A Multifunctional Radar Signal Processing Method for Maritime Surveillance

Abstract

The present invention provides a method for processing multifunctional radar signals for sea, which firstly adopts the DBF system, mainly searches, forms three-dimensional coverage of the monitoring area, and forms a preliminary classification of surface and air targets; for stable sea surface targets, they are distinguished according to the amplitude of the echo, and step-frequency imaging is performed on large targets, and the size of the ship is distinguished according to the size of the target imaging; for air targets, low-altitude aircraft, flying birds, etc. are distinguished according to the stability of the track and micro-Doppler characteristics. The present invention can simultaneously search, monitor and identify close-range low-altitude targets and sea surface targets, and can effectively improve the performance of the radar system.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a radar signal processing method. Background Art

[0002] China is a large maritime country with a mainland coastline of 18,400 km. As my country has a long coastline and abundant coastal resources, the coastal economy plays a very important role in China's total economy. But at the same time, the long coastline brings huge economic benefits but also brings certain challenges. Therefore, the shore-based radars deployed along the coast are required to be able to handle low, slow and small targets in the offshore environment and monitor large targets of ships in distant waters. Most shore-based radars are distributed along the coastline, with a harsh working environment and interference from natural clutter such as sea clutter, bad weather, wind, sand, rain and snow.

[0003] The pulse Doppler radar system is used for searching and tracking normal targets. When the instantaneous bandwidth is not high, the radar resolution is relatively low. The stepped frequency mode has high resolution in radial distance and can form a high-resolution range image of the target, but its working distance is relatively limited, and it mainly completes the imaging of close-range static or low-speed targets. Pulse Doppler radar combined with the stepped frequency technology system can improve the radar's range resolution when the instantaneous bandwidth is not high, making it easier to identify small targets. The patent application "A staggered tracking method combining high repetition rate step frequency with pulse Doppler system" and the papers "Research on frequency stepping and pulse Doppler composite velocity measurement" and "Design of frequency stepping and pulse Doppler composite compatibility" and other public documents all discuss the application of the pulse Doppler radar system and the step frequency system. However, the patent application "A staggered tracking method combining high repetition rate step frequency with pulse Doppler system" only discusses how to perform step frequency imaging in the high repetition rate mode; the paper "Research on frequency stepping and pulse Doppler composite velocity measurement" discusses how to improve the velocity measurement accuracy by combining Doppler and step frequency; the paper "Design of frequency stepping and pulse Doppler composite compatibility" only discusses the feasibility of combining the principles of the two systems. However, none of them discusses how to combine pulse Doppler and step frequency imaging and automatically switch between the two systems to be compatible with the recognition of large targets on the sea surface and small targets at low altitude. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a method for processing sea-targeting multifunctional radar signals, which can simultaneously search, monitor and identify close-range low-altitude targets and sea surface targets.

[0005] The technical solution adopted by the present invention to solve the technical problem comprises the following steps:

[0006] 1) After the radar is powered on, it analyzes the instructions sent by the host computer and selects the working mode according to the instructions; when working in pulse Doppler mode, it goes to step 2); when working in stepped frequency mode, it goes to step 3);

[0007] 2) Obtain radar echo data, perform moving target detection, constant false alarm detection and point trace condensation; classify the target according to its speed, RCS and echo amplitude. If the target is classified as an aerial target, proceed to step 4); if the target is classified as a ship, the radar switches to stepped frequency mode and proceeds to step 3);

[0008] 3) Obtain radar echo data or target data detected by pulse Doppler mode, perform velocity compensation on the data; perform IFFT operation on the motion compensated data; perform periodic extension on the IFFT data, use the same distance selection method to extract the target, and obtain the one-dimensional range image of the target; identify the target length according to the imaging results;

[0009] 4) Receive the host computer command. If there is an imaging command, switch to the working mode specified by the command. The pulse Doppler mode goes to step 2), and the stepped frequency mode goes to step 3); if there is no imaging command, switch back to the pulse Doppler mode and go to step 2).

[0010] The radar echo data of step 2) or step 3) is pre-processed with pulse compression.

[0011] The step 2) performs FFT processing on the radar echo data to complete moving target detection.

[0012] In the step 2), GOCFAR is used for constant false alarm detection in the low-altitude environment, and VICAFR is used for constant false alarm detection in the sea surface environment.

[0013] In step 2), targets with an RCS greater than 1 square meter or a speed less than 8 m / s and a target amplitude greater than 20 dB above the average noise are classified as ships, and other targets are classified as drones.

[0014] In the step 2), targets with a height greater than a set threshold are classified as aerial targets, target features are further extracted, and time-frequency analysis is performed using multiple rounds of echo data. If the target meets the calibrated time-frequency features of a rotary-wing UAV, it is determined to be a rotary-wing UAV, otherwise it is determined to be an unknown aerial target.

[0015] In step 2), if the target is classified as an aerial target, a micro-Doppler method is used to further extract target feature information.

[0016] Step 3) Calculate the compensation factor Multiply the data of all frequency points under each range gate by the compensation factor to complete the speed compensation, where f0 represents the radar starting frequency, Δf represents the radar step frequency, and V s represents the estimated target speed, c represents the speed of light, T r It represents the pulse repetition period, and i represents the serial number of different step frequencies.

[0017] In step 3), the number of continuous distance units occupied by the target in the one-dimensional distance image is recorded, and the number is multiplied by the actual distance represented by each distance unit to obtain the radial length of the target; the radial length is divided by |cos(azimuth angle - heading angle)| to obtain the actual length of the target.

[0018] The step 3) uses the same distance selection method to extract the target, and only extracts the distance length information corresponding to the pulse width τ for each sampling point, and compares it with the same distance units extracted from the adjacent sampling points, and extracts the points with larger amplitudes as the extraction results. The extracted result is the one-dimensional distance image of the target.

[0019] The beneficial effects of the present invention are: the radar echo signal is processed by the pulse Doppler mode, and the target detection and target parameter extraction are completed through moving target detection, constant false alarm detection and target cohesion, so as to realize the detection of "low, small and slow" targets (with unmanned aerial vehicles as typical targets) and marine active targets (with marine ships as typical targets). The two different systems of pulse Doppler mode and stepped frequency mode are applied in the radar signal processing system, which complement each other and can effectively improve the system performance.

[0020] The present invention organically combines two types of radars, detects low-altitude and sea-surface targets through pulse Doppler radars, and adopts different clutter suppression methods for low altitude and sea-surface targets to cope with different clutter situations. After detecting the target, a preliminary classification is performed according to the target information. If a target is preliminarily classified as a ship, it automatically switches to the step frequency mode, and automatically performs step frequency imaging on the target classified as a ship. If it is classified as a drone, further feature extraction is performed on the drone target, and the drone is always tracked in the pulse Doppler mode. Therefore, it is possible to judge whether the target at sea is a ship or a drone. If it is a ship, the size of the ship can be preliminarily determined. If it is a drone, the characteristics and track information of the drone can be accurately known, thereby realizing monitoring of the situation at sea.

[0021] The present invention improves the distance resolution of large targets on the sea surface through a low-resolution search and stepped frequency tracking method.

[0022] The present invention combines three-coordinate detection, stepped frequency imaging, micro-Doppler and other technologies, so that the radar system has a certain recognition ability and preliminary target classification ability, and can distinguish targets into large, medium and small ships on the sea, quadcopters in the air, other aerial targets, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of initialization of the method for processing sea multifunctional radar signals of the present invention;

[0024] Figure 2 This is a working flow chart of the pulse Doppler mode of the present invention;

[0025] Figure 3 This is a schematic diagram of the VICFAR detection principle in the pulse Doppler mode of the present invention;

[0026] Figure 4 This is a schematic diagram of the GOCFAR detection principle in the pulse Doppler mode of the present invention;

[0027] Figure 5 This is a working diagram of the step frequency mode of the present invention;

[0028] Figure 6 The figure is an overall workflow diagram of the method for processing sea multifunctional radar signals of the present invention. DETAILED DESCRIPTION

[0029] The present invention is further described below in conjunction with the accompanying drawings and embodiments. The present invention includes but is not limited to the following embodiments.

[0030] The present invention adopts the idea of ​​hierarchical processing. First, the DBF system is adopted, with search as the main method, to form a three-dimensional coverage of the monitoring area, and to form a preliminary classification of surface and aerial targets. For sea targets that are in stable navigation, they are distinguished according to the amplitude of the echo, large targets are subjected to stepped frequency imaging processing, and the size of the ship is distinguished according to the size of the target imaging; for aerial targets, low-altitude aircraft, birds, etc. are distinguished according to the stability of the track and micro-Doppler characteristics. In addition, the comprehensive processing of sea surface targets and low, slow and small targets is also a difficult point. The present invention adopts adaptive CFAR technology to suppress clutter for different environmental backgrounds, thereby improving the detection probability of the target.

[0031] The embodiment of the present invention provides a method for processing sea multi-function radar signals, such as Figure 1 As shown, the method comprises the following steps:

[0032] S1. Radar is powered on and DSP is initialized.

[0033] S2. Analyze the instructions sent by the host computer and select the working mode

[0034] S3, sending a doorbell to FPGA to indicate that DSP initialization is complete;

[0035] S4, wait for the data sent by FPAG and enter the corresponding working mode.

[0036] exist Figure 1 In the technical solution shown, after the radar is powered on, the DSP configures the default parameters, creates the blocks and view objects required for subsequent function calculations, and then waits for the configuration of each module to be completed. After the configuration is completed, the instructions sent by the host computer through the network port are parsed, and the working mode is selected as pulse Doppler mode or step frequency mode according to the instructions; after entering the corresponding working mode, a doobell doorbell is sent to the front-end FPGA to indicate that the DSP initialization is complete, and the FPGA is waiting to send the processed pulse compression echo data of each beam through the SRIO interface, and then enters the pulse Doppler mode or step frequency mode to work.

[0037] The workflow of the pulse Doppler mode is as follows Figure 2 As shown, the specific steps include:

[0038] S31, receiving the pulse compression echo data of each beam after being processed by FPGA;

[0039] S32, performing moving target detection on the received data;

[0040] S33, performing constant false alarm processing on the detected data, setting a threshold, and performing target screening;

[0041] S34, performing target aggregation processing on the selected targets;

[0042] S35, sending the condensed target to the host computer through the network port.

[0043] S36, classify the target, if the height of the target is greater than the air target threshold (depending on the radar progress, the height is 50 meters in this embodiment), then judge it as an air target, otherwise it is judged as a sea target;

[0044] S37. If the target is classified as an aerial target, further feature extraction is performed, and time-frequency analysis is performed using multiple turns of echo data. If it meets the calibrated video features of a rotary-wing UAV, it is determined to be a rotary-wing UAV, and the results are displayed (including structural information such as the shape and size of the target, and motion information such as the attitude and speed of the target). Otherwise, it is determined to be an unknown aerial target. If the target is classified as a ship, the stepped frequency mode is entered (according to the size of the radar reflection area, the present invention divides the targets into large, medium and small ships on the sea, quadcopters in the air, other aerial targets, etc.).

[0045] The following Figure 2 The specific implementation of each step of the illustrated embodiment is described in detail:

[0046] This step uses the pulse Doppler mode, in which the long-distance ranging accuracy of the radar and the radar range are an irreconcilable contradiction. To improve the ranging accuracy, the radar introduces the linear frequency modulation signal into the pulse Doppler system, but the intra-pulse modulation of the linear frequency modulation signal has a very high signal bandwidth, resulting in higher hardware requirements for the radar receiver.

[0047] The pulse Doppler system uses LFM signals. In order to improve the radar range resolution without affecting the radar range, pulse compression technology is required. In step S31, the data obtained from the FPGA is a signal with a greatly improved signal-to-noise ratio after pulse compression.

[0048] Next, in step S32, the signal is subjected to moving target detection, i.e., FFT processing, and accumulation in the carrier frequency domain, which can improve the signal-to-noise ratio.

[0049] In order to detect the echo of the moving target of interest in a complex clutter environment, the pulse Doppler radar must adopt a certain CFAR processing technology to prevent the false alarm probability of the radar from changing too much when the clutter environment changes, while ensuring a certain detection probability. Therefore, the large average CFAR (GOCFAR) and adaptive CFAR (VICFAR) are selected in step S33. For low-altitude environments with relatively simple clutter environments, a fixed GOCFAR is used, and for sea surfaces with complex and changeable clutter environments, VICAFR is used. VICFAR is a variable identification CFAR, and its principle is as follows: Figure 3 As shown. It determines the fluctuation characteristics of the background clutter in the two reference windows by calculating the statistical variance and statistical mean ratio of the first half reference window and the second half reference window, so as to select a suitable constant false alarm detector to detect the target. The detection threshold is equal to the product of the threshold coefficient and the estimated value of the background noise. The target is determined to be higher than the detection threshold. The statistics VI is used to determine whether the sampled data in the reference window comes from a uniform environment. The calculation method is as follows:

[0050]

[0051] In the above formula It represents the arithmetic mean of the amplitudes of N / 2 sampling units in the first half reference window or the second half reference window. is the variance, N is the number of sampling units, and xi is the amplitude of each sampling unit.

[0052] Statistics It is used to test whether the statistical means of the previous and next reference windows are the same, where represents the amplitude mean of each sampling unit in the first half of the reference window, Represents the amplitude mean of each sampling unit in the second half of the reference window.

[0053] Figure 3 A in the figure represents the first half reference window, B represents the second half reference window, ∑A, ∑B and ∑AB are the sums of the amplitude values ​​after square filtering in the first half reference window, the second half reference window and the entire reference window respectively; VI A and VI B are the change indexes of the front and rear reference windows respectively. N / 2 and α N The definition is as follows, when the false alarm probability is P FA The threshold factor corresponding to different reference window lengths under the condition of .

[0054]

[0055] The judgment method of clutter background is shown in the following two equations:

[0056]

[0057]

[0058] K VI and K MR is the set comparison threshold. When the value of the statistic VI is greater than the threshold K VI When , the reference window is considered uniform, otherwise it is non-uniform; when the mean ratio MR is less than or greater than K MR The means are considered different when , otherwise they are the same.

[0059] From the above analysis, it can be seen that by comparing the values ​​of VI and MR with the corresponding thresholds, it is possible to confirm whether the front and rear reference windows are uniform and whether the means of the front and rear reference windows are the same, and then the specific threshold can be selected according to the following scheme.

[0060]

[0061] GOCFAR is an improved algorithm of unit average CFAR, which can solve the problem of clutter edge and maintain a good false alarm probability in the clutter edge environment. It also performs well in a uniform environment. Figure 4 As shown in the figure, the reference unit is located on both sides of the detection unit, and the protection unit is mainly used in the case of a single target to prevent the target energy from leaking to the reference unit and affecting the detection effect; first, the amplitude mean of the reference window before and after the unit to be detected is calculated respectively, and then the larger of the two means is multiplied by the threshold factor to form a detection threshold, and the value above the detection threshold is judged as a target.

[0062] The point trace condensation process in step S34 is to perform corresponding processing on the point trace data that passes the threshold after the distance dimension constant false alarm detection. After the echo signal is subjected to moving target detection and constant false alarm detection, the obtained point trace contains the actual target point trace and the point trace caused by the false alarm. The point trace condensation technology is to distinguish and merge the target point trace according to the point trace resolution and merging criteria or methods, and filter out the virtual point trace at the same time, and finally condense the target point trace according to the maximum amplitude method to form a unique target point trace. The condensed point trace contains parameter information such as target distance, azimuth, amplitude, speed, etc. This information is uploaded to the host computer through the network port, and the target information is displayed through the display and control terminal.

[0063] The working process of the step frequency mode is as follows Figure 5 As shown, the specific steps include:

[0064] S41, receiving pulse compression echo data near the imaging target range unit after being processed by FPGA;

[0065] S42, calculating the speed compensation factor, performing speed compensation on the data, and multiplying the data of all frequency points under each range gate by the corresponding compensation factor;

[0066] S43, performing IFFT operation on the motion compensated data;

[0067] S44, extracting data targets from the data after IFFT by using the same distance selection method (only extracting the distance length information corresponding to the pulse width τ for each sampling point, and comparing it with the same distance units extracted from the adjacent sampling points, and taking the point with the larger amplitude as the extraction result.) is to sequentially arrange the extracted data to form a high-resolution one-dimensional range image of the target;

[0068] S45, identifying the target length according to the imaging result;

[0069] S46, sending the imaging result of the target and the target length to the host computer through the network port.

[0070] The following Figure 5 The specific implementation of each step of the illustrated embodiment is described in detail:

[0071] The stepped frequency mode uses a stepped frequency signal. The pulse stepped frequency signal has high resolution and greatly reduces the requirements for AD sampling. However, the stepped frequency signal is a Doppler sensitive signal. When imaging the target, it is necessary to use a pulse Doppler radar to accurately measure the speed, and then use the speed measurement information of the pulse Doppler radar to perform motion compensation on the stepped frequency system echo signal. In step S42, the compensation factor is calculated, and then the data of all frequency points under each range gate are multiplied by the compensation factor.

[0072]

[0073] Where f0 represents the radar starting frequency, Δf represents the radar step frequency, V s represents the estimated target speed, c represents the speed of light, T r It represents the pulse repetition period, and i represents the serial number of different step frequencies.

[0074] In step S44, target extraction is performed on the data after IFFT processing. The target extraction algorithm is to obtain a complete one-dimensional range image. It completes the following two tasks: ① Arrange the folded results according to the actual distance; ② Select the best result from the redundant information in different sampling points according to certain criteria to obtain the optimal one-dimensional range image that matches the actual situation.

[0075] First, the number of extended cycles is calculated for the result after IFFT, and the same distance selection method is used after the cycle extension. The selection method is to extract only the distance length information corresponding to the pulse width τ for each sampling point, and compare it with the same distance unit extracted from the adjacent sampling points, and take the point with the larger amplitude as the extraction result. The result after extraction is the one-dimensional range image of the target.

[0076]

[0077] in, f s represents the sampling frequency, B represents the bandwidth, N represents the number of frequency steps, and Δf represents the radar step frequency.

[0078] In step S45, the real length of the target is calculated based on the one-dimensional range image formed in S44. Due to noise interference, the imaging result is first subjected to sliding window processing. After appropriate sliding window averaging processing, the envelope of the imaging result has almost no change, but the noise fluctuations superimposed in the envelope tend to be gentle, which has little impact on subsequent analysis. After the target is extracted, when the amplitude is 11dB higher than the noise amplitude, it is considered to be a target. The number of continuous distance units occupied by the target in the one-dimensional range image is recorded, and the radial length of the target is obtained by multiplying it by the actual distance represented by each distance unit after refinement. Divide the radial length by |cos(azimuth-heading)| to obtain the actual length of the target.

[0079] The target imaging result in S44 and the actual length of the target in S45 are reported to the host computer through the network port and displayed through the display control.

[0080] like Figure 6 As shown, the overall workflow of the present invention is as follows:

[0081] First, the radar is powered on, and the DSP initializes each module. After the initialization is completed, the instructions sent by the host computer through the network port are parsed, and the working mode is selected according to the instructions. When working in pulse Doppler mode, the radar echo data is obtained through the SRIO interface with the FPGA, and moving target detection, constant false alarm detection, point trace condensation and other processing are performed. After the processing is completed, the target information is reported to the host computer through the network port, and the host computer displays the target. At the same time, the target is preliminarily classified according to the characteristic information such as the speed, RCS, and echo amplitude of the target. The speed of the ship is slow, the RCS is large, and the echo amplitude is high. The speed of the drone is fast, the RCS is small, and the echo amplitude is low. Targets with RCS greater than 1 square meter or speed less than 8m / s and target amplitude higher than the average noise by more than 20dB are classified as ships, and other targets are classified as drones. If a target is classified as a ship, the DSP switches to the stepped frequency mode at this time. According to the target speed detected by the pulse Doppler mode, the speed compensation is first performed, and then the IFFT operation is performed. The data after IFFT is then extended periodically, and the target is extracted using the same distance selection method. The extracted result is the one-dimensional range image of the target. The number of continuous distance units occupied by the target in the one-dimensional range image is recorded, and the radial length of the target is obtained by multiplying it by the actual distance represented by each distance unit. The radial length is divided by |cos(azimuth-heading angle)| to obtain the actual length of the target. The imaging results and the actual length of the target are uploaded to the host computer for display. After the imaging is completed, if a command is issued, it switches to the working mode specified by the command. If no command to continue imaging is issued, it automatically switches back to the pulse Doppler mode. If the target is classified as a drone, it always works in the pulse Doppler mode to track the drone in real time. At the same time, the micro-Doppler method can be used to further extract features from drone targets. Therefore, the display of the host computer can be used to determine whether the target at sea is a ship or a drone. If it is a ship, the size of the ship can be preliminarily determined. If it is a drone, the track information of the drone can be accurately obtained, thereby realizing real-time monitoring of the maritime situation.

[0082] Through the description of the above implementations, those skilled in the art can easily understand that the example implementations described here can be implemented by software, or by combining software with necessary hardware.

[0083] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0084] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for processing sea multifunctional radar signals, characterized in that: The following steps are involved: 1) After the radar is powered on, it analyzes the instructions sent by the host computer and selects the working mode according to the instructions; when working in pulse Doppler mode, it goes to step 2); when working in stepped frequency mode, it goes to step 3); 2) Obtain radar echo data, perform moving target detection, constant false alarm detection and point trace condensation; classify the target according to its speed, RCS and echo amplitude. If the target is classified as an aerial target, proceed to step 4); if the target is classified as a ship, the radar switches to stepped frequency mode and proceeds to step 3); 3) Obtain radar echo data or target data detected by pulse Doppler mode and perform velocity compensation on the data; Perform IFFT operation on the data after velocity compensation; perform periodic extension on the data after IFFT, use the same distance selection method to extract the target, and obtain the one-dimensional range image of the target; Identify the target length based on the imaging results; 4) Receive the host computer command. If there is an imaging command, switch to the working mode specified by the command. The pulse Doppler mode goes to step 2), and the stepped frequency mode goes to step 3); if there is no imaging command, switch back to the pulse Doppler mode and go to step 2).

2. The method for processing sea multifunctional radar signals according to claim 1, characterized in that: The radar echo data of step 2) or step 3) is pre-processed with pulse compression.

3. The method for processing sea multifunctional radar signals according to claim 1, characterized in that: The step 2) performs FFT processing on the radar echo data to complete moving target detection.

4. The method for processing sea multifunctional radar signals according to claim 1, characterized in that: In the step 2), GOCFAR is used for constant false alarm detection in the low-altitude environment, and VICAFR is used for constant false alarm detection in the sea surface environment.

5. The method for processing sea multifunctional radar signals according to claim 1, characterized in that: In step 2), targets with an RCS greater than 1 square meter or a speed less than 8 m / s and a target amplitude 20 dB higher than the average noise are classified as ships, and other targets are classified as drones.

6. The method for processing sea multifunctional radar signals according to claim 1, characterized in that: In the step 2), targets with a height greater than a set threshold are classified as aerial targets, target features are further extracted, and time-frequency analysis is performed using multiple rounds of echo data. If the target meets the calibrated time-frequency features of a rotary-wing UAV, it is determined to be a rotary-wing UAV, otherwise it is determined to be an unknown aerial target.

7. The method for processing sea-view multifunctional radar signals according to claim 1, characterized in that: In step 2), if the target is classified as an aerial target, a micro-Doppler method is used to further extract target feature information.

8. The method for processing sea-view multifunctional radar signals according to claim 1, characterized in that: Step 3) Calculate the compensation factor Multiply the data of all frequency points under each range gate by the compensation factor to complete the speed compensation, where f0 represents the radar starting frequency, Δf represents the radar step frequency, Vs represents the estimated target speed, c represents the speed of light, Tr represents the pulse repetition period, and i represents the serial number of different step frequencies.

9. The method for processing sea-view multifunctional radar signals according to claim 1, characterized in that: In step 3), the number of continuous distance units occupied by the target in the one-dimensional distance image is recorded, and the number is multiplied by the actual distance represented by each distance unit to obtain the radial length of the target; the radial length is divided by |cos(azimuth angle - heading angle)| to obtain the actual length of the target.

10. The method for processing sea-view multifunctional radar signals according to claim 1, characterized in that: The step 3) uses the same distance selection method to extract the target, and only extracts the distance length information corresponding to the pulse width τ for each sampling point, and compares it with the same distance units extracted from the adjacent sampling points, and extracts the points with larger amplitudes as the extraction results. The extracted result is the one-dimensional distance image of the target.

Images