Field

[0001] This invention belongs to the field of airborne distributed array signal processing technology, specifically relating to a method for eliminating strong sidelobe clutter in airborne multi-faceted array radar. Background Technology

[0002] Airborne multi-array radars achieve omnidirectional airspace detection by distributing multiple radar arrays around the fuselage. When multiple radar arrays on the same plane are coherently combined, the distributed layout can generate severe sidelobes and even grating lobes in the radiation pattern, resulting in high-power sidelobe clutter. This strong sidelobe clutter can degrade target detection performance or cause false alarms, severely impacting radar performance.

[0003] Existing airborne active phased array radars primarily achieve low sidelobes in their radiation patterns through array weighting, thereby reducing sidelobe power. Commonly used low sidelobe methods include traditional Taylor weighting, Chebyshev weighting, and optimized weights obtained through optimization algorithms. On the other hand, guard channels can be used to conceal sidelobe clutter.

[0004] Existing sidelobe clutter suppression methods mainly suffer from the following problems: 1) In multi-array radar layouts, the classic low sidelobe weighting fails; 2) When the radiation pattern sidelobes are high, the sidelobe concealment of the protection channel will fail, resulting in false alarms; 3) The low sidelobe weights obtained by intelligent optimization algorithms are limited by the phase shifter and attenuator, which limits their effectiveness in practical engineering and easily causes main lobe distortion, thus affecting detection power and angle measurement performance. Summary of the Invention

[0005] In view of this, the present invention provides a method for eliminating strong sidelobe clutter in airborne multi-array radar, which can quickly and accurately estimate the position of strong sidelobe clutter in the radar spectrum, effectively eliminate false alarm targets caused by strong sidelobe clutter, and improve target detection performance.

[0006] To achieve the above-mentioned technical objectives, the specific technical solution adopted by the present invention is as follows:

[0007] A method for eliminating strong sidelobe clutter in an airborne multi-array radar includes the following steps:

[0008] S101: Based on the array layout of the multi-faceted radar, calculate the radiation pattern of the multi-faceted array in sinusoidal space at a preset angle;

[0009] S102: Obtain the angle information of the strong sidelobe in the radiation pattern based on the radiation pattern;

[0010] S103: Based on the angle information, convert the strong sidelobes into the spectrum;

[0011] S104: Based on the Doppler information and distance information of the strong sidelobes in the spectrum, remove the strong sidelobes;

[0012] Wherein: 102 further includes: obtaining the relative position between the strong sidelobe and the main lobe;

[0013] When the preset angle in S101 is changed, the angle information is directly obtained based on the relative position and the changed preset angle.

[0014] Furthermore, the method for calculating the radiation pattern is as follows:

[0015] Based on the array structure of the multifaceted array, calculate the array's preset angular pattern F(u,v) in sinusoidal space:

[0016] F(u,v)=(a(u set ,v set )) H *a(u,v)

[0017] a(u,v) is the signal steering vector, and its expression is:

[0018]

[0019] Where λ is the wavelength;

[0020] pos_array represents the coordinates of the facets of the multifaceted array;

[0021] u set =sin(phi) set )*cos(theta set );

[0022] v set =sin(theta) set );

[0023] phi set and theta set These are the preset azimuth and elevation angles of the multi-faceted radar system;

[0024] u = sin(phi) * cos(theta);

[0025] v = sin(theta);

[0026] phi∈[-90,90] and theta∈[-90,90] are the azimuth and elevation angles of the radar system, respectively.

[0027] Furthermore, the method for obtaining the angle information and the relative position is as follows:

[0028] Based on the radiation pattern, select each strong sidelobe point in the sinusoidal space (u≤u0)∩(v≤v0), and calculate the sinusoidal difference between each strong sidelobe point and the preset angle:

[0029]

[0030]

[0031] in: These are the sine values ​​corresponding to the nth strong sidelobe; Δu n and Δv n The sine difference is the value mentioned above.

[0032] Furthermore, the method for obtaining the angle information of strong sidelobes under actual flight conditions is as follows:

[0033] Calculate the direction of strong sidelobe clutter under actual flight conditions

[0034] Calculate the corresponding sine values ​​based on the azimuth beam phi0 and the elevation beam pointing theta0 of the radar system;

[0035] u0 = sin(phi0) * cos(theta0)

[0036] v0 = sin(theta0)

[0037] Set the clutter protection range Pr, and calculate the sine value [U] corresponding to all strong sidelobe boundary points. side V side ]

[0038] [U side V side ]={[u0±Δu n ±Pr,v0±Δv n ±Pr],n=1,2,...N}

[0039]

[0040] Furthermore, the method for converting the angle information of strong sidelobes under actual flight conditions into a spectrogram is as follows:

[0041] Calculate the radar system angle corresponding to the angle information of each strong sidelobe [phi] side theta side ]

[0042] theta side =arcsin(V side )

[0043] phi side =arcsin(U side / cos(theta side ))

[0044] Based on the inertial navigation information of the carrier aircraft using the multi-faceted array, the radar system angle [phi] side theta side Switch to the geographic system [Az_G_side0, El_G_side0]; set the strong clutter elevation angle threshold El_max, and select the strong clutter boundary point:

[0045] [AZ_G,El_G]={[AZ_G_side0,El_G_side0]|El_G_side0<El_max}

[0046] Calculate the velocity V_c at each strong clutter boundary point based on the three-dimensional velocity of the carrier aircraft:

[0047] V_c = [V_North, V_East, V_Down]

[0048] *[cos(Az_G)*cos(El_G); sin(Az_G)*cos(El_G); -sin(El_G)]

[0049] Where [V_North, V_East, V_Down] represent the northward speed, eastward speed, and groundward speed of the aircraft, respectively;

[0050] The distance R_c of each strong clutter boundary point is calculated based on the aircraft altitude, Earth radius, and geographic pitch angle El_G.

[0051] Based on the strong clutter range R_c and velocity V_c, as well as the radar operating waveform parameters, calculate the range gate Nr_c and Doppler gate Nf_c corresponding to the boundary points of the strong clutter in the spectrum.

[0052] Furthermore, the method for removing the strong sidelobes is as follows:

[0053] Determine the protection range of the strong clutter based on its boundary points:

[0054] Pr_Nr∈[Nr_c_min,Nr_c_max]

[0055] Pr_Nf∈[Nf_c_min,Nf_c_max]

[0056] Remove false alarms from the protection range.

[0057] By adopting the above technical solution, the present invention can bring the following beneficial effects:

[0058] (1) It is applicable to the sidelobe clutter suppression problem caused by the deterioration of the radiation pattern sidelobe due to any distributed layout, irregular layout and dilution layout. It has high accuracy in estimating the location of sidelobe clutter, small amount of computation, and is easy to implement in engineering.

[0059] (2) It will not affect the performance of the main lobe of the radiation pattern, and can guarantee the detection power and the angle measurement performance of the target within the main lobe. Moreover, this method can be applied to the estimation of main lobe clutter at the same time. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a sinusoidal spatial pattern in a specific embodiment of the present invention;

[0062] Figure 2 This is a radar spectrum diagram according to a specific embodiment of the present invention. Detailed Implementation

[0063] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0064] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0065] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0066] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0067] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0068] In one embodiment of the present invention, a method for eliminating strong sidelobe clutter in an airborne multi-array radar includes the following steps:

[0069] S101: Based on the array layout of the multi-faceted radar, calculate the radiation pattern of the multi-faceted array in sinusoidal space at a preset angle;

[0070] S102: Obtain the angle information of the strong sidelobe in the radiation pattern based on the radiation pattern;

[0071] S103: Based on the angle information, convert the strong sidelobes into the spectrum;

[0072] S104: Based on the Doppler information and distance information of the strong sidelobes in the spectrum, remove the strong sidelobes;

[0073] Wherein: 102 further includes: obtaining the relative position between the strong sidelobe and the main lobe;

[0074] When the preset angle in S101 is changed, the angle information is directly obtained based on the relative position and the changed preset angle.

[0075] In this embodiment, the method for calculating the radiation pattern is as follows:

[0076] Based on the array structure of the multifaceted array, calculate the array's preset angular pattern F(u,v) in sinusoidal space:

[0077] F(u,v)=(a(u set ,v set )) H *a(u,v)

[0078] a(u,v) is the signal steering vector, and its expression is:

[0079]

[0080] Where λ is the wavelength;

[0081] pos_array represents the coordinates of the facets of the multifaceted array;

[0082] u set =sin(phi) set )*cos(theta set );

[0083] v set =sin(theta) set );

[0084] phi set and theta set These are the preset azimuth and elevation angles of the multi-faceted radar system;

[0085] u = sin(phi) * cos(theta);

[0086] v = sin(theta);

[0087] phi∈[-90,90] and theta∈[-90,90] are the azimuth and elevation angles of the radar system, respectively.

[0088] In this embodiment, the method for obtaining the angle information and the relative position is as follows:

[0089] Based on the radiation pattern, select each strong sidelobe point in the sinusoidal space (u≤u0)∩(v≤v0), and calculate the sinusoidal difference between each strong sidelobe point and the preset angle:

[0090]

[0091]

[0092] in: These are the sine values ​​corresponding to the nth strong sidelobe; Δu n and Δv n The sine difference is the value mentioned above.

[0093] In this embodiment, the angle information of strong sidelobes under actual flight conditions is obtained as follows:

[0094] Calculate the direction of strong sidelobe clutter under actual flight conditions

[0095] Calculate the corresponding sine values ​​based on the azimuth beam phi0 and the elevation beam pointing theta0 of the radar system;

[0096] u0 = sin(phi0) * cos(theta0)

[0097] v0 = sin(theta0)

[0098] Set the clutter protection range Pr, and calculate the sine value [U] corresponding to all strong sidelobe boundary points. side V side ]

[0099] [U side V side ]={[u0±Δu n ±Pr,v0±Δv n ±Pr],n=1,2,...N}

[0100]

[0101] In this embodiment, the method for converting the angle information of strong sidelobes under actual flight conditions into a spectrogram is as follows:

[0102] Calculate the radar system angle corresponding to the angle information of each strong sidelobe [phi] side theta side ]

[0103] theta side =arcsin(V side )

[0104] phi side =arcsin(U side / cos(theta side ))

[0105] Based on the inertial navigation information of the carrier aircraft using the multi-faceted array, the radar system angle [phi] side theta side Switch to the geographic system [Az_G_side0, El_G_side0]; set the strong clutter elevation angle threshold El_max, and select the strong clutter boundary point:

[0106] [AZ_G,El_G]={[AZ_G_side0,El_G_side0]|El_G_side0<El_max}

[0107] Calculate the velocity V_c at each strong clutter boundary point based on the three-dimensional velocity of the carrier aircraft:

[0108] V_c = [V_North, V_East, V_Down]

[0109] *[cos(Az_G)*cos(El_G); sin(Az_G)*cos(El_G); -sin(El_G)]

[0110] Where [V_North, V_East, V_Down] represent the northward speed, eastward speed, and groundward speed of the aircraft, respectively;

[0111] The distance R_c of each strong clutter boundary point is calculated based on the aircraft altitude, Earth radius, and geographic pitch angle El_G.

[0112] Based on the strong clutter range R_c and velocity V_c, as well as the radar operating waveform parameters, calculate the range gate Nr_c and Doppler gate Nf_c corresponding to the boundary points of the strong clutter in the spectrum.

[0113] In this embodiment, the method for removing the strong sidelobes is as follows:

[0114] Determine the protection range of the strong clutter based on its boundary points:

[0115] Pr_Nr∈[Nr_c_min,Nr_c_max]

[0116] Pr_Nf∈[Nf_c_min,Nf_c_max]

[0117] Remove false alarms from the protection range.

[0118] To further explain,

[0119] This embodiment first simulates the radiation pattern of the multi-array radar in sinusoidal space when the multi-array is pointing at a large angle, based on the multi-array radar layout; then, it determines the relative positions of the strong sidelobes and the main lobe and the range of the strong sidelobes in sinusoidal space; finally, it maps the sinusoidal spatial position of the strong sidelobes to the range Doppler position in the radar spectrum, thereby eliminating the detected clutter false alarms.

[0120] The specific steps are as follows:

[0121] (1) Calculate the radiation pattern of the multi-faceted array

[0122] Based on the array structure of the multifaceted array, calculate the large-angle radiation pattern F(u,v) of the array in sinusoidal space:

[0123] F(u,v)=(a(u max ,v max )) H *a(u,v)

[0124] a(u,v) is the signal steering vector, and its expression is:

[0125]

[0126] Where λ is the wavelength, and pos_array is the array coordinates u of the multifaceted array. max =sin(phi) max )*cos(theta max ), v max =sin(theta) max ), phi max theta max Let be the azimuth and elevation angles of the radar system's maximum beam pointing, respectively, and u = sin(phi) * cos(theta), v = sin(theta), where phi ∈ [-90, 90] and theta ∈ [-90, 90] are the azimuth and elevation angles of the radar system, respectively.

[0127] Let phi max =60°, theta max =60° yields a sinusoidal spatial radiation pattern pointing downwards at large angles, such as Figure 1 As shown.

[0128] (2) Determine the relative positions of the strong sidelobe and the main lobe.

[0129] Based on the beam pointing pattern simulation results, each strong sidelobe point is selected in the sinusoidal space (u≤u0)∩(v≤v0), and their sinusoidal difference relative to the beam pointing is calculated:

[0130]

[0131]

[0132] in These are the sine values ​​corresponding to the nth strong sidelobe.

[0133] (3) Calculate the direction of strong sidelobe clutter under actual flight conditions

[0134] Calculate the sine value corresponding to the beam direction based on the azimuth, elevation beam direction phi0, and theta0 of the radar system.

[0135] u0 = sin(phi0) * cos(theta0)

[0136] v0 = sin(theta0)

[0137] Set the clutter protection range Pr, and calculate the sine value [U] corresponding to all strong sidelobe boundary points. side V side ]

[0138] [U side V side ]={[u0±Δu n ±Pr,v0±Δv n ±Pr],n=1,2,...N}

[0139]

[0140] like Figure 1 As shown, each strong sidelobe corresponds to 4 clutter boundaries.

[0141] Calculate the radar system azimuth corresponding to each group of strong sidelobe boundary points [phi] side theta side ]

[0142] theta side =arcsin(V side )

[0143] phi side =arcsin(U side / cos(theta side ))

[0144] Based on the aircraft's inertial navigation information, the radar system's azimuth angle [phi] is determined. side theta side Switch to the geographic system [Az_G_side0, El_G_side0]. Set the strong clutter elevation angle threshold El_max, and select the strong clutter boundary point:

[0145] [AZ_G,El_G]={[AZ_G_side0,El_G_side0]|El_G_side0<El_max}

[0146] (4) Calculate the position of the strong clutter boundary point in the radar spectrum.

[0147] Calculate the velocity V_c at each strong clutter boundary point based on the three-dimensional velocity of the carrier aircraft:

[0148] V_c = [V_North, V_East, V_Down]

[0149] *[cos(Az_G)*cos(El_G); sin(Az_G)*cos(El_G); -sin(El_G)]

[0150] Where [V_North, V_East, V_Down] represent the northward speed, eastward speed, and groundward speed of the aircraft, respectively.

[0151] The distance R_c of each strong clutter boundary point is calculated based on the aircraft altitude, Earth radius, and geographic pitch angle El_G.

[0152] Based on the strong clutter range R_c and velocity V_c, as well as the radar operating waveform parameters, calculate the range gate Nr_c and Doppler gate Nf_c corresponding to the boundary points of the strong clutter in the spectrum.

[0153] For a given strong clutter block, the protection range of the strong clutter is determined based on its boundary points:

[0154] Pr_Nr∈[Nr_c_min,Nr_c_max]

[0155] Pr_Nf∈[Nf_c_min,Nf_c_max]

[0156] (5) Eliminate false alarms and noise

[0157] After CFAR detection, false alarms belonging to the strong clutter boundary are removed.

[0158] Further explanation:

[0159] (1) Calculate the radiation pattern of the multi-faceted array

[0160] Let phi max =60°, theta max =60° yields a sinusoidal spatial radiation pattern pointing downwards at large angles, such as Figure 1 As shown.

[0161] (2) Determine the relative positions of the strong sidelobe and the main lobe.

[0162] Based on the beam pointing pattern simulation results, each strong sidelobe point is selected in the sinusoidal space (u≤u0)∩(v≤v0), and their sinusoidal difference relative to the beam pointing is calculated:

[0163] Δu n =0.0646 0.0688 0.1252...

[0164] Δv n =0.0273 0.0088 0.0088...

[0165] (3) Calculate the direction of strong sidelobe clutter under actual flight conditions

[0166] Based on the radar system's azimuth, elevation beam pointing at 28.4° and -60.6°, calculate the sine value corresponding to the beam pointing.

[0167] u0 = 0.2335

[0168] v0 = -0.8712

[0169] Calculate the sine value [U] corresponding to all strong sidelobe boundary points. side V side ]

[0170] [U side V side ={[0.2335±0.0646±0.026,-0.8712±0.0273±0.026]...}

[0171]

[0172] Calculate the radar system azimuth corresponding to each group of strong sidelobe boundary points [phi] side theta side ]

[0173] theta side = -54.9°...

[0174] phi side =34.3°...

[0175] Based on the aircraft's inertial navigation information [136.7728, 13.7461, -68.3593], the radar azimuth angle [34.3°, -54.9°] is converted to the geographic frame [-118.1°, -21.3°]. A strong clutter elevation angle threshold of -35° is set, and a strong clutter boundary point is selected.

[0176] [AZ_G,El_G]=[-118.1°,-21.3°]...

[0177] (4) Calculate the position of the strong clutter boundary point in the radar spectrum.

[0178] Calculate the velocity V_c at each strong clutter boundary point based on the three-dimensional velocity of the carrier aircraft:

[0179] V_c = [-145.2, -79.8, -15.2]

[0180] *[cos(-118.1°)*cos(-21.3°); sin(-118.1°)*cos(-21.3°); -sin(-21.3°)]...

[0181] Based on parameters such as the strong clutter range R_c and velocity V_c, the range gate Nr_c and Doppler gate Nf_c corresponding to the boundary points of the strong clutter in the spectrum are calculated.

[0182] For a given strong clutter block, the protection zone for the strong clutter is determined based on its boundary points, such as... Figure 2 As shown.

[0183] (5) Eliminate false alarms and noise

[0184] After CFAR detection, false alarms belonging to the strong clutter boundary are removed.

[0185] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for eliminating strong sidelobe clutter in an airborne multi-array radar, characterized in that, Includes the following steps: S101: Based on the array layout of the multi-faceted radar, calculate the radiation pattern of the multi-faceted array in sinusoidal space at a preset angle; S102: Obtain the angle information of the strong sidelobe in the radiation pattern based on the radiation pattern; S103: Based on the angle information, convert the strong sidelobes into the spectrum; S104: Based on the Doppler information and distance information of the strong sidelobes in the spectrum, remove the strong sidelobes; Wherein: S102 further includes: obtaining the relative position between the strong sidelobe and the main lobe; When the preset angle in S101 is changed, the angle information is directly obtained based on the relative position and the changed preset angle.

2. The method for eliminating strong sidelobe clutter in airborne multi-array radar according to claim 1, characterized in that, The method for calculating the radiation pattern is as follows: Based on the array structure of the multi-faceted array, calculate the array's preset angle pattern in sinusoidal space. : It is the signal steering vector, and its expression is: Where λ is the wavelength; pos_array represents the coordinates of the facets of the multifaceted array; ； ； phi set and theta set These are the preset azimuth and elevation angles of the multi-faceted radar system; ； ； phi∈[-90,90] and theta∈[-90,90] are the azimuth and elevation angles of the radar system, respectively.

3. The method for eliminating strong sidelobe clutter in airborne multi-array radar according to claim 2, characterized in that, The method for obtaining the angle information and the relative position is as follows: Based on the radiation pattern, select each strong sidelobe point within the sinusoidal space (u≤u0)∩(v≤v0), and calculate the sinusoidal difference between each strong sidelobe point and the preset angle: in: , These are the sine values ​​corresponding to the nth strong sidelobe; and The sinusoidal difference value, and The corresponding sine value is calculated based on the azimuth and elevation beam directions of the radar system.

4. The method for eliminating strong sidelobe clutter in airborne multi-array radar according to claim 3, characterized in that, The method for obtaining the angle information of strong sidelobes under actual flight conditions is as follows: Calculate the direction of strong sidelobe clutter under actual flight conditions Calculate the corresponding sine values ​​based on the azimuth beam phi0 and the elevation beam pointing theta0 of the radar system; Set the clutter protection range Pr, and calculate the sine value [U] corresponding to all strong sidelobe boundary points. side V side ] 。 5. The method for eliminating strong sidelobe clutter in airborne multi-array radar according to claim 4, characterized in that, The method for converting the angle information of strong sidelobes under actual flight conditions into a spectrogram is as follows: Calculate the radar system angle corresponding to the angle information of each strong sidelobe [phi] side theta side ] Based on the inertial navigation information of the carrier aircraft using the multi-faceted array, the radar system angle [phi] side theta side Switch to the geographic system [Az_G_side0, El_G_side0]; set the strong clutter elevation angle threshold El_max, and select the strong clutter boundary point: Calculate the velocity V_c at each strong clutter boundary point based on the three-dimensional velocity of the carrier aircraft: in These are the northward speed, eastward speed, and ground-direction speed of the aircraft, respectively. The distance R_c of each strong clutter boundary point is calculated based on the aircraft altitude, Earth radius, and geographic pitch angle El_G. Based on the strong clutter range R_c and velocity V_c, as well as the radar operating waveform parameters, calculate the range gate Nr_c and Doppler gate Nf_c corresponding to the boundary points of the strong clutter in the spectrum.

6. The method for eliminating strong sidelobe clutter in airborne multi-array radar according to claim 5, characterized in that, The method for removing the strong side lobes is as follows: Determine the protection range of the strong clutter based on its boundary points: Remove false alarms from the protection range.

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