A method for suppressing rain and snow clutter in navigation radar based on clutter envelope

Abstract

The invention discloses a navigation radar rain and snow clutter suppression method based on clutter envelope. The method adopts a first-order model to iteratively update and calculate smooth echo data; adopts azimuth distance mean filtering to smooth echo data; adopts sliding window average power maximum and minimum weighted estimation of clutter power; further obtains a smoothed inner envelope of the clutter power estimation value, and weights it with the rain and snow clutter suppression strength to obtain a detection threshold; the invention performs target detection and rain and snow clutter suppression, can effectively reduce interference target shielding and clutter edge shielding, better retains target information, and obtains a better rain and snow clutter suppression effect.

Description

Technical Field

[0001] The invention relates to the technical field of marine radars, and in particular to a method for suppressing rain and snow clutter of a navigation radar based on clutter envelope. Background Art

[0002] Marine navigation radar is a key navigation device for ship navigation to avoid obstacles and collisions. Ships will encounter various complex weather conditions when sailing at sea. In rainy and snowy weather conditions, the atmospheric visibility is poor, and ship drivers cannot observe the surrounding navigation obstacles through visual or optical equipment. They rely heavily on navigation radar to ensure navigation safety. However, when the navigation radar detects the target, the radar echo will also be seriously affected by rain and snow clutter. For a long time, suppressing rain and snow clutter has been one of the key issues in marine radar signal processing and data processing.

[0003] While suppressing rain and snow clutter, the rain and snow clutter suppression algorithm often suppresses the target to a certain extent, resulting in a decrease in the probability of target detection and an increase in missed alarms, which still poses a safety threat to navigation safety. Rain and snow have the characteristics of uneven temporal and spatial distribution. Using a fixed or automatic suppression algorithm for rain and snow clutter often has the problem of poor applicability. The usual practice is to use a manually adjustable parameter method to suppress rain and snow.

[0004] The existing marine rain and snow clutter suppression algorithms mainly include median filtering, CFAR, etc. Each filtering method has its own shortcomings and cannot fully adapt to clutter suppression under various rain and snow weather conditions. By setting manually adjustable parameters, the adaptability of these methods can be enhanced.

[0005] In the existing marine radar rain and snow clutter suppression algorithm, two sliding windows are used to calculate the sliding window mean of the adjacent points of the target point in the first three scan lines and the sliding window mean of the adjacent points of the target point respectively. The rain and snow clutter estimation value is obtained by weight distribution. The amplitude of rain and snow clutter can be estimated more accurately. However, when suppressing rain and snow clutter, the target suppression degree is also large, and it is easily affected by the interference targets in the first three scans, resulting in excessive suppression.

[0006] And a rain and snow interference suppression method based on moving sum deviation and STC control. This method determines the deviation data length according to the range and suppression gain, and then determines the moving sum deviation coefficient. The moving sum deviation coefficient is used to weight the echo data, and the detection threshold is determined in combination with the STC curve to detect the target and suppress rain and snow clutter. However, this method has the problem that the rain and snow clutter suppression intensity for long-distance targets is not ideal. Summary of the invention

[0007] In order to overcome the defects and shortcomings of the prior art, the present invention provides a navigation radar rain and snow clutter suppression method based on clutter envelope, which performs target detection and rain and snow clutter suppression by iteratively calculating smoothed echo data through multiple scanning measurement values ​​and calculating the smoothed inner envelope of the clutter power estimation value. The method can suppress rain and snow clutter while minimizing the impact on the detection probability of the target.

[0008] The second object of the present invention is to provide a navigation radar rain and snow clutter suppression system based on clutter envelope.

[0009] A third object of the present invention is to provide a storage medium.

[0010] A fourth object of the present invention is to provide a computing device.

[0011] In order to achieve the above object, the present invention adopts the following technical solutions:

[0012] The present invention provides a navigation radar rain and snow clutter suppression method based on clutter envelope, comprising the following steps:

[0013] Preset rain and snow clutter suppression working parameters, wherein the working parameters include the working mode and suppression intensity of the navigation radar rain and snow clutter suppression;

[0014] Acquire the echo data of the current azimuth, perform information scanning on the pulse signal transmitted in each azimuth during the rotation period of the radar antenna, and obtain the echo data vector of each azimuth;

[0015] The first-order model is used to iteratively update the estimated value of the target echo intensity of the nth scan to obtain smooth echo data;

[0016] Calculate the mean of the sliding window before the i-th distance unit and the mean of the sliding window after the i-th distance unit respectively, perform weighted summation on the maximum and minimum values ​​of the mean of the front and rear sliding windows, and calculate the clutter power estimation value;

[0017] calculating a smoothed inner envelope of an estimate of clutter power;

[0018] The smoothed echo data is subjected to azimuth distance mean filtering,

[0019] The smoothed echo after mean filtering is used as the echo energy estimate, the detection threshold is calculated, and the target detection is performed based on the detection threshold. The detection threshold is calculated by the weighted sum of the clutter power smoothed inner envelope value and the rain and snow clutter suppression strength, and is proportional to the current rain and snow clutter suppression strength;

[0020] When the smoothed echo after mean filtering is greater than or equal to the detection threshold, it is determined that there is a target and the output echo is the original echo data. When the smoothed echo after mean filtering is less than the detection threshold, it is determined that there is no target and no echo data is output.

[0021] As a preferred technical solution, the first-order model is used to iteratively update the estimated value of the target echo intensity for the nth scan, specifically:

[0022]

[0023] w=0.8-k*a

[0024] Among them, y n (i) indicates the current echo data, Indicates the echo data of the previous circle. represents the estimated value of the echo strength, w is the echo data update weighting coefficient, which is adjusted according to the rain and snow clutter suppression strength and is proportional to the current input rain and snow clutter suppression coefficient, k is the manually adjustable rain and snow clutter suppression strength, and a is the normalization coefficient of the rain and snow clutter suppression strength.

[0025] As a preferred technical solution, the weighted sum of the maximum and minimum values ​​of the front and rear sliding window means is performed to calculate the clutter power estimation value, which is specifically expressed as:

[0026]

[0027]

[0028]

[0029] in, represents the estimated value of clutter power, represents the estimated value of the clutter power of the front sliding window, represents the estimated value of the clutter power of the rear sliding window, N_w represents the sliding window length, N_p represents the protection unit window length, i represents the sequence number of the i-th range unit, m is the sequence number of the data unit used for clutter power estimation calculation, k is the manually adjustable rain and snow clutter suppression strength, and b is the normalization coefficient of the rain and snow clutter suppression strength.

[0030] As a preferred technical solution, the smoothed inner envelope of the estimated value of the clutter power is calculated, and the specific calculation method is expressed as follows:

[0031] Estimated value of clutter power at a certain azimuth It is expressed as:

[0032]

[0033] calculate The smooth inner envelope of

[0034]

[0035]

[0036] Among them, N_max represents the serial number of the farthest distance unit, and rct represents the inner envelope variation coefficient.

[0037] As a preferred technical solution, the smoothed echo data is subjected to azimuth distance mean filtering, and the mean filtered data includes current distance unit echo data, adjacent front and rear distance unit echo data, and adjacent first two azimuth same distance unit echo data.

[0038] As a preferred technical solution, the detection threshold is specifically expressed as:

[0039] T n (i) = k*e*env n (i)+k*f

[0040] Among them, env n (i) represents the smoothed inner envelope value of the clutter power, k is the manually adjustable rain and snow clutter suppression strength, e is the normalization coefficient of rain and snow clutter suppression, and f is the scaling factor of rain and snow clutter suppression.

[0041] In order to achieve the above second purpose, the present invention adopts the following technical solutions:

[0042] A rain and snow clutter suppression system based on smoothed echo data and clutter power smoothed envelope threshold, comprising: a parameter preset module, an echo data acquisition module, a smoothed echo data module, a clutter power estimation value calculation module, a smoothed inner envelope calculation module, a mean filter module, a detection threshold calculation module, and a target detection module;

[0043] The parameter preset module is used to preset rain and snow clutter suppression working parameters, and the working parameters include the working mode and suppression intensity of the navigation radar rain and snow clutter suppression;

[0044] The echo data acquisition module is used to acquire the echo data of the current azimuth, and to perform information scanning on the pulse signal transmitted in each azimuth during the rotation period of the radar antenna to obtain the echo data vector of each azimuth;

[0045] The smoothed echo data module is used to iteratively update the estimated value of the echo intensity of the n-th scanning target using a first-order model to obtain smoothed echo data;

[0046] The clutter power estimation value calculation module is used to respectively calculate the mean of the sliding window before the i-th distance unit and the mean of the sliding window after the i-th distance unit, perform weighted summation on the maximum and minimum values ​​of the mean of the front and rear sliding windows, and calculate the clutter power estimation value;

[0047] The smoothed inner envelope calculation module is used to calculate the smoothed inner envelope of the estimated value of the clutter power;

[0048] The mean filter module is used to perform azimuth distance mean filtering on the smoothed echo data.

[0049] The detection threshold calculation module is used to calculate the detection threshold by taking the smoothed echo after mean filtering as the echo energy estimation, wherein the detection threshold is calculated by weighted sum of the clutter power smoothed inner envelope value and the rain and snow clutter suppression strength, and is proportional to the current rain and snow clutter suppression strength;

[0050] The target detection module is used to perform target detection based on the detection threshold. When the smoothed echo after mean filtering is greater than or equal to the detection threshold, it is determined that there is a target and the output echo is the original echo data. When the smoothed echo after mean filtering is less than the detection threshold, it is determined that there is no target and no echo data is output.

[0051] In order to achieve the third objective, the present invention adopts the following technical solutions:

[0052] A computer-readable storage medium stores a program, and when the program is executed by a processor, the method for suppressing rain and snow clutter based on smoothed echo data and clutter power smoothing envelope threshold is implemented.

[0053] In order to achieve the fourth objective, the present invention adopts the following technical solutions:

[0054] A computing device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the rain and snow clutter suppression method based on smoothed echo data and clutter power smoothing envelope threshold as described above is implemented.

[0055] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0056] (1) The present invention adopts a first-order model iterative update calculation. The greater the rain and snow clutter suppression strength, the greater the weight of the historical echo data used in the target echo intensity estimation value. Since the rain and snow clutter has a weak correlation between scans, but is not absolutely independent, the correlation also changes under different meteorological conditions. The method of dynamically adjusting the weighting coefficient can better adapt to different meteorological conditions. The echo data is smoothed and pre-processed to reduce the rain and snow clutter intensity, which can better retain the target intensity.

[0057] (2) The present invention calculates the estimated value of clutter power, and weights the selection between the larger value and the smaller value in the average power of the front and rear sliding windows. It can be manually controlled by the rain and snow clutter suppression strength, and then the inner envelope smoothing is performed as the clutter power estimation. It can not only reduce the impact of interference targets falling into the sliding window in multi-target scenarios, but also improve the shielding effect of clutter edges on weak targets.

[0058] (3) The present invention uses azimuth distance mean filtering on the smoothed echo data to eliminate the salt and pepper noise caused by the first-order iterative update and improve the rain and snow clutter suppression effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the rain and snow clutter suppression method based on smoothed echo data and clutter power smoothing envelope threshold of the present invention;

[0060] Figure 2 This is the original radar image without using the rain and snow clutter suppression algorithm;

[0061] Figure 3 This is a radar image after suppressing rain and snow clutter using the method of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] Example

[0064] like Figure 1 As shown, this embodiment provides a navigation radar rain and snow clutter suppression method based on clutter envelope, comprising the following steps:

[0065] Step 1: Determine rain and snow interference suppression working parameters, the working parameters include the working mode and suppression strength k of the navigation radar rain and snow clutter suppression. In this embodiment, the suppression strength k is set to k=50;

[0066] Step 2: Read the current azimuth echo data;

[0067] During the rotation period of the radar antenna, a pulse signal is transmitted in each azimuth for information scanning, and an echo data vector Y is obtained by scanning one azimuth. n , is the amplitude of the echo sampling point in the scanning azimuth;

[0068] Y n ={y n (1), y n (2), y n (3), …, y n (i), …, y n ((N_max))},

[0069] i={1,2,3,…,i,…,N_max{,

[0070] n represents the nth scanning cycle.

[0071] The navigation radar antenna rotates one circle and scans in 2048 azimuths, and obtains 4096 sampling data in each azimuth (corresponding to a range of 12 nautical miles). After scanning one circle, 2048 echo data vectors are obtained, and the length of each echo data vector is 4096, N_max=4096.

[0072] In the second round of radar scanning, at the 100th azimuth, an echo data vector Y is obtained 2 , expressed as:

[0073] Y 2 ={y 2 (1), y 2 (2), y 2 (3), …, y 2 ((4096))}

[0074] In the third round of radar scanning, at the 100th azimuth, an echo data vector Y is obtained. 3 , expressed as:

[0075] Y 3 ={y 3 (1), y 3 (2), y 3 (3), …, y 3 ((4096))}

[0076] Step 3: Based on the multiple scan measurement values ​​of the echo data unit, an estimated value of the target intensity of the echo data unit is formed to obtain smooth echo data.

[0077] The first-order model is used to iteratively update and calculate the estimated value of the target echo intensity of the nth scan, and the weighted data is the current echo data and the previous echo data;

[0078]

[0079] w=0.8-k*a

[0080] w is the echo data update weighting coefficient, which is adjusted according to the rain and snow clutter suppression strength and is proportional to the current input rain and snow clutter suppression coefficient. k is the manually adjustable rain and snow clutter suppression strength, and a is the normalization coefficient of the rain and snow clutter suppression strength. The greater the rain and snow clutter suppression strength k, the greater the weight of the historical echo data used in the target echo strength estimate. Rain and snow clutter are weakly correlated between scans, but they are not absolutely independent. Under different meteorological conditions, the correlation also changes. This method of dynamically adjusting the weighting coefficient can better adapt to different meteorological conditions.

[0081] In this embodiment, the first-order model is adopted to iteratively update and calculate the echo intensity at the 64th sampling point in the third scan.

[0082]

[0083] Step 4: Calculate the estimated value of clutter power, calculate the mean of the front and rear sliding windows, estimate the clutter energy, corresponding to the i-th range unit, the front sliding window clutter power estimate,

[0084]

[0085] Calculate the mean of the sliding window corresponding to the i-th distance unit, and estimate the clutter power of the sliding window.

[0086]

[0087] Perform weighted summation on the maximum and minimum values ​​of the front and rear sliding window averages to calculate the clutter power estimate:

[0088]

[0089] i represents the serial number of the i-th distance unit, m is the serial number of the data unit used for clutter power estimation calculation, represents the estimated value of clutter power, represents the estimated value of the clutter power of the front sliding window, represents the estimated value of the clutter power in the rear sliding window, N_w represents the sliding window length, N_p represents the protection unit window length, and the data of the protection unit needs to be eliminated when calculating the current clutter power estimate. k is the manually adjustable rain and snow clutter suppression strength, and b is the normalization coefficient of the rain and snow clutter suppression strength. The larger the rain and snow clutter suppression strength k, the more inclined to select the larger average power of the front and rear sliding windows. The calculation of the clutter power estimate is obtained by weighted selection between the larger or smaller values ​​in the average power of the front and rear sliding windows, which can be manually controlled by the rain and snow clutter suppression strength.

[0090] In this embodiment, the lengths of the front and rear sliding windows are both 16, the lengths of the front and rear protection units are 12, and the clutter power of the front sliding window is estimated at the 64th distance unit.

[0091]

[0092] Corresponding to the i-th distance unit, the sliding window clutter power is estimated,

[0093]

[0094] Calculate the clutter power estimate

[0095]

[0096] Repeat the third circle, the 100th azimuth, and the corresponding clutter power estimates of all sampling points are:

[0097]

[0098] Step 5: Calculate an estimate of the clutter power The smooth inner envelope of .

[0099] For a certain direction

[0100] N_max is the number of the farthest distance unit;

[0101] calculate The smooth inner envelope of .

[0102]

[0103] Inner envelope calculation method:

[0104]

[0105] Get env n (i), rct is the inner envelope variation coefficient.

[0106] In this embodiment, the clutter power estimation is calculated The inner envelope of is:

[0107]

[0108]

[0109] in:

[0110]

[0111] Get env 3 (i), inner envelope variation coefficient rct=1000.

[0112] Step 6: The smoothed echo data obtained in step S3 is subjected to azimuth distance mean filtering, specifically, the smoothed echo data of the 100th azimuth is subjected to azimuth distance mean filtering;

[0113] Step S3 obtains the smoothed echo data Then, mean filtering is performed. The mean filtering data is composed of the echo data of the current range unit, the echo data of the adjacent front and rear range units, and the echo data of the adjacent first two azimuths with the same range unit. A 5-unit mean filtering is performed, which is specifically expressed as:

[0114]

[0115] Indicates the echo amplitude of the unit at the same distance in the previous direction,

[0116] Indicates the echo amplitude of the same distance unit in the first two directions,

[0117] In this embodiment, i is 64, which is specifically expressed as:

[0118]

[0119] Indicates the 64th range unit echo at the 99th azimuth,

[0120] Indicates the 64th range unit echo at the 98th azimuth,

[0121] The purpose of the mean filtering process for the smoothed echo is to eliminate the salt and pepper noise caused by the first-order iterative update.

[0122] Step 7: Smoothed echo after mean filtering The detection threshold is obtained by weighted summing of the smoothed inner envelope value of the clutter power and the rain and snow clutter suppression strength. The weighted summation coefficient of the detection threshold is adjustable and is proportional to the current input rain and snow clutter suppression strength. In this embodiment, T n (i) Target detection for detection threshold:

[0123] T n (i) = k*e*env n (i)+k*f

[0124] k is the manually adjustable rain and snow clutter suppression strength, e is the normalization coefficient of rain and snow clutter suppression, and f is the scaling factor of rain and snow clutter suppression.

[0125]

[0126] Realize the suppression of rain and snow clutter.

[0127] In this embodiment, i is 64, which is specifically expressed as:

[0128] For smooth echo T 3 (64) is the threshold for target detection

[0129] T 64 (i)=0.5*env 3 (64)+50

[0130]

[0131] y 3 (64) is the original echo data of the 3rd scan, 100th azimuth, and 64th distance unit.

[0132] like Figure 2 and Figure 3 As shown, by comparison, it can be found that the rain and snow clutter in the original radar image almost covers the entire range of 6 nautical miles. After using the method of the present invention, the rain and snow clutter area is significantly suppressed, and the target image is clearer.

[0133] Example 2

[0134] The present embodiment provides a rain and snow clutter suppression system based on smoothed echo data and clutter power smoothed envelope threshold, including: a parameter preset module, an echo data acquisition module, a smoothed echo data module, a clutter power estimation value calculation module, a smoothed inner envelope calculation module, a mean filtering module, a detection threshold calculation module, and a target detection module;

[0135] In this embodiment, the parameter preset module is used to preset rain and snow interference suppression working parameters, and the working parameters include the working mode and suppression intensity of the navigation radar rain and snow clutter suppression;

[0136] In this embodiment, the echo data acquisition module is used to acquire the echo data of the current azimuth, and to perform information scanning on the pulse signal transmitted in each azimuth during the rotation period of the radar antenna to obtain the echo data vector of each azimuth;

[0137] In this embodiment, the smoothing echo data module is used to iteratively update the estimated value of the echo intensity of the n-th scanning target using a first-order model to obtain smoothed echo data;

[0138] In this embodiment, the clutter power estimation value calculation module is used to respectively calculate the mean of the sliding window before the i-th distance unit and the mean of the sliding window after the i-th distance unit, perform weighted summation on the maximum and minimum values ​​of the mean of the front and rear sliding windows, and calculate the clutter power estimation value;

[0139] In this embodiment, the smoothed inner envelope calculation module is used to calculate the smoothed inner envelope of the estimated value of the clutter power;

[0140] In this embodiment, the mean filtering module is used to perform azimuth distance mean filtering on the smoothed echo data.

[0141] In this embodiment, the detection threshold calculation module is used to calculate the detection threshold by taking the smoothed echo after mean filtering as the echo energy estimation, and the detection threshold is calculated by the weighted sum of the clutter power smoothed inner envelope value and the rain and snow clutter suppression strength, and is proportional to the current rain and snow clutter suppression strength;

[0142] In this embodiment, the target detection module is used to perform target detection based on the detection threshold. When the smoothed echo after mean filtering is greater than or equal to the detection threshold, it is determined that there is a target, and the output echo is the original echo data. When the smoothed echo after mean filtering is less than the detection threshold, it is determined that there is no target, and no echo data is output.

[0143] Example 3

[0144] This embodiment provides a storage medium, which may be a storage medium such as a ROM, RAM, a disk, or an optical disk. The storage medium stores one or more programs. When the program is executed by a processor, the rain and snow clutter suppression method based on smoothed echo data and a clutter power smoothing envelope threshold of Embodiment 1 is implemented.

[0145] Example 4

[0146] The present embodiment provides a computing device, which may be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer or other terminal device with a display function. The computing device includes a processor and a memory, and the memory stores one or more programs. When the processor executes the program stored in the memory, the rain and snow clutter suppression method based on smoothed echo data and clutter power smoothing envelope threshold of embodiment 1 is implemented.

[0147] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A navigation radar rain and snow clutter suppression method based on clutter envelope, characterized in that: The steps include: Presetting rain and snow clutter suppression working parameters, wherein the working parameters include the working mode and suppression intensity of the rain and snow clutter suppression of the navigation radar; Acquire the echo data of the current azimuth, perform information scanning on the pulse signal transmitted in each azimuth during the rotation period of the radar antenna, and obtain the echo data vector of each azimuth; The first-order model is used to iteratively update the estimated value of the target echo intensity of the nth scan to obtain smooth echo data; Calculate the mean of the sliding window before the i-th distance unit and the mean of the sliding window after the i-th distance unit respectively, perform weighted summation on the maximum and minimum values ​​of the mean of the front and rear sliding windows, and calculate the clutter power estimation value; calculating a smoothed inner envelope of an estimate of clutter power; The smoothed echo data is subjected to azimuth range mean filtering, The smoothed echo after mean filtering is used as the echo energy estimate, the detection threshold is calculated, and the target detection is performed based on the detection threshold. The detection threshold is calculated by the weighted sum of the clutter power smoothed inner envelope value and the rain and snow clutter suppression strength, and is proportional to the current rain and snow clutter suppression strength; When the smoothed echo after mean filtering is greater than or equal to the detection threshold, it is determined that there is a target and the output echo is the original echo data. When the smoothed echo after mean filtering is less than the detection threshold, it is determined that there is no target and no echo data is output.

2. The method for suppressing rain and snow clutter of a navigation radar based on clutter envelope according to claim 1 is characterized in that: The first-order model is used to iteratively update the estimated value of the target echo intensity for the nth scan, specifically: w=0.8-k*a Among them, y n (i) indicates the current echo data, Indicates the echo data of the previous cycle. represents the estimated value of the echo strength, w is the echo data update weighting coefficient, which is adjusted according to the rain and snow clutter suppression strength and is proportional to the current input rain and snow clutter suppression coefficient, k is the manually adjustable rain and snow clutter suppression strength, and a is the normalization coefficient of the rain and snow clutter suppression strength.

3. The method for suppressing rain and snow clutter of a navigation radar based on clutter envelope according to claim 1, characterized in that: The weighted sum of the maximum and minimum values ​​of the front and rear sliding window averages is performed to calculate the clutter power estimation value, which is specifically expressed as: in, represents the estimated value of clutter power, represents the estimated value of the clutter power of the front sliding window, represents the estimated value of the clutter power of the rear sliding window, N_w represents the sliding window length, N_p represents the protection unit window length, i represents the sequence number of the i-th range unit, m is the sequence number of the data unit used for clutter power estimation calculation, k is the manually adjustable rain and snow clutter suppression strength, and b is the normalization coefficient of the rain and snow clutter suppression strength.

4. The method for suppressing rain and snow clutter of a navigation radar based on clutter envelope according to claim 1, characterized in that: The smoothed inner envelope of the estimated value of the clutter power is calculated in the following manner: Estimated value of clutter power at a certain azimuth It is expressed as: calculate The smooth inner envelope of Among them, N_max represents the serial number of the farthest distance unit, and rct represents the inner envelope variation coefficient.

5. The method for suppressing rain and snow clutter of navigation radar based on clutter envelope according to claim 1, characterized in that: The smoothed echo data is subjected to azimuth distance mean filtering, wherein the mean filtered data includes echo data of the current distance unit, echo data of adjacent front and rear distance units, and echo data of the adjacent first two azimuth distance units.

6. The method for suppressing rain and snow clutter of a navigation radar based on clutter envelope according to claim 1, characterized in that: The detection threshold is specifically expressed as: Tn(i)=k*e*envn(i)+k*f Among them, env n (i) represents the smoothed inner envelope value of the clutter power, k is the manually adjustable rain and snow clutter suppression strength, e is the normalization coefficient of rain and snow clutter suppression, and f is the scaling factor of rain and snow clutter suppression.

7. A navigation radar rain and snow clutter suppression system based on clutter envelope, characterized in that: include: Parameter preset module, echo data acquisition module, smooth echo data module, clutter power estimation value calculation module, smooth inner envelope calculation module, mean filter module, detection threshold calculation module, target detection module; The parameter preset module is used to preset rain and snow clutter suppression working parameters, and the working parameters include the working mode and suppression intensity of the navigation radar rain and snow clutter suppression; The echo data acquisition module is used to acquire the echo data of the current azimuth, and to perform information scanning on the pulse signal transmitted in each azimuth during the rotation period of the radar antenna to obtain the echo data vector of each azimuth; The smoothed echo data module is used to iteratively update the estimated value of the echo intensity of the n-th scanning target using a first-order model to obtain smoothed echo data; The clutter power estimation value calculation module is used to respectively calculate the mean of the sliding window before the i-th distance unit and the mean of the sliding window after the i-th distance unit, perform weighted summation on the maximum and minimum values ​​of the mean of the front and rear sliding windows, and calculate the clutter power estimation value; The smoothed inner envelope calculation module is used to calculate the smoothed inner envelope of the estimated value of the clutter power; The mean filter module is used to perform azimuth distance mean filtering on the smoothed echo data. The detection threshold calculation module is used to calculate the detection threshold by taking the smoothed echo after mean filtering as the echo energy estimation, wherein the detection threshold is calculated by weighted sum of the clutter power smoothed inner envelope value and the rain and snow clutter suppression strength, and is proportional to the current rain and snow clutter suppression strength; The target detection module is used to perform target detection based on the detection threshold. When the smoothed echo after mean filtering is greater than or equal to the detection threshold, it is determined that there is a target and the output echo is the original echo data. When the smoothed echo after mean filtering is less than the detection threshold, it is determined that there is no target and no echo data is output.

8. A computer-readable storage medium storing a program, characterized in that: When the program is executed by the processor, the navigation radar rain and snow clutter suppression method based on the clutter envelope as described in any one of claims 1 to 6 is implemented.

9. A computing device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the navigation radar rain and snow clutter suppression method based on the clutter envelope as described in any one of claims 1 to 6 is implemented.

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