A target detection method and system of a DBF system monitoring radar under sea clutter

Abstract

The application discloses a target detection method and system of a DBF system monitoring radar under sea clutter, and relates to the technical field of radio direction finding, and comprises the following steps: performing special beamforming DBF processing on the elevation array element echo data to obtain high-wave-position data, and performing conventional DBF processing on the elevation array element echo data to obtain low-wave-position data; performing pulse compression, Doppler filtering, constant false alarm detection and angle solution processing on the low-wave-position data and the high-wave-position data respectively, outputting low-wave-position tracks and high-wave-position tracks, dividing the low-wave-position tracks into high-speed channel tracks and low-speed channel tracks according to different Doppler channel numbers, processing the low-speed channel tracks through a pre-detection tracking cascaded range image backtracking correlation technology, outputting first tracks, and taking the high-wave-position tracks as second tracks; and taking the high-speed channel tracks, the first tracks and the second tracks as target detection results and outputting the target detection results. The application effectively suppresses the sea clutter without affecting the detection and tracking of high-altitude and high-speed targets by the radar.

Description

Technical Field

[0001] This invention relates to the field of radar detection, and in particular to a target detection method and system for DBF-based surveillance radar under sea clutter. Background Technology

[0002] Sea clutter, the large amount of backscattered echoes generated when a radar beam illuminates the sea surface, is one of the core factors limiting the detection performance of sea surface radar targets (such as ships, low-altitude aircraft, icebergs, etc.). In key areas such as maritime surveillance, maritime safety, maritime search and rescue, and shipborne / shore-based radar defense systems, the detection and tracking of small targets against strong sea clutter backgrounds remains an extremely challenging task. Compared to land clutter, the sea surface, as a dynamic, random, and time-varying distributed scatterer, generates clutter that exhibits high non-stationarity and non-Gaussianity in the time, frequency, and spatial domains, easily obscuring target signals with similar characteristics. Therefore, a deep understanding of the intrinsic physical characteristics and statistical laws of sea clutter, and the development of efficient and robust suppression and processing techniques accordingly, has become a long-term and important research direction in the field of radar signal processing.

[0003] The generation mechanism and characteristics of sea clutter are extremely complex, influenced by two main categories of factors: sea surface conditions and radar parameters. From an environmental perspective, ocean physical parameters such as wind speed, wind direction, wave height, wave direction, seawater salinity, and temperature collectively determine sea surface roughness and capillary wave structure, directly affecting scattering intensity and distribution. From the radar system's perspective, radar operating frequency, polarization, incident angle, spatial resolution, and observation time all significantly alter the presentation of sea clutter. Among these, the incident angle is crucial for defining sea clutter scattering regions and mechanisms: at near-grazing angles, sea clutter intensity is high, and its amplitude statistical distribution tends towards a long-tailed composite Gaussian model (such as the K-distribution or Weibull distribution), exhibiting significant temporal correlation and spatial non-uniformity. Simultaneously, due to the periodic motion of ocean waves, sea clutter exhibits significant Doppler broadening and spectral peaking in the frequency domain, with its Doppler center frequency and spectral width closely related to radar pointing and wave motion direction. These complex characteristics cause a sharp decline in the performance of traditional detection methods based on static backgrounds and fixed thresholds.

[0004] Therefore, a target detection method and system for DBF-based surveillance radar under sea clutter was developed to solve the above problems. Summary of the Invention

[0005] This invention proposes a target detection method and system for DBF-based surveillance radar under sea clutter, in order to solve the problem that the target detection performance of existing detection methods drops sharply under the complex characteristics of sea clutter.

[0006] The present invention achieves the above objectives through the following technical solutions:

[0007] This invention provides a target detection method for a DBF-based surveillance radar under sea clutter, comprising:

[0008] Acquire pitch azimuth array element-level echo data;

[0009] Special shaping DBF processing is performed on the pitch azimuth element echo data to obtain high-wavelength data. The special shaping DBF processing is to set notches in the pitch angle range of the illuminated sea surface to suppress the intensity of sea clutter in the airspace. Conventional DBF processing is performed on the pitch azimuth element echo data to obtain low-wavelength data.

[0010] Pulse compression, Doppler filtering, constant false alarm rate detection, and angle resolution processing are performed on low-wavelength and high-wavelength data respectively to output low-wavelength and high-wavelength traces. The low-wavelength traces are divided into high-speed channel traces and low-speed channel traces according to different Doppler channel numbers. The low-speed channel traces are processed by pre-detection tracking cascaded range image backtracking correlation technology to output the first trace, and the high-wavelength traces are used as the second trace.

[0011] The high-speed channel spot, the first spot, and the second spot are combined as the target detection result output.

[0012] The DBF processing used in the above-mentioned DBF processing of the pitch azimuth array element echo data is the conventional DBF processing in the prior art. DBF stands for "Digital Beamforming".

[0013] Furthermore, the elevation azimuth array element echo data undergoes special shape-based DBF processing to obtain high-frequency data, including:

[0014] Discretize the range of pitch angles of the high-wave illumination of the sea surface, and calculate the corresponding steering vector based on the discretization results;

[0015] Based on the clutter intensity, diagonal loading coefficient, and identity matrix corresponding to each direction, the sea clutter covariance matrix is ​​calculated using vector multiplication, addition, and conjugate operations.

[0016] Based on the sea clutter covariance matrix, a shaping adjustment factor is constructed using matrix inverse operations.

[0017] Calculate the weight coefficients of the conventional DBF filter based on the direction of the high-wavelength data;

[0018] The shaped DBF weight coefficients are calculated by multiplying the shaping adjustment factor and the conventional DBF filter weight coefficients. The shaping adjustment factor determines the depth and width of the notch in the shaped DBF weight coefficient radiation pattern. The depth of the notch represents the strength of sea clutter suppression. The greater the depth of the notch, the stronger the sea clutter suppression. The width of the notch represents the angular range of sea clutter suppression.

[0019] Spatial filtering is performed on the pitch azimuth array element echo data based on the shaping DBF weight coefficients and DBF processing flow to obtain high-frequency data.

[0020] Furthermore, the formula for calculating the weighting coefficients of the shaped DBF is as follows:

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] in, Indicates the weighting coefficients of the shaped DBF. The sea clutter covariance matrix is... As a shaping regulator, These are the weight coefficients for a standard DBF filter. The clutter intensity corresponding to each direction, This represents the vector transpose operation. The diagonal loading coefficient is used. It is the identity matrix. Indicates conjugate computation. The range of pitch angles representing the wave potential illuminating the sea level is discretized into P values. As the guide vector, Relative value The smaller the size, the deeper the overall depth of the notch. Under certain circumstances, The larger the notch, the deeper it is. The larger the value, the wider the notch. Different notch depths can be obtained by taking different values. To obtain different notch widths.

[0027] The shaping adjustment factor determines the depth and width of the notch in the shaped DBF weight coefficient pattern. A greater notch depth results in stronger sea clutter suppression, while the notch width represents the angular range of sea clutter suppression. Therefore, by setting different sea clutter elevation angle ranges, corresponding clutter intensities, diagonal loading coefficients, and other parameters, different shaping adjustment factors can be obtained, leading to different notch depths and widths.

[0028] Furthermore, the low-speed channel traces are processed using pre-detection tracking cascaded distance image backtracking correlation technology to output the first trace, including:

[0029] For each low-speed channel track and the output tracks of the adjacent previous few frames, a multi-frame track filtering association based on TBD is used. If the current low-speed channel track is not associated with the output tracks of the adjacent previous few frames, the current low-speed channel track is discarded. If the current low-speed channel track is associated with the output tracks of the adjacent previous few frames, the current low-speed channel track is retained and the process proceeds to the next step.

[0030] Extract the current low-speed channel trace and the distance images corresponding to the adjacent previous frame traces associated with it, calculate the average distance image of the distance images of the adjacent previous frame traces, and calculate the correlation coefficient between the current low-speed channel trace distance image and the average distance image.

[0031] A correlation coefficient threshold decision is made on the correlation coefficient, discarding low-speed channel traces with correlation coefficients less than the correlation coefficient threshold value, and retaining low-speed channel traces with correlation coefficients greater than or equal to the correlation coefficient threshold value.

[0032] Furthermore, the average range image of the point traces in the preceding few frames is calculated, and the correlation coefficient between the current low-velocity channel point trace and the average range image is calculated, including:

[0033] The formula for calculating the retained low-velocity channel traces and the distance images corresponding to the adjacent traces from the previous few frames is as follows: , , That is, the distance image corresponding to the nth low-speed channel point. Represents the normalized distance image of the nth low-speed channel point;

[0034] Calculate the average distance image of the traces in the preceding few adjacent frames. The calculation formula is: The distance image of the points in the first few frames is represented as Where N represents the total number of associated points;

[0035] Calculate the correlation coefficient between the current point distance image and the mean distance image of the track: ,in, Represents norm operations, This indicates the distance of the current point trace to the image in the association.

[0036] Furthermore, the threshold value of the correlation coefficient ranges from approximately 0.8 to 0.9.

[0037] Furthermore, the low-wavelength spot is divided into low-velocity channel spot and high-velocity channel spot according to different Doppler channel numbers.

[0038] Furthermore, the points whose absolute radial velocity is less than the sea clutter velocity are low-velocity channel points.

[0039] The present invention also provides a system for a target detection method of a DBF-based surveillance radar under sea clutter, comprising:

[0040] The acquisition module is used to acquire pitch astronomical array element-level echo data;

[0041] The first processing module is used to perform special shaping DBF processing on the pitch azimuth element echo data to obtain high-wavelength data. The special shaping DBF processing is to set notches in the pitch angle range of the illuminated sea surface to suppress the intensity of sea clutter in the airspace. The pitch azimuth element echo data is processed by conventional DBF processing to obtain low-wavelength data.

[0042] The second processing module performs pulse compression, Doppler filtering, constant false alarm rate detection, and angle resolution processing on the low-wavelength data and high-wavelength data, respectively, and outputs low-wavelength and high-wavelength traces. The low-wavelength traces are divided into high-speed channel traces and low-speed channel traces according to different Doppler channel numbers. The low-speed channel traces are processed by the pre-detection tracking cascaded distance image backtracking correlation technology to output the first trace, and the high-wavelength traces are used as the second trace.

[0043] The third processing module is used to perform pulse compression, Doppler filtering, constant false alarm detection and angle resolution processing on the high-wavelength data, and output the high-wavelength point trace, which is the second point trace.

[0044] The output module is used to output the high-speed channel spot, the first spot, and the second spot as the target detection result.

[0045] The beneficial effects of this invention are as follows:

[0046] This invention proposes a target detection method and system for DBF-based surveillance radar under sea clutter. It employs low-wavelength TBD-based cascaded range image backtracking correlation suppression technology and high-wavelength DBF filtering technology with special shaping to suppress sea clutter. Through hierarchical processing of spatial and velocity channels, it effectively suppresses sea clutter without affecting the radar's detection and tracking of high-altitude and high-speed targets. It is simple and easy to implement in engineering, and the parameters can be set according to the actual application scenario, which has strong flexibility and applicability. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the target detection technology of a pitch-dimensional DBF system surveillance radar under sea clutter according to the present invention.

[0048] Figure 2 This is a schematic diagram of the radiation pattern of special shaping DBF processing and conventional DBF processing when the beam pointing is -27°. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0051] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0052] like Figure 1 As shown in the figure. The processing flow of a target detection technology for a pitch-dimensional DBF (Dual Array Filtering) surveillance radar under sea clutter in this embodiment includes: performing DBF processing and special shaping DBF on the pitch azimuth array element echo data respectively to obtain low-position data and high-position data. The beam illuminating the sea surface with the main beam is the low-position data, and the remaining beams are the high-position data. The low-position data based on DBF processing undergoes conventional pulse compression, Doppler filtering, constant false alarm rate (CFAR) detection, and angle resolution processing to obtain output point traces. These point traces are divided into low-velocity channel traces and high-velocity channel traces according to the Doppler channel number. For the low-velocity channel traces, a large number of sea clutter point traces are discarded using TBD (Total Range Filtering) concatenated range image backtracking correlation technology. The remaining point traces are merged with the high-velocity channel traces to form the total output point traces for the current position. Special shaping DBF processing refers to special shaping DBF processing using a low elevation angle (direction of incoming waves from the sea level), that is, attenuation within the elevation angle range corresponding to the direction of incoming waves from the sea level and on the DBF pattern. The beam data after special shaping DBF processing is processed by conventional pulse compression, Doppler filtering, and constant false alarm rate detection signal processing to obtain the output point trace.

[0053] After signal processing, the low-wavelength data yields several points. For those points whose absolute radial velocity (converted from the Doppler channel number) is less than 8 m / s (i.e., low-velocity channel points), a pre-detection tracking cascaded range image backtracking correlation technique is applied. Specifically, if the current frame is frame 1, then the data of the two adjacent previous frames are correlated by track filtering. If the number of correlated points is greater than 2, then the range images corresponding to these points are extracted, and their normalized range image and average range image are calculated. Then, the correlation coefficient is calculated, and only the points that have exceeded the correlation coefficient threshold are retained, while the rest are discarded.

[0054] The specific steps involved in the TBD concatenated distance image backtracking related processing are as follows:

[0055] Step 1: First, designate the points with an absolute radial velocity of less than 8 m / s as low-velocity channel points. For each low-velocity channel point, use a 3-frame trajectory filtering association based on TBD with the output points of the two preceding frames. That is, perform association filtering with the points of the two preceding frames. If no association is found, discard the point.

[0056] Step 2: Extract the distance images corresponding to the current frame's traces and the traces from the previous few frames, and perform distance image correlation calculations. The associated frame's trace is represented as... The distance image of the points in the first two frames is represented as Where N represents the total number of associated points, which in this embodiment could be 2 or 3. First, the normalized distance image of each point is calculated: , Then calculate the average distance image: Finally, the correlation coefficient between the current point's distance image and the average distance image of the flight path is calculated: .in, Represents norm operations.

[0057] Step 3: Perform correlation coefficient threshold decision: discard. The dots are preserved. The output points are determined by the TBD cascaded distance image retrospective correlation technique. This is the threshold value for the correlation coefficient, which is set to 0.86 in this embodiment.

[0058] By applying pre-detection tracking cascade range image backtracking correlation technology to measured low-wave data of a radar illuminating the sea surface, 60% of the sea clutter spot is effectively removed while the target spot is effectively preserved.

[0059] The DBF processing procedure for special shaping includes:

[0060] Step 1: Define the range of elevation angles for the high-wave illumination of the sea level. Discretize into 100 values ​​at equal intervals: Then the corresponding guide vectors can be calculated as follows: , This represents the vector transpose operation. Adjusting the pitch angle range determines the position and width of the notch.

[0061] Step 2: Let the clutter intensity corresponding to each direction be Gaussian in shape, represented as: Therefore, the sea clutter covariance matrix can be calculated: .in, Indicates conjugate computation. It is the diagonal loading coefficient. It is an identity matrix. Adjustment and The shape and depth of the notch can be adjusted; in general, Relative value The smaller the size, the deeper the overall depth of the notch, while... Under certain circumstances, The larger the size, the deeper the notch; this can be achieved by setting... The specific values ​​in the notch allow for different shapes and depths. Different notch widths are obtained by varying the range of pitch angle values. The larger the value, the wider the notch. To obtain different notch widths.

[0062] Step 3: Calculate the beam pointing direction. The shaping DBF weight coefficients are: ,in These are standard DBF weighting coefficients. The direction pattern of the weight coefficients in the special shape DBF (Dual Matrix Formulation) represents the matrix inverse operation. Figure 2 As shown by the solid black line in the middle, the direction plot of the conventional DBF weight coefficients is as follows. Figure 2 As shown by the black dashed line, within the pitch angle range Internally, the specially shaped DBF suppresses sea clutter by approximately 20 dB more than a conventional DBF, hence the term "notch." This can be achieved by adjusting the pointing angle. To obtain the direction pattern of other pointing angles.

[0063] This embodiment also provides a system for the target detection method of the DBF system surveillance radar under sea clutter, including:

[0064] The acquisition module is used to acquire pitch astronomical array element-level echo data;

[0065] The first processing module is used to perform DBF processing and special shaping DBF processing on the pitch azimuth element echo data to obtain low-wavelength data and high-wavelength data, respectively.

[0066] The second processing module is used to perform pulse compression, Doppler filtering and constant false alarm detection on the low-wavelength filtered data, output low-wavelength spot traces, divide the low-wavelength spot traces into high-speed channel spot traces and low-speed channel spot traces, and obtain the first spot trace based on the pre-detection tracking cascaded distance image backtracking correlation technology.

[0067] The third processing module is used to perform pulse compression, Doppler filtering and constant false alarm detection on the high-wave data, and output the high-wave spot trace. The special shaping DBF is to set notches in the pitch angle range of the illuminated sea surface to filter out sea clutter in the airspace and obtain the second spot trace.

[0068] The output module is used to output the high-speed channel spot, the first spot, and the second spot as the target detection result.

[0069] Compared with the prior art, the present invention has the following beneficial effects.

[0070] 1) This invention employs TBD cascaded range image backtracking correlation technology for sea clutter spot suppression. Since this method is only used for spots in low-wave-position, low-velocity channels, it minimizes computational load while ensuring timely departure of fast targets.

[0071] 2) This invention employs TBD cascaded range image backtracking correlation technology. First, TBD technology is used to suppress sea clutter points with short correlation times (milliseconds and seconds). Then, range image backtracking correlation is used to suppress sea clutter points with longer correlation times (seconds or even tens of seconds). This dual sea clutter suppression technology can effectively filter out sea clutter points such as fractals and sea spikes, while retaining slow-moving targets and targets with approximately tangential motion.

[0072] 3) This invention uses TBD cascaded range image backtracking correlation technology, which is also applicable to ground clutter. Since ground objects are more rigid than sea clutter and have strong range image correlation, only the TBD algorithm is needed in the ground clutter application scenario.

[0073] 4) This invention uses DBF technology with special high-wave-position shaping. For fixed-station radar, the direction of incoming waves from the sea surface is fixed. Therefore, the DBF coefficients with special shaping can be generated offline, which is simple and easy to apply in engineering.

[0074] 5) The special shaping DBF technology used in this invention is also applicable to ground clutter, and the depth and width of the notch can be parameterized, making it flexible and efficient.

[0075] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A target detection method for DBF-based surveillance radar under sea clutter, characterized in that, include: Acquire pitch azimuth array element-level echo data; Special shaping DBF processing is performed on the pitch azimuth element echo data to obtain high-wavelength data. The special shaping DBF processing is to set notches in the pitch angle range of the illuminated sea surface to suppress the intensity of sea clutter in the airspace. Conventional DBF processing is performed on the pitch azimuth element echo data to obtain low-wavelength data. Pulse compression, Doppler filtering, constant false alarm rate detection, and angle resolution processing are performed on low-wavelength and high-wavelength data respectively to output low-wavelength and high-wavelength traces. The low-wavelength traces are divided into high-speed channel traces and low-speed channel traces according to different Doppler channel numbers. The low-speed channel traces are processed by pre-detection tracking cascaded range image backtracking correlation technology to output the first trace, and the high-wavelength traces are used as the second trace. The high-speed channel spot, the first spot, and the second spot are combined as the target detection result output.

2. The target detection method of a DBF-based surveillance radar under sea clutter according to claim 1, characterized in that, Special shaper DBF processing is applied to the elevation azimuth array element echo data to obtain high-frequency data, including: Discretize the range of pitch angles of the high-wave illumination of the sea surface, and calculate the corresponding steering vector based on the discretization results; Based on the clutter intensity, diagonal loading coefficient, and identity matrix corresponding to each direction, the sea clutter covariance matrix is ​​calculated using vector multiplication, addition, and conjugate operations. Based on the sea clutter covariance matrix, a shaping adjustment factor is constructed using matrix inverse operations. Calculate the weighting coefficients of the conventional DBF filter based on the direction of the high-wavelength data; The shaped DBF weight coefficients are calculated by multiplying the shaping adjustment factor and the conventional DBF filter weight coefficients. The shaping adjustment factor determines the depth and width of the notch in the shaped DBF weight coefficient radiation pattern. The depth of the notch represents the strength of sea clutter suppression. The greater the depth of the notch, the stronger the sea clutter suppression. The width of the notch represents the angular range of sea clutter suppression. Spatial filtering is performed on the pitch azimuth array element echo data based on the shaping DBF weight coefficients and DBF processing flow to obtain high-frequency data.

3. The target detection method of a DBF-based surveillance radar under sea clutter according to claim 2, characterized in that, The formula for calculating the weight coefficients of the shaped DBF is: ； ； ； ； ； in, Indicates the weighting coefficients of the shaped DBF. The sea clutter covariance matrix is... As a shaping regulator, These are the weight coefficients for a standard DBF filter. The clutter intensity corresponding to each direction, This represents the vector transpose operation. The diagonal loading coefficient is used. It is the identity matrix. Indicates conjugate computation. The range of pitch angles representing the wave potential illuminating the sea level is discretized into P values. As the guide vector, Relative value The smaller the size, the deeper the overall depth of the notch. Under certain circumstances, The larger the notch, the deeper it is. The larger the value, the wider the notch. Different notch depths can be obtained by taking different values. To obtain different notch widths.

4. The target detection method of a DBF-based surveillance radar under sea clutter according to claim 1, characterized in that, The low-speed channel traces are processed using pre-detection tracking cascaded distance image backtracking correlation technology to output the first trace, including: For each low-speed channel track and the output tracks of the adjacent previous few frames, a multi-frame track filtering association based on TBD is used. If the current low-speed channel track is not associated with the output tracks of the adjacent previous few frames, the current low-speed channel track is discarded. If the current low-speed channel track is associated with the output tracks of the adjacent previous few frames, the current low-speed channel track is retained and the process proceeds to the next step. Extract the current low-speed channel trace and the distance images corresponding to the adjacent previous frame traces associated with it, calculate the average distance image of the distance images of the adjacent previous frame traces, and calculate the correlation coefficient between the current low-speed channel trace and the average distance image. A correlation coefficient threshold decision is made on the correlation coefficient, discarding low-speed channel traces with correlation coefficients less than the correlation coefficient threshold value, and retaining low-speed channel traces with correlation coefficients greater than or equal to the correlation coefficient threshold value.

5. A target detection method for a DBF-based surveillance radar under sea clutter as described in claim 4, characterized in that, Calculate the average distance image of the point traces in the preceding few frames, and calculate the correlation coefficient between the current low-velocity channel point trace distance image and the average distance image, including: The formula for calculating the retained low-velocity channel traces and the distance images corresponding to the adjacent traces from the previous few frames is as follows: , , That is, the distance image corresponding to the nth low-speed channel point. Represents the normalized distance image of the nth low-speed channel point; Calculate the average distance image of the traces in the preceding few adjacent frames. The calculation formula is: The distance image of the points in the first few frames is represented as Where N represents the total number of associated points; Calculate the correlation coefficient between the current point distance image and the mean distance image of the track: ,in, Represents norm operations, This indicates the distance of the current point trace to the image in the association.

6. A target detection method for a DBF-based surveillance radar under sea clutter as described in claim 1, characterized in that, The threshold value for the correlation coefficient ranges from 0.8 to 0.

9.

7. A target detection method for a DBF-based surveillance radar under sea clutter as described in claim 1, characterized in that, The low-wavelength spot is divided into low-velocity channel spot and high-velocity channel spot according to the different Doppler channel numbers.

8. A target detection method for a DBF-based surveillance radar under sea clutter as described in claim 7, characterized in that, The points whose absolute radial velocity is less than the sea clutter velocity are low-velocity channel points.

9. A system for a target detection method of a DBF-based surveillance radar under sea clutter as described in any one of claims 1-8, characterized in that, include: The acquisition module is used to acquire pitch astronomical array element-level echo data; The first processing module is used to perform special shaping DBF processing on the pitch azimuth element echo data to obtain high-wavelength data. The special shaping DBF processing is to set notches in the pitch angle range of the illuminated sea surface to suppress the intensity of sea clutter in the airspace. The pitch azimuth element echo data is processed by conventional DBF processing to obtain low-wavelength data. The second processing module performs pulse compression, Doppler filtering, constant false alarm rate detection, and angle resolution processing on the low-wavelength data and high-wavelength data, respectively, and outputs low-wavelength and high-wavelength traces. The low-wavelength traces are divided into high-speed channel traces and low-speed channel traces according to different Doppler channel numbers. The low-speed channel traces are processed by the pre-detection tracking cascaded distance image backtracking correlation technology to output the first trace, and the high-wavelength traces are used as the second trace. The third processing module is used to perform pulse compression, Doppler filtering, constant false alarm detection and angle resolution processing on the high-wavelength data, and output the high-wavelength point trace, which is the second point trace. The output module is used to output the high-speed channel spot, the first spot, and the second spot as the target detection result.

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