CN114355308A - Navigation radar rain and snow clutter suppression method based on clutter envelope - Google Patents
Navigation radar rain and snow clutter suppression method based on clutter envelope Download PDFInfo
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Abstract
The invention discloses a navigation radar rain and snow clutter suppression method based on clutter envelope, which adopts a first-order model to iteratively update and calculate smooth echo data; filtering and smoothing echo data by adopting an azimuth distance mean value; weighting and estimating clutter power by adopting the maximum value and the minimum value of the average power of the sliding window; further solving the smooth inner envelope of the clutter power estimation value, and weighting the smooth inner envelope with the rain and snow clutter suppression intensity to obtain a detection threshold; the invention carries out target detection and rain and snow clutter suppression, can effectively lower interference target shielding and clutter edge shielding, better retains target information and obtains better rain and snow clutter suppression effect.
Description
Technical Field
The invention relates to the technical field of marine radars, in particular to a navigation radar rain and snow clutter suppression method based on clutter envelope.
Background
The marine navigation radar is key navigation equipment for avoiding obstacles and collisions in the navigation process of a ship, the ship can meet various complex weather environments in the marine navigation, the atmospheric horizontal visibility is poor under the weather conditions of rain and snow, a ship driver can not observe surrounding navigation obstacles through visual observation or optical equipment, and the navigation radar is particularly relied on to ensure the navigation safety. However, when the navigation radar detects a target, the radar echo is also seriously influenced by rain and snow clutter, and the suppression of the rain and snow clutter is one of the key problems of signal processing and data processing of the navigation radar for a long time.
The rain and snow clutter suppression algorithm can suppress the target to a certain degree while suppressing the rain and snow clutter, so that the target detection probability is reduced, the alarm leakage is increased, and the safety threat to the navigation safety can be still formed. The rain and snow have the characteristic of uneven time and space distribution, and the problem of poor applicability often exists by adopting a fixed or automatic suppression algorithm for rain and snow clutter. It is common practice to adopt a manually adjustable parameter method for rain and snow suppression.
The existing rain and snow clutter suppression algorithm for the ship mainly comprises median filtering, CFAR and the like. Each filtering method has own defects and cannot be completely adapted to clutter suppression under various rain and snow weather conditions, and the adaptability of the methods can be enhanced by setting parameters which can be manually adjusted.
In the existing rain and snow clutter suppression algorithm for the marine radar, two sliding windows are adopted, the average value of sliding windows of adjacent points of target points of the first three scanning lines and the average value of sliding windows of adjacent points of target points are calculated respectively, a rain and snow clutter estimation value is obtained through weight distribution, the amplitude of the rain and snow clutter can be estimated accurately, but the suppression degree of the target is large when the rain and snow clutter are suppressed, and the influence of interference on the target by the first three scanning lines is easily caused, so that excessive suppression is caused.
The method determines the length of deviation data according to the measuring range and the suppression gain, further determines a mobile summation deviation coefficient, uses the mobile summation deviation coefficient to carry out weighting processing on echo data, determines a detection threshold by combining an STC curve, detects a target and suppresses rain and snow clutters, but the method has the problem that the effect of suppressing the rain and snow clutters of a long-distance target is not ideal.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides a navigation radar rain and snow clutter suppression method based on clutter envelope.
The invention also provides a navigation radar rain and snow clutter suppression system based on clutter envelope.
A third object of the present invention is to provide a storage medium.
It is a fourth object of the invention to provide a computing device.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a navigation radar rain and snow clutter suppression method based on clutter envelope, which comprises the following steps:
presetting rain and snow clutter suppression working parameters, wherein the working parameters comprise a navigation radar rain and snow clutter suppression working mode and suppression strength;
acquiring current azimuth echo data, and performing information scanning on each azimuth transmitting pulse signal in a rotation period of a radar antenna to obtain an echo data vector of each azimuth;
iteratively updating the estimated value of the echo intensity of the nth scanning target by adopting a first-order model to obtain smooth echo data;
respectively calculating the average value of the front sliding window of the ith distance unit, carrying out weighted summation on the maximum value and the minimum value of the average value of the front sliding window and the rear sliding window corresponding to the average value of the rear sliding window of the ith distance unit, and calculating a clutter power estimation value;
calculating a smooth inner envelope of an estimated value of the clutter power;
performing azimuth distance mean filtering on the smoothed echo data,
calculating a detection threshold by taking the smooth echo after mean filtering as echo energy estimation, and carrying out target detection based on the detection threshold, wherein the detection threshold is obtained by weighted summation calculation of a clutter power smooth inner envelope value and a rain and snow clutter suppression intensity and is proportional to the current rain and snow clutter suppression intensity;
and when the smooth echo after the average filtering is larger than or equal to the detection threshold, judging that a target exists, outputting the echo as original echo data, and when the smooth echo after the average filtering is smaller than the detection threshold, judging that no target exists, and outputting no echo data.
As a preferred technical solution, the iteratively updating the estimated value of the echo intensity of the nth scanning target by using the first-order model specifically includes:
w＝0.8-k*a
wherein, yn(i) Is representative of the current echo data and,
the echo data of the last turn is represented,
and w is an estimated value representing the echo intensity, w is an echo data updating weighting coefficient, adjustment is carried out according to the rain and snow clutter suppression intensity, the adjustment is proportional to the current input rain and snow clutter suppression coefficient, k is the manually adjustable rain and snow clutter suppression intensity, and a is a normalization coefficient of the rain and snow clutter suppression intensity.
As a preferred technical solution, the weighted summation is performed on the maximum value and the minimum value of the front and rear sliding window mean values, and the clutter power estimation value is calculated, which is specifically expressed as:
wherein the content of the first and second substances,
the clutter power estimate is represented as an estimate of the clutter power,
representing an estimate of the power of the forward sliding window clutter,
the method comprises the steps of representing a rear sliding window clutter power estimated value, N _ w representing the sliding window length, N _ p representing the protection unit window length, i representing the serial number of the ith distance unit, m representing the serial number of a data unit used for clutter power estimation calculation, k representing the manually adjustable rain and snow clutter suppression strength, and b representing the normalization coefficient of the rain and snow clutter suppression strength.
As a preferred technical solution, the calculating of the smooth inner envelope of the estimation value of the clutter power is expressed by a specific calculation manner:
estimation of clutter power in a certain azimuth
Expressed as:
computing
Is smoothed by
Where N _ max represents the number of the farthest distance cell, and rct represents the inner envelope variation coefficient.
As a preferred technical solution, the smoothed echo data is subjected to azimuth distance mean filtering, and the mean filtering data includes current range unit echo data, adjacent front and rear range unit echo data, and adjacent front and rear two azimuth same range unit echo data.
As a preferred technical solution, the detection threshold is specifically expressed as:
Tn(i)＝k*e*envn(i)+k*f
wherein, envn(i) And expressing a clutter power smooth inner envelope value, k is the manually adjustable rain and snow clutter suppression strength, e is a normalization coefficient of the rain and snow clutter suppression, and f is a scaling coefficient of the rain and snow clutter suppression.
In order to achieve the second object, the invention adopts the following technical scheme:
a rain and snow clutter suppression system based on smoothed echo data and a clutter power smoothing envelope threshold, comprising: the device comprises a parameter presetting module, an echo data acquisition module, a smooth echo data module, a clutter power estimation value calculation module, a smooth inner envelope calculation module, a mean value filtering module, a detection threshold calculation module and a target detection module;
the parameter presetting module is used for presetting rain and snow clutter suppression working parameters, and the working parameters comprise a navigation radar rain and snow clutter suppression working mode and suppression strength;
the echo data acquisition module is used for acquiring echo data of the current azimuth, and performing information scanning on each azimuth transmitting pulse signal in the rotation period of the radar antenna to obtain an echo data vector of each azimuth;
the smooth echo data module is used for iteratively updating the estimated value of the echo intensity of the nth scanning target by adopting a first-order model to obtain smooth echo data;
the clutter power estimation value calculation module is used for calculating the mean value of the front sliding window of the ith distance unit respectively, carrying out weighted summation on the maximum value and the minimum value of the mean value of the front sliding window and the rear sliding window corresponding to the mean value of the rear sliding window of the ith distance unit, and calculating a clutter power estimation value;
the smooth inner envelope calculation module is used for calculating a smooth inner envelope of the estimation value of the clutter power;
the mean filtering module is used for carrying out azimuth distance mean filtering on the smoothed echo data,
the detection threshold calculation module is used for calculating a detection threshold by taking the smooth echo after mean value filtering as echo energy estimation, wherein the detection threshold is obtained by weighted summation calculation of a clutter power smooth inner envelope value and a rain and snow clutter suppression intensity and is proportional to the current rain and snow clutter suppression intensity;
the target detection module is used for carrying out target detection based on a detection threshold, when the smooth echo after mean value filtration is larger than or equal to the detection threshold, the target is judged to be present, the output echo is original echo data, and when the smooth echo after mean value filtration is smaller than the detection threshold, the target is judged to be absent, and no echo data is output.
In order to achieve the third object, the invention adopts the following technical scheme:
a computer-readable storage medium storing a program which, when executed by a processor, implements the method for suppressing rain and snow clutter based on smoothed echo data and a clutter power smoothing envelope threshold as described above.
In order to achieve the fourth object, the invention adopts the following technical scheme:
a computing device comprising a processor and a memory storing a processor executable program, the processor when executing the program stored in the memory implementing a method of rain and snow clutter suppression based on smoothed echo data and a clutter power smoothing envelope threshold as described above.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the method adopts a first-order model iterative update calculation, and the larger the rain and snow clutter suppression intensity is, the larger the weight of the historical echo data used in the target echo intensity estimation value is; the correlation of the rain and snow clutter is weak but not absolutely independent between scans, and the correlation is also changed under different meteorological conditions, so that the method for dynamically adjusting the weighting coefficient can better adapt to different meteorological conditions; echo data are smoothed, the intensity of rain and snow clutter is reduced through preprocessing, and the target intensity can be better reserved.
(2) The estimation value of the clutter power is calculated, the large value or the small value in the average power of the front sliding window and the rear sliding window is selected in a weighting mode, manual control can be carried out through the rain and snow clutter suppression strength, then inner enveloping smoothing is carried out, and the estimation value is used as the clutter power estimation, so that the influence of an interference target falling into the sliding window in a multi-target scene can be reduced, and the shielding effect of clutter edges on weak and small targets can be improved.
(3) The invention eliminates salt-and-pepper noise caused by first-order iterative update by using azimuth distance mean filtering on the smooth echo data, and improves the effect of inhibiting rain and snow clutter.
Drawings
FIG. 1 is a schematic flow chart of a method for suppressing rain and snow clutter according to the present invention based on smoothed echo data and a clutter power smoothing envelope threshold;
FIG. 2 is an original radar image without the use of a rain and snow clutter suppression algorithm;
FIG. 3 is a radar image with rain, snow and clutter suppressed using the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
As shown in fig. 1, the embodiment provides a method for suppressing rain and snow clutter of a navigation radar based on clutter envelope, which includes the following steps:
step 1: determining rain and snow interference suppression working parameters, wherein the working parameters comprise a navigation radar rain and snow clutter suppression working mode and a suppression strength k, and in the embodiment, the suppression strength k is set to be 50;
step 2: reading echo data of the current position;
in the rotation period of the radar antenna, information scanning is carried out on each azimuth transmitting pulse signal, and one azimuth is scanned to obtain an echo data vector YnThe amplitude of the echo sampling point in the scanning direction is used as the amplitude of the echo sampling point in the scanning direction;
Yn＝{yn(1)，yn(2)，yn(3)，…，yn(i)，…，yn((N_max))}，
i＝{1，2，3，…，i，…，N_max{，
n denotes the nth scan.
The navigation radar antenna rotates for a circle, scanning is carried out at 2048 directions, and 4096 sampling data (corresponding to 12 nautical miles) are obtained at each direction. 2048 echo data vectors are obtained by scanning one circle, each echo data vector is 4096 in length, and N _ max is 4096.
In the 2 nd turn of radar scanning, at the 100 th azimuth, 1 echo data vector Y is obtained2Expressed as:
Y2＝{y2(1)，y2(2)，y2(3)，…，y2((4096))}
in the 3 rd turn of radar scanning, at the 100 th azimuth, 1 echo data vector Y is obtained3Expressed as:
Y3＝{y3(1)，y3(2)，y3(3)，…，y3((4096))}
and step 3: and forming an echo data unit target intensity estimated value according to the multiple scanning measured values of the echo data unit to obtain smooth echo data.
Adopting a first-order model to iteratively update and calculate the estimated value of the echo intensity of the nth scanning target, wherein the weighted data are current echo data and previous circle echo data;
w＝0.8-k*a
w is an echo data updating weighting coefficient, and the weighting coefficient is adjusted according to the rain and snow clutter suppression intensity and is proportional to the current input rain and snow clutter suppression coefficient. k is the manually adjustable rain and snow clutter suppression intensity, and a is the normalization coefficient of the rain and snow clutter suppression intensity. The larger the rain and snow clutter suppression intensity k, the larger the weight of the historical echo data used in the target echo intensity estimate. The correlation of the rain and snow clutter is weak among scans, but the rain and snow clutter is not absolutely independent, and the correlation is also changed under different meteorological conditions.
In this embodiment, the echo intensity at the 64 th sampling point of the 3 rd scan is calculated using a first order model iterative update.
And 4, step 4: calculating the estimation value of clutter power, calculating the average value of front and back sliding windows, estimating clutter energy, estimating clutter power of the front sliding window corresponding to the ith distance unit,
calculating the average value of the back sliding window corresponding to the ith distance unit, estimating the clutter power of the back sliding window,
carrying out weighted summation on the maximum value and the minimum value of the mean values of the front sliding window and the rear sliding window, and calculating a clutter power estimation value:
i denotes the sequence number of the ith distance unit, m is the data unit sequence number used for the clutter power estimation calculation,
the clutter power estimate is represented as an estimate of the clutter power,
representing an estimate of the power of the forward sliding window clutter,
the method comprises the steps of representing a rear sliding window clutter power estimation value, representing the length of a sliding window by N _ w, representing the length of a protection unit window by N _ p, calculating the data of the protection unit to be removed when the current clutter power estimation is carried out, wherein k is manually adjustable rain and snow clutter suppression strength, and b is a normalization coefficient of the rain and snow clutter suppression strength. The larger the rain and snow clutter suppression strength k is, the larger the average power of the front and rear sliding window clutter is selected. The calculation of the clutter power estimation value is obtained by weighting and selecting a larger value or a smaller value in the average power of the front sliding window and the rear sliding window, and manual control can be performed through the rain clutter suppression strength and the snow clutter suppression strength.
In this embodiment, the length of the front sliding window and the length of the rear sliding window are both 16, the length of the front protection unit and the rear protection unit is 12, the 64 th distance unit, the clutter power estimation of the front sliding window,
corresponding to the ith distance unit, estimating clutter power of a post-sliding window,
computing clutter power estimates
Repeat circle 3, azimuth 100, corresponding clutter power estimates for all samples:
step 5, calculating the estimation value of the clutter power
The smoothed inner envelope.
For a certain orientation
N _ max is the number of the farthest distance element;
computing
The smoothed inner envelope.
The inner envelope calculation method comprises the following steps:
determination of envn(i) And rct is the inner envelope variation coefficient.
In this embodiment, a clutter power estimate is calculated
The inner envelope specifically comprises:
wherein:
determination of env3(i) The inner envelope variation coefficient rct is 1000.
Step 6: performing azimuth distance mean filtering on the smoothed echo data obtained in the step S3, specifically performing azimuth distance mean filtering on the smoothed echo data of the 100 th azimuth;
smoothed echo data obtained in step S3
And then carrying out mean value filtering, wherein mean value filtering data comprises current range unit echo data, adjacent front and back range unit echo data and adjacent front and back two azimuth same range unit echo data, and carrying out 5-unit mean value filtering, which is specifically represented as:
in this embodiment, i is 64, which is specifically expressed as:
for the average filtering processing of the smooth echo, the aim is to eliminate the salt-and-pepper noise caused by the first-order iterative update.
And 7: smoothed echo after mean filtering
For echo energy estimation, the detection threshold is obtained by weighted summation of a clutter power smooth inner envelope value and rain and snow clutter suppression intensity, the weighted summation coefficient of the detection threshold is adjustable and is proportional to the current input rain and snow clutter suppression intensity, and the embodiment uses Tn(i) Target detection is performed for the detection threshold:
Tn(i)＝k*e*envn(i)+k*f
k is the rain and snow clutter suppression intensity which can be adjusted manually, e is the normalization coefficient of the rain and snow clutter suppression, and f is the scaling coefficient of the rain and snow clutter suppression.
The suppression of the rain and snow clutter is realized.
In this embodiment, i is 64, which is specifically expressed as:
for smooth echo
By T3(64) Target detection for threshold
T64(i)＝0.5*env3(64)+50
y3(64) Is the raw echo data for the 3 rd scan, 100 th azimuth, 64 th range bin.
As shown in fig. 2 and fig. 3, the comparison shows that the rain and snow clutter of the original radar image almost covers 6 internal ranges of the sea, the rain and snow clutter area after the method is used is obviously inhibited, and the target image is clearer.
Example 2
The embodiment provides a rain and snow clutter suppression system based on smooth echo data and a clutter power smooth envelope threshold, which includes: the device comprises a parameter presetting module, an echo data acquisition module, a smooth echo data module, a clutter power estimation value calculation module, a smooth inner envelope calculation module, a mean value filtering module, a detection threshold calculation module and a target detection module;
in this embodiment, the parameter presetting module is configured to preset rain and snow interference suppression working parameters, where the working parameters include a rain and snow clutter suppression working mode and suppression strength of the navigation radar;
in this embodiment, the echo data acquisition module is configured to acquire echo data of a current azimuth, and perform information scanning on each azimuth transmission pulse signal in a rotation period of the radar antenna to obtain an echo data vector of each azimuth;
in this embodiment, the echo smoothing data module is configured to iteratively update the estimated value of the echo intensity of the nth scanning target by using a first-order model to obtain smoothed echo data;
in this embodiment, the clutter power estimation value calculation module is configured to calculate a mean value of a front sliding window of the ith distance unit, perform weighted summation on a maximum value and a minimum value of the mean value of the front sliding window and the rear sliding window corresponding to the mean value of a rear sliding window of the ith distance unit, and calculate a clutter power estimation value;
in this embodiment, the smoothed inner envelope calculation module is configured to calculate a smoothed inner envelope of the estimated value of the clutter power;
in this embodiment, the mean filtering module is configured to mean filter the smoothed echo data with azimuth and distance,
in this embodiment, the detection threshold calculation module is configured to calculate a detection threshold by using a smoothed echo after mean filtering as echo energy estimation, where the detection threshold is obtained by performing weighted summation calculation on a clutter power smooth inner envelope value and a rain and snow clutter suppression intensity, and is proportional to the current rain and snow clutter suppression intensity;
in this embodiment, the target detection module is configured to perform target detection based on a detection threshold, determine that there is a target when a mean-filtered smoothed echo is greater than or equal to the detection threshold, output an echo as original echo data, and determine that there is no target and no echo data is output when the mean-filtered smoothed echo is less than the detection threshold.
Example 3
The present embodiment provides a storage medium, which may be a storage medium such as a ROM, a RAM, a magnetic disk, an optical disk, etc., and the storage medium stores one or more programs, and when the programs are executed by a processor, the method for suppressing the clutter of rain and snow based on the smoothed echo data and the clutter power smoothing envelope threshold of embodiment 1 is implemented.
Example 4
The embodiment provides a computing device, which may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, or other terminal device with a display function, and the computing device includes a processor and a memory, where the memory stores one or more programs, and when the processor executes the programs stored in the memory, the method for suppressing rain and snow clutter based on smooth echo data and a clutter power smooth envelope threshold of embodiment 1 is implemented.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (9)
1. A navigation radar rain and snow clutter suppression method based on clutter envelope is characterized by comprising the following steps:
presetting rain and snow clutter suppression working parameters, wherein the working parameters comprise a navigation radar rain and snow clutter suppression working mode and suppression strength;
acquiring current azimuth echo data, and performing information scanning on each azimuth transmitting pulse signal in a rotation period of a radar antenna to obtain an echo data vector of each azimuth;
iteratively updating the estimated value of the echo intensity of the nth scanning target by adopting a first-order model to obtain smooth echo data;
respectively calculating the average value of the front sliding window of the ith distance unit, carrying out weighted summation on the maximum value and the minimum value of the average value of the front sliding window and the rear sliding window corresponding to the average value of the rear sliding window of the ith distance unit, and calculating a clutter power estimation value;
calculating a smooth inner envelope of an estimated value of the clutter power;
performing azimuth distance mean filtering on the smoothed echo data,
calculating a detection threshold by taking the smooth echo after mean filtering as echo energy estimation, and carrying out target detection based on the detection threshold, wherein the detection threshold is obtained by weighted summation calculation of a clutter power smooth inner envelope value and a rain and snow clutter suppression intensity and is proportional to the current rain and snow clutter suppression intensity;
and when the smooth echo after the average filtering is larger than or equal to the detection threshold, judging that a target exists, outputting the echo as original echo data, and when the smooth echo after the average filtering is smaller than the detection threshold, judging that no target exists, and outputting no echo data.
2. The method for suppressing the clutter of the rain and snow based on the smoothed echo data and the clutter power smoothing envelope threshold according to claim 1, wherein the estimation value of the echo intensity of the target scanned for the nth time is updated iteratively by using a first-order model, which specifically comprises:
w＝0.8-k*a
wherein, yn(i) Is representative of the current echo data and,
the echo data of the last turn is represented,
and w is an estimated value representing the echo intensity, w is an echo data updating weighting coefficient, adjustment is carried out according to the rain and snow clutter suppression intensity, the adjustment is proportional to the current input rain and snow clutter suppression coefficient, k is the manually adjustable rain and snow clutter suppression intensity, and a is a normalization coefficient of the rain and snow clutter suppression intensity.
3. The method for suppressing rain and snow clutter according to claim 1, wherein the weighted summation of the maximum and minimum of the mean of the forward and backward sliding windows is performed to calculate the clutter power estimation value, which is specifically expressed as:
wherein the content of the first and second substances,
the clutter power estimate is represented as an estimate of the clutter power,
representing an estimate of the power of the forward sliding window clutter,
representing a backward sliding window clutter power estimation value, N _ w representsThe length of a sliding window, N _ p represents the length of a protection unit window, i represents the serial number of the ith distance unit, m is the serial number of a data unit used for clutter power estimation calculation, k is the manually adjustable rain and snow clutter suppression strength, and b is a normalization coefficient of the rain and snow clutter suppression strength.
4. The method for suppressing rain and snow clutter based on smoothed echo data and a clutter power smoothing envelope threshold according to claim 1, wherein the smoothing inner envelope of the estimation value of the clutter power is calculated by:
estimation of clutter power in a certain azimuth
Expressed as:
computing
Is smoothed by
Where N _ max represents the number of the farthest distance cell, and rct represents the inner envelope variation coefficient.
5. The method of claim 1 wherein the smoothed echo data is filtered by mean azimuth-range filtering, the mean filtered data including current range bin echo data, adjacent forward and backward range bin echo data, and adjacent first two azimuth co-range bin echo data.
6. The method for suppressing rain and snow clutter based on smoothed echo data and a clutter power smoothing envelope threshold according to claim 1, wherein the detection threshold is specifically expressed as:
Tn(i)＝k*e*envn(i)+k*f
wherein, envn(i) And expressing a clutter power smooth inner envelope value, k is the manually adjustable rain and snow clutter suppression strength, e is a normalization coefficient of the rain and snow clutter suppression, and f is a scaling coefficient of the rain and snow clutter suppression.
7. A rain and snow clutter suppression system based on smoothed echo data and a clutter power smoothing envelope threshold, comprising: the device comprises a parameter presetting module, an echo data acquisition module, a smooth echo data module, a clutter power estimation value calculation module, a smooth inner envelope calculation module, a mean value filtering module, a detection threshold calculation module and a target detection module;
the parameter presetting module is used for presetting rain and snow clutter suppression working parameters, and the working parameters comprise a navigation radar rain and snow clutter suppression working mode and suppression strength;
the echo data acquisition module is used for acquiring echo data of the current azimuth, and performing information scanning on each azimuth transmitting pulse signal in the rotation period of the radar antenna to obtain an echo data vector of each azimuth;
the smooth echo data module is used for iteratively updating the estimated value of the echo intensity of the nth scanning target by adopting a first-order model to obtain smooth echo data;
the clutter power estimation value calculation module is used for calculating the mean value of the front sliding window of the ith distance unit respectively, carrying out weighted summation on the maximum value and the minimum value of the mean value of the front sliding window and the rear sliding window corresponding to the mean value of the rear sliding window of the ith distance unit, and calculating a clutter power estimation value;
the smooth inner envelope calculation module is used for calculating a smooth inner envelope of the estimation value of the clutter power;
the mean filtering module is used for carrying out azimuth distance mean filtering on the smoothed echo data,
the detection threshold calculation module is used for calculating a detection threshold by taking the smooth echo after mean value filtering as echo energy estimation, wherein the detection threshold is obtained by weighted summation calculation of a clutter power smooth inner envelope value and a rain and snow clutter suppression intensity and is proportional to the current rain and snow clutter suppression intensity;
the target detection module is used for carrying out target detection based on a detection threshold, when the smooth echo after mean value filtration is larger than or equal to the detection threshold, the target is judged to be present, the output echo is original echo data, and when the smooth echo after mean value filtration is smaller than the detection threshold, the target is judged to be absent, and no echo data is output.
8. A computer-readable storage medium storing a program which, when executed by a processor, implements a method for suppressing rain and snow clutter according to any one of claims 1 to 6 based on smoothed echo data and a clutter power smoothing envelope threshold.
9. A computing device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored by the memory, implements a method for rain and snow clutter suppression based on smoothed echo data and a clutter power smoothing envelope threshold according to any of claims 1 to 6.
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