A pulse Doppler radar weather cloud clutter automatic filtering method

Abstract

The application discloses a kind of pulse Doppler radar weather cloud clutter automatic filtering methods, belong to radar data processing technical field.The method utilizes the statistical characteristics of weather cloud clutter on radar echo spectrum diagram, and the spatial position and motion characteristics of radar track in monitoring area, realizes the automatic identification and effective elimination of weather cloud clutter, reduces false track, and improves the detection performance of radar to "low, slow and small" target.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of pulse Doppler radar weather cloud clutter automatic filtering method, belong to radar data processing technical field. BACKGROUND

[0002] In recent years, "low, slow and small" target detection radar is widely used in airport bird detection, "black flight" unmanned aerial vehicle, and provides important guarantee for key point safety. "Low, slow and small" target refers to low-altitude, slow-speed small flight target, flight height is generally below 1000 meters, speed is slow, and radar reflection area is very small, so it is difficult to find, capture, deal with and respond. Considering that "low, slow and small" target has the characteristics of small RCS (Radar Cross Section), slow speed and low height, and the radar point track generated by weather cloud target also has the characteristics of "low, slow and small", if the target (bird or unmanned aerial vehicle) invades the airport in cloudy weather, the target point track and weather cloud point track detected by the radar are sent into the tracking processing flow together, which may cause unstable target track, poor accuracy, and even track error, so that the radar cannot provide effective monitoring information for users. In view of this situation, the radar clutter filtering technology can be used to filter the point track generated by clutter, so that the point track entering the target tracking processing flow is real and effective to the greatest extent, and the tracking performance of real target is improved.

[0003] In radar engineering, how to reduce the interference of background clutter and effectively detect the target in strong background clutter is a basic and important issue. Before the deployment and application of the radar, the clutter characteristics of the current geographical environment need to be processed in a targeted manner, so that the radar can adapt to the current environment and achieve the best use performance. However, for weather cloud clutter, the thickness, height, wind direction and wind speed of the cloud layer are changeable, which means that the use environment changes over time. Therefore, it is difficult to achieve self-adaptation by using traditional methods, so it is difficult to effectively process weather cloud clutter by using traditional methods. In addition, "low, slow and small" radar is usually a narrowband radar, which cannot image and identify the target. Therefore, it is difficult to automatically identify and filter weather cloud point track from "low, slow and small" point track, and the engineering demand is urgent. After investigation of public literature, no effective technology can solve this problem. SUMMARY

[0004] To solve the problem of automatic identification and filtering of weather cloud clutter point track, the present application utilizes the statistical characteristics of weather cloud clutter on radar echo spectrum graph, as well as the spatial position and motion characteristics of radar point track in the monitoring area, to realize automatic identification and effective elimination of weather cloud clutter, reduce false track, and improve the detection performance of radar on "low, slow and small" target.

[0005] A kind of pulse Doppler radar weather cloud clutter automatic filtering method, the method comprises:

[0006] Step one, judging whether there is weather cloud clutter in the current scanning area according to the spectrum feature of any radar spectrum map in the radar scanning area;

[0007] Step two, if there is weather cloud clutter, estimating the height, distance, speed and RCS range of the weather cloud by using the statistical feature of all the point tracks in the radar scanning area;

[0008] Step three, judging whether each point track in the radar spectrum is a point track produced by weather cloud clutter which needs to be filtered according to the estimated height, distance, speed and RCS range of the weather cloud.

[0009] Optionally, the step one comprises:

[0010] Supposing the echo power of any point track A in the radar spectrum map is P A ;

[0011] M spectrum blocks around the point track A are obtained, the point tracks in the M spectrum blocks which are similar to the point track A in echo power are counted, and the total number of the point tracks is N;

[0012] N point tracks are respectively sorted in descending order according to distance and speed, forming a distance sequence R N and a speed sequence V N , which constitute a sequence S N ;

[0013] The correlation coefficient p R|V| of the absolute value |V N | of the distance sequence R N and the speed sequence V N in the sequence S N is calculated.

[0014] When |p R|V| |>delta2, it is determined that weather cloud clutter exists, otherwise it is determined that weather cloud clutter does not exist, wherein delta2 is a preset correlation coefficient threshold.

[0015] Optionally, it is assumed that the point track similar to the point track A in echo power is called point track B, and the point track B satisfies |P B -P A |<delta1, P B is the echo power of the point track B, and delta1 is a preset power threshold.

[0016] Optionally, the correlation coefficient p R|V| is a Pearson correlation coefficient, a Kendall correlation coefficient or a Spearman correlation coefficient.

[0017] Optionally, the step two comprises:

[0018] acquiring a radar spectrum chart containing weather cloud clutter in a whole scanning area of a radar;

[0019] counting the number of point tracks contained in the radar spectrum chart in the whole scanning area, assuming S, a distance sequence R S , a height sequence H S , an RCS sequence RCS S , and a velocity sequence V S ;

[0020] respectively estimating the distance, velocity, height and RCS range of the weather cloud according to the following formulas:

[0021] R rg =[min(R S ),max(R S )];

[0022] V rg =[min(V S ),max(V S )];

[0023] H rg =[min(H S ),max(H S )];

[0024] RCS rg =[min(RCS S ),max(RCS S )].

[0025] Optionally, the step three comprises:

[0026] for any point track X in the whole scanning area of the radar, if R X ∈R rg , V X ∈V rg , H X ∈H rg , and RCS X ∈RCS rg , then X is considered as weather cloud clutter false alarm; wherein R X represents the distance value of the point track X, V X represents the velocity value of the point track X, H X represents the height value of the point track X, and RCS X represents the RCS value of the point track X.

[0027] Optionally, the value range of M is 4-9.

[0028] The application also provides an airport "low, slow and small" target detection monitoring method, which is used for the point track detected by a radar, adopts the automatic filtering method of the pulse Doppler radar weather cloud clutter to filter out the point track generated by the weather cloud clutter, and tracks the remaining point tracks as target point tracks of the "low, slow and small" target.

[0029] The application has the following advantages:

[0030] By using the statistical characteristics of the weather cloud clutter on the radar echo spectrum diagram and the spatial position and motion characteristics of the radar point track in the monitoring area, the automatic identification and effective elimination of the weather cloud clutter are realized, the false tracks are reduced, and the detection performance of the radar on the "low, slow and small" target is improved. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0032] Figure 1 It is a flow chart of the automatic filtering method of the pulse Doppler radar weather cloud clutter provided by the application.

[0033] Figure 2 It is an example diagram of weather clutter, in which the horizontal direction represents the speed, the speed absolute value increases from right to left, the vertical direction represents the distance, the distance increases from bottom to top, the dashed line frame in the diagram is artificially added for the convenience of description, the purple red points represent the echo points with high power, the power of the blue points is relatively low, and the power of the green points is the lowest. It can be obviously seen from the dashed line frame that, in a certain distance range, the echo points show the phenomenon that "the farther the distance, the greater the speed". DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the application more clear, the following will further describe the embodiments of the application in combination with the drawings.

[0035] The radar spectrum obtained by the "low, slow and small" target detection radar is usually a distance-speed two-dimensional spectrum, and other information of the point track such as height information and RCS information can be calculated according to the echo information of the radar.

[0036] Embodiment one:

[0037] The embodiment provides an automatic filtering method of a pulse Doppler radar weather cloud clutter, please refer to Figure 1 , the method comprises:

[0038] Step 1: Determine whether there is meteorological cloud clutter in the current scanning area based on the spectral characteristics of any radar spectrum map within the radar scanning area.

[0039] Step 2: If meteorological cloud clutter exists, the height, distance, velocity, and RCS range of the meteorological cloud are estimated using the statistical characteristics of all points within the radar scanning area.

[0040] Step 3: Based on the estimated height, distance, speed, and RCS range of the meteorological cloud, determine whether each point in the radar spectrum is a point generated by meteorological cloud clutter that needs to be filtered.

[0041] Example 2

[0042] This embodiment provides an automatic filtering method for meteorological cloud clutter from pulse Doppler radar. This method can be used in airport "low, slow, and small" target detection and monitoring scenarios to remove false alarms caused by meteorological cloud clutter from all points detected by the radar. The method includes three steps:

[0043] The first step is to determine whether meteorological cloud clutter exists within the radar scanning range; the second step is to estimate the characteristics of the meteorological clouds; and the third step is to filter the meteorological clouds. This radar scanning range refers to the airport's detection and monitoring range.

[0044] (1) The first step is to determine whether weather cloud clutter exists.

[0045] Within any spectrum obtained by radar detection of the airport's detection and monitoring range, acquire M spectral blocks surrounding any point A, and process them according to the following procedure, where the empirical value of M can be an integer from 4 to 9:

[0046] a) Count the total number N of points with power similar to point A. In M spectral blocks, the total number of points with power similar to point A is N; when point B in the spectrum |P B -P A When | < δ1, the two are considered to have similar power, where δ1 is a preset power threshold. The distance and velocity of each point can be directly read from the radar spectrum, such as... Figure 2 As shown.

[0047] b) Sort the N points in descending order of distance to form a sequence S. N , including distance sequence R N With velocity sequence V N .

[0048] c) Calculate S N Mid-distance sequence R N The absolute value of the velocity sequence |V N |correlation coefficient ρ R|V|Common correlation coefficients include Pearson correlation coefficient, Kendall correlation coefficient and Spearman correlation coefficient, which can be selected according to the situation; δ2 is a preset correlation coefficient threshold, when |ρ R|V| |>δ2, it is determined that the weather cloud clutter exists, otherwise it is determined that the weather cloud clutter does not exist.

[0049] d) If the weather cloud clutter does not exist, terminate the process; if the weather cloud clutter exists, proceed to the second step.

[0050] (2) The second step, weather cloud feature estimation

[0051] This step needs to estimate the distance range R rg , height range H rg , RCS range RCS rg and speed range V rg of the cloud clutter point track target.

[0052] Suppose that the entire scanning area of the radar is obtained according to the first step, and all the frequency spectrums containing weather cloud clutter are obtained, and after the statistics of all the frequency spectrums, S points are obtained, distance sequence R S , height sequence H S , RCS sequence RCS S and speed sequence V S are obtained; the ranges can be determined by the following methods:

[0053] R rg =[min(R S ),max(R S )];

[0054] V rg =[min(V S ),max(V S )];

[0055] H rg =[min(H S ),max(H S )];

[0056] RCS rg =[min(RCS S ),max(RCS S )];

[0057] (3) The third step: weather cloud filtering

[0058] When it is determined that the weather cloud clutter exists, when the radar detects a point track X, if it falls in the four ranges at the same time, i.e. R X ∈R rg , V X ∈V rg , HX ∈H rg , RCS X ∈RCS rg If X is considered as a point track generated by weather cloud clutter, the point track needs to be deleted; otherwise, X is considered as a target point track, and for the airport target detection process, the target tracking can be performed according to the point track.

[0059] To verify the effectiveness of the method, the radar spectrum diagram shown in FIG. 1 is taken as an example. Figure 2 The four spectrum blocks adjacent to any point track A are obtained, and the following process is performed:

[0060] a) Statistics N. In the four spectrum blocks, the total number of point tracks similar to P A is N=5.

[0061] b) The five points are sorted in descending order of distance to form a sequence S N .

[0062] c) The correlation coefficient p N of the distance sequence R N and the absolute value |V N | of the velocity sequence is calculated. RV Here, the Pearson correlation coefficient is used.

[0063] d) It is determined that there is weather cloud clutter, and the second step is entered.

[0064] (1) The second step is weather cloud feature estimation

[0065] The distance range R rg of the cloud clutter point track target is estimated as [955, 2115], the height range H rg is [282, 431], the RCS range RCS rg is [0.0001, 0.2], and the velocity range V rg is [6.1, 12.5].

[0066] (2) The third step is weather cloud filtering

[0067] When a point track X is detected in the radar scanning range, it is considered that X is a point track generated by weather cloud clutter if it falls in the four ranges, and the point track needs to be deleted.

[0068] Some steps in the embodiments of the application can be implemented by software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or a hard disk.

[0069] The above merely describes preferred embodiments of the present application, and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An automatic filtering method for meteorological cloud clutter using pulse Doppler radar, characterized in that, The method includes: Step 1: Determine whether there is meteorological cloud clutter in the current scanning area based on the spectral characteristics of any radar spectrum map within the radar scanning area. Step 2: If meteorological cloud clutter exists, the height, distance, velocity, and RCS range of the meteorological cloud are estimated using the statistical characteristics of all points within the radar scanning area. Step 3: Based on the estimated height, distance, speed, and RCS range of the meteorological cloud, determine whether each point in the radar spectrum is a point generated by meteorological cloud clutter that needs to be filtered. Step one includes: Let the echo power at any point A in the radar spectrum be... ; Get the area around point A M Each spectrum block, statistics M Let the total number of traces in the spectrum blocks that have echo power similar to trace A be . N ; Will N The points are sorted in descending order of distance to form a distance sequence. With velocity sequence , forming a sequence ; Calculate sequence Mid-distance sequence With velocity sequence absolute value correlation coefficient ; when If the weather clutter is present, it can be determined that it exists; otherwise, it is determined that it does not exist. The threshold is the preset correlation coefficient. Step two includes: Obtain radar spectrum map containing meteorological cloud clutter within the entire radar scanning area; Count the number of points contained in the radar spectrum map of the entire scanning area, assuming it is . Distance sequence height sequence RCS sequence velocity sequence ; Estimate the distance, velocity, altitude, and RCS range of the meteorological cloud using the following formulas: ； ； ； 。 2. The method according to claim 1, characterized in that, Assuming that a point with a similar echo power to point A is called point B, then point B satisfies the following conditions: , Let B be the echo power at point B. This is the preset power threshold.

3. The method according to claim 2, characterized in that, The correlation coefficient This refers to the Pearson correlation coefficient, Kendall correlation coefficient, or Spearman correlation coefficient.

4. The method according to claim 3, characterized in that, Step three includes: For any point X within the entire radar scanning area, if , , , If X is considered a false alarm due to meteorological cloud clutter, then X is considered a false alarm. This represents the distance value of point X. This represents the velocity value of point X. This represents the height value of point X. This represents the RCS value of point X.

5. The method according to claim 4, characterized in that, The area around mark A M In each spectrum block M The value range is 4-9.

6. A method for detecting and monitoring "low, slow, and small" targets at airports, characterized in that, The method described herein uses any one of the methods described in claims 1-5 to filter out the points detected by radar, thereby filtering out the points generated by meteorological cloud clutter and tracking the remaining points as target points of "low, slow and small" targets.

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