Meteorological track combined suppression method

Abstract

The invention provides a meteorological track combined suppression method, and relates to the technical field of unmanned aerial vehicle navigation feature recognition, and the method comprises the steps: carrying out the meteorological track interference suppression based on a central moment and a normalized amplitude feature; the combined trace point signals are used as features, multi-dimensional features of the meteorological trace point proportion, the meteorological track direction and the wind speed after the track are formed, and the meteorological interference track is suppressed; setting a proportion threshold based on proportion statistics of meteorological plots in the track, and obtaining two states of normal display and meteorological track waiting for blanking processing according to a size relationship between an actual proportion and the proportion threshold; according to the technical key points, a signal dimension is obtained based on a central moment and a normalized amplitude, a classification processing condition is performed on the signal dimension, a track meteorological plot proportion is counted, and by means of a set proportion threshold, a normal display state and a meteorological track waiting blanking processing state can be obtained by comparing the actual proportion with the set proportion threshold. And the anti-interference capability of the unmanned aerial vehicle in the flight process can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of unmanned aerial vehicle navigation feature recognition, and in particular to a weather track joint suppression method. Background Art

[0002] With the introduction of airspace economy, the gradual relaxation of civil aviation control and the realization of drone material transportation and rescue, the requirements for airspace management will become increasingly higher. The prosperity of airspace economy faces the problems of drone prevention, bird strike prevention, military radar blind spot filling and low and slow operation in urban environment.

[0003] Radar is an effective means of low-altitude defense. Due to the strong clutter and interference in the complex urban environment, and the weak reflection echo of radar signals from small low-altitude targets, in order to ensure good target detection capability, radar needs to have good anti-interference ability and the ability to detect weak targets and identify them.

[0004] Unmanned aircraft are unmanned aircraft that are controlled by radio remote control equipment and self-contained program control devices, or are operated completely or intermittently autonomously by an onboard computer. Compared with manned aircraft, drones are often more suitable for tasks that are too "dull, dirty or dangerous", so they are widely used in aerial photography, agriculture, plant protection, micro selfies, express delivery, disaster relief, wildlife observation, monitoring infectious diseases, surveying and mapping, news reporting, power inspections, disaster relief, film and television shooting, creating romance, etc.

[0005] Unmanned aircraft are the same as manned aircraft. They can get good feedback on their own location and working status by configuring radar, remote sensing, radio and other systems. The inherent technology of radar has anti-interference characteristics, such as pulse compression, constant false alarm rate, low sidelobe antenna, etc.

[0006] However, with the increasing degree of urbanization, when drones are used in cities, the inherent means of existing radars can only reduce interference to a certain extent. Due to the complex urban environment, there are often complex electromagnetic environments and strong electromagnetic fields. The radar's anti-interference ability is not enough to overcome such situations, thus affecting the stable operation of the radar. Summary of the invention

[0007] In order to overcome the problem that when existing drones are used in cities, the inherent means of existing radars can only play a certain role in reducing interference. Due to the complex urban environment, there are often complex electromagnetic environments and strong electromagnetic fields. The anti-interference ability of the radar is not enough to overcome such situations, thereby affecting the stable operation of the radar. The embodiment of the present application provides a meteorological track joint suppression method, which obtains the shape information of the drone through the central moment, reflects the energy distribution of the drone based on the normalized amplitude, and describes the specific state of the drone during flight, so as to facilitate the extraction of signal dimension features during the flight of the drone in the method, thereby providing support for subsequent judgments;

[0008] At the same time, the direction of the meteorological point track is set according to the overall situation of the track image, the signal dimension is obtained based on the central moment and normalized amplitude, and the meteorological attributes of the point track are judged by classifying the signal dimension. After the statistics of the meteorological point tracks, the proportional threshold is set. By comparing the actual proportion with the actual proportional threshold, two states of normal display and meteorological track waiting for blanking processing can be obtained, which is convenient for improving the anti-interference ability of the UAV during flight on the basis of traditional radar anti-interference technology.

[0009] The technical solution adopted by the embodiment of the present application to solve the technical problem is:

[0010] A weather track joint suppression method comprises the following steps: suppressing weather track interference based on central moment and normalized amplitude characteristics; using joint point track signals as characteristics to form multi-dimensional characteristics of meteorological point track proportion, meteorological track direction and wind speed after the track, and suppressing the weather interference track; setting a proportion threshold based on the proportion statistics of meteorological point tracks in the track, and obtaining two states of normal display and weather track waiting for blanking processing according to the size relationship between the actual proportion and the proportion threshold.

[0011] In a possible implementation, the central moment reflects the shape information of the target with translation, rotation and scale invariant features, and the normalized amplitude reflects the energy distribution of the target based on the target shape information reflected by the central moment.

[0012] In a possible implementation, the central moment is normalized for the Doppler spectrum X=[X(1), X(2), ...X(N)] Among them, n=1, 2,…N.

[0013] In one possible implementation, based on The p-order central moment that normalizes the central moment is is the first-order origin moment, that is, the distribution center of the target Doppler spectrum.

[0014] In one possible implementation, the normalized amplitude is based on Amp nomalized=X(tar) / max(X) reflects the energy distribution of the target.

[0015] In a possible implementation, it also includes judging the direction of the weather interference track according to the overall situation of the track image, which direction includes the direction away from the station and the direction towards the station, and only moves in a single direction at a time.

[0016] In a possible implementation, the direction of the meteorological trace is also set, and the meteorological attribute of the trace is identified in combination with the classification processing of the signal dimension.

[0017] In a possible implementation, step 1: setting the direction of the meteorological point trace;

[0018] Step 2: Signal dimension feature extraction and judgment;

[0019] Step 3: Mark the meteorological points and make proportional statistics on the meteorological points;

[0020] Step 4: Refer to the proportion statistics of meteorological points in the track and set a threshold for the meteorological mark ratio;

[0021] Step 5: Normal display of point-trace weather or processing of weather interference track.

[0022] The beneficial effects of this application are:

[0023] First, in this scheme, the shape information of the UAV is obtained through the central moment, and the energy distribution of the UAV is reflected based on the normalized amplitude, which describes the specific state of the UAV during flight, making it easier to extract the signal dimension features of the UAV during flight in this method, thereby providing support for subsequent judgments;

[0024] Second, in this scheme, the direction of the meteorological point track is set by the overall situation of the track picture, the signal dimension is obtained based on the central moment and normalized amplitude, and the meteorological attributes of the point track are judged by the classification and processing of the signal dimension. After the statistics of the meteorological point tracks, the proportional threshold is set, and the actual proportion is compared with the actual proportional threshold. Two states of normal display and meteorological track waiting for blanking processing can be obtained, which is convenient for improving the anti-interference ability of UAVs during flight on the basis of traditional radar anti-interference technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process structure of the present invention in use. DETAILED DESCRIPTION

[0026] The technical solution in the embodiment of the present application is to solve the problems of the above-mentioned background technology, and the overall idea is as follows:

[0027] Embodiment 1:

[0028] This embodiment introduces a method for joint suppression of weather tracks. Figure 1 As shown, the meteorological track interference suppression is performed based on the central moment and normalized amplitude characteristics;

[0029] The central moment reflects the shape information of the target with translation, rotation and scale invariant features, and the normalized amplitude reflects the energy distribution of the target based on the target shape information reflected by the central moment, thereby describing the specific state of the UAV (mainly small UAVs with narrow-band radar and low speed) during flight;

[0030] Secondly, by using the central moment and normalized amplitude as the extraction of different physical features during the UAV's navigation process, when extracting sample data from the UAV's flight process, information that can reflect the essential characteristics of the target can be extracted from a sample data. The process of mapping the sample space from high dimension to low dimension can improve the recognition probability, thereby improving the suppression effect of meteorological interference tracks;

[0031] Next, the Doppler spectrum X = [X(1), X(2), ... X(N)] is normalized Wherein, n = 1, 2, ... N;

[0032] In the central moment, the Doppler spectrum X = [X(1), X(2), ...X(N)] is normalized Then the p-order central moment is

[0033] Where n = 1, 2, ... N, is the first-order origin moment (i.e., the distribution center of the target Doppler spectrum);

[0034] For UAVs with narrowband tracking radar, it is generally impossible to extract a high-resolution one-dimensional image of the target for classification and identification. The target can only be identified based on the target's energy distribution and target spectrum shape information. The shape information of the target is reflected by the central moment, and the energy distribution of the target is reflected by the normalized amplitude. In order to avoid the influence of automatic gain control (AGC) and other measures on the target energy distribution, the target peak is normalized by the main clutter intensity, as follows: Amp n omalized=X(tar) / max(X), which can reflect the energy distribution of the target.

[0035] By adopting the above technical solution:

[0036] The above design uses the central moment translation, rotation and scale-invariant features to support the work of obtaining the shape information of the UAV, reflects the energy distribution of the UAV based on the normalized amplitude, and describes the specific state of the UAV during flight, which facilitates the extraction of signal dimension features during the flight of the UAV in this method, thereby providing support for subsequent judgments.

[0037] Embodiment 2:

[0038] Based on Example 1, this example introduces the specific steps of a method for joint suppression of weather tracks:

[0039] S1: Determine the direction of the meteorological interference track based on the overall situation of the track image (the direction includes the direction away from the station and the direction towards the station, and only moves in one direction at a time);

[0040] S2: setting the direction of the meteorological trace, combining the classification processing of the signal dimension, and identifying the meteorological attributes of the trace;

[0041] S3: Mark the meteorological points and make proportional statistics on the track meteorological points;

[0042] Among them, since the signal dimensional characteristics of meteorological and real low-altitude small targets are very similar, in order to prevent the omission of low-altitude small real targets, the proportion of meteorological points in the track is counted and an appropriate proportion threshold is set. When the meteorological mark ratio is greater than the set threshold, the meteorological interference track is blanked. When the meteorological mark ratio is less than the set threshold, the track is displayed normally.

[0043] By adopting the above technical solution:

[0044] The above design sets the direction of the meteorological point track through the overall situation of the track picture, obtains the signal dimension based on the central moment and normalized amplitude, and classifies the signal dimension to determine the meteorological attributes of the point track. After counting the meteorological point tracks of the track, a proportional threshold is set, and the actual proportion is compared with the actual proportional threshold to support normal display of the track (the proportion of the meteorological mark is less than the set threshold) and waiting for the weather interference track to be blanked (the proportion of the meteorological mark is greater than the set threshold).

[0045] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from this are still within the scope of protection of the present invention.

Claims

1. A method for joint suppression of weather tracks, characterized in that: include: Based on the central moment and normalized amplitude characteristics, weather track interference suppression is performed; Taking the joint point track signal as the feature, the multi-dimensional features of meteorological point track ratio, meteorological track direction and wind speed after the track are formed, and the meteorological interference track is suppressed; Among them, based on the proportional statistics of meteorological points in the track, a proportional threshold is set, and according to the size relationship between the actual proportion and the proportional threshold, two states of normal display and meteorological track waiting for blanking processing are obtained.

2. A method for joint suppression of weather tracks as claimed in claim 1, characterized in that: The central moment reflects the shape information of the target with translation, rotation and scale invariant features, and the normalized amplitude reflects the energy distribution of the target based on the target shape information reflected by the central moment.

3. A weather track joint suppression method as claimed in claim 1, characterized in that: In the central moment, the Doppler spectrum X = [X(1), X(2), ...X(N)] is normalized Among them, n=1, 2,…N.

4. A weather track joint suppression method as claimed in claim 3, characterized in that: based on The p-order central moment that normalizes the central moment is in, is the first-order origin moment, that is, the distribution center of the target Doppler spectrum.

5. A weather track joint suppression method as claimed in claim 4, characterized in that: The normalized amplitude is based on Amp n omalized=X(tar) / max(X) reflects the energy distribution of the target.

6. A weather track joint suppression method as claimed in claim 1, characterized in that: It also includes judging the direction of the meteorological interference track based on the overall situation of the track image. This direction includes the direction away from the station and the direction towards the station, and it only moves in a single direction at a time.

7. A weather track joint suppression method as claimed in claim 1, characterized in that: It also includes setting the direction of the meteorological point trace, combining the classification processing of the signal dimension, and identifying the meteorological attributes of the point trace.

8. The working steps of a weather track joint suppression method according to any one of claims 1 to 7, characterized in that: include: Step 1: Set the direction of the weather trace; Step 2: Signal dimension feature extraction and judgment; Step 3: Mark the meteorological points and make proportional statistics on the meteorological points; Step 4: Refer to the proportion statistics of meteorological points in the track and set a threshold for the meteorological mark ratio; Step 5: Normal display of point-trace weather or processing of weather interference track.

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