Method for identifying rain clutter by navigation radar

Abstract

The invention discloses a method for identifying rain clutters by a navigation radar, which comprises the following steps of: identifying a large echo region, and filtering radar echoes by using a basic detection curve to obtain a large echo region image; large echo region feature value calculation, including average signal-to-clutter ratio feature value calculation and rectangularity feature value calculation, to obtain a corresponding average signal-to-clutter ratio feature value and rectangularity; according to the method, the rain clutter region is identified by using the average signal-to-clutter ratio characteristic value and the rectangularity according to a set threshold value, if the identified rain clutter region is subjected to subsequent statistical processing, a relatively accurate automatic rain clutter adjustment parameter can be given, and if the rain clutter region is not identified in the current scene, the automatic rain clutter adjustment parameter can be 0. According to the method for identifying the rain clutter through the navigation radar, the rain clutter echo can be correctly identified in the echo received by the radar, and then data support is provided for automatic giving of follow-up rain clutter adjusting parameters.

Description

Technical Field

[0001] The invention relates to the field of ship propulsion motor control, and in particular to a method for identifying rain clutter by a navigation radar based on a preset super spiral observer in the ship field. Background Art

[0002] The signal received by the navigation radar receiver contains not only the echo signal of the target, but also the signal reflected by rain clutter. In the current navigation radar, digital signal processing technology and manual adjustment of rain clutter buttons are used to filter out rain clutter as much as possible before the signal reaches the display device, so as to minimize the false alarm rate of rain clutter. With the improvement of automation and the increase in the demand for crew use, it is necessary to develop a method that can automatically give rain clutter adjustment parameters. However, through comparison, it is found that the rain clutter suppression in existing domestic and foreign navigation radars is mainly based on manual adjustment of rain clutter parameters. Although there is an automatic adjustment function for rain clutter parameters, when this function is used, there will be a problem of automatically giving rain clutter adjustment parameters when there is no rain clutter, resulting in the loss of ship target echo or land target echo. The main reason for this problem is that the rain clutter echo is not identified and processed. Summary of the invention

[0003] The object of the present invention is to provide a method for identifying rain clutter by a navigation radar to solve one or more of the above problems.

[0004] The present invention discloses a method for identifying rain clutter by a navigation radar, comprising: Identify large echo areas and use basic detection curves to filter radar echoes to obtain large echo area images; Calculation of characteristic values ​​in large echo regions, including calculation of average signal-to-clutter ratio characteristic values ​​and rectangularity characteristic values, to obtain corresponding average signal-to-clutter ratio characteristic values ​​and rectangularity; The rain clutter area is identified according to the set threshold.

[0005] In some implementations, the specific process of identifying the large echo region includes the following steps: Basic detection curve statistics: multiple grids under the radar polar coordinates are obtained based on the division of azimuth and distance. Each grid corresponds to an azimuth distance unit. The echo amplitude data of multiple sampling points within each grid range are averaged to obtain the grid average amplitude value. All grid average amplitude values ​​in each azimuth unit are linearly interpolated and smoothed to obtain the basic detection curve corresponding to all sampling points in each azimuth unit. Filter the echo data using the basic detection curve; Connected domain judgment: Connected domain processing is performed on all filtered grids to obtain multiple connected domains, and each connected domain is judged. If the maximum length of the connected domain is greater than the set threshold 1, and the maximum width of the connected domain is greater than the set threshold 2, then this connected domain is judged as a large echo area.

[0006] In some embodiments, the process of filtering echo data using the basic detection curve is: using the basic detection curve in the azimuth unit to filter the first echo data in the azimuth unit, comparing the basic curve value of the first sampling point in each grid and the echo amplitude value of the first sampling point of the first echo in each grid, if the echo amplitude value is greater than the basic detection curve value, then the mark position of the current grid is set to 1, otherwise it is set to 0, and the area set to 0 is filtered out.

[0007] In some embodiments, the process of calculating the average signal-to-clutter ratio eigenvalue is as follows: edge detection is first performed on the average amplitude values ​​of all grids, and the amplitude values ​​with large gradient changes are retained. Then, the grid area obtained by the area filling process is the target area. The average signal-to-clutter ratio eigenvalue of the large echo area is calculated by counting the number of grids in the large echo area and the number of grids in the target area of ​​the large echo area. Assuming that the number of grids in the large echo area is N and the number of grids in the target area of ​​the large echo area is M, the average signal-to-clutter ratio eigenvalue Snr is expressed as .

[0008] In some embodiments, the rectangularity characteristic value calculation process is as follows: the rectangularity reflects the approximation degree between the shape of the large echo area and the rectangle. Since the shape of the long strip elliptical area is usually a land coastal target or a bridge target, which is approximately rectangular, the rectangularity can be used as a distinctive feature to identify the target area. Assuming that the number of grids in the large echo area is N, and the maximum length and width corresponding to the large echo area are L and H respectively, the rectangularity Rec is expressed as .

[0009] In some embodiments, the process of identifying the rain clutter area according to the set threshold is that if the average signal-to-clutter ratio eigenvalue of the large echo area is less than the corresponding set threshold, and the rectangularity eigenvalue of the large echo area is less than the corresponding set threshold, then except for the target area in the large echo area, the remaining area in the large echo area is judged as the rain clutter area.

[0010] The advantage of the method for identifying rain clutter by a navigation radar described in the present invention is that it can correctly identify rain clutter echoes in the echoes received by the radar, thereby providing data support for the automatic provision of subsequent rain clutter adjustment parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1An example diagram of mesh division in some embodiments of the present invention; Figure 2 A schematic diagram of a basic monitoring curve in an orientation unit in some embodiments of the present invention; Figure 3 The original radar echo image in some embodiments of the present invention; Figure 4 In some embodiments of the present invention, Figure 3 The radar echo filtered image; Figure 5 In some embodiments of the present invention, Figure 4 The image after the radar echo connected domain is judged; Figure 6 In some embodiments of the present invention, Figure 5 Image after radar echo recognition; Figure 7 The present invention is a flowchart of a method for a navigation radar to identify rain clutter in some embodiments of the present invention. DETAILED DESCRIPTION

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. The contents and drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0013] In order to make the technical solutions and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0014] Combination Figure 7 In this embodiment, a method for identifying rain clutter by a navigation radar is described. Figure 3 Taking the original radar echo image shown in FIG. 1 as an example, a method for identifying rain clutter by a navigation radar is specifically described. The method for identifying rain clutter by a navigation radar includes the following steps: S1. Identification of large echo regions. The specific operation process of identifying large echo regions is as follows: S1.1. Basic detection curve statistics: Based on the division of azimuth and distance, multiple grids under the radar polar coordinates are obtained. Each grid corresponds to an azimuth unit and a distance unit (the grid division example is shown in the figure below). Figure 1As shown in the figure), the echo amplitude data of multiple sampling points within each grid range are averaged to obtain the grid average amplitude value, and all grid average amplitude values ​​within each azimuth unit are linearly interpolated and smoothed to obtain the basic detection curve corresponding to all sampling points in each azimuth unit (the schematic diagram of the basic detection curve within the range unit can be shown in the figure). Figure 2 As shown, in Figure 2 In the figure, curve A is the basic detection curve, and curve B is the echo amplitude data of a line); S1.2. Filter the echo data using the basic detection curve. Filter the first echo data in the azimuth unit using the basic detection curve in the azimuth unit. Compare the basic curve value of the first sampling point in each grid with the echo amplitude value of the first sampling point of the first echo in each grid. If the echo amplitude value is greater than the basic detection curve value, set the flag position of the current grid to 1, otherwise set it to 0. After the filtering operation, obtain Figure 4 The radar echo filtered image shown; S1.3, after filtering, the data with the flag bit of all grids as 1 is processed to obtain multiple connected domains, and each connected domain is judged. If the maximum length of the connected domain is greater than the set threshold 1, and the maximum width of the connected domain is greater than the set threshold 2, then this connected domain is judged as a large echo area. After this step, Figure 5 The radar echo filtered image shown is a plurality of large echo area images obtained after connected domain judgment processing (i.e., the image after radar echo connected domain judgment), wherein the maximum length described here refers to the maximum number of grid cells occupied at the distance where the connected domain is located, and the maximum width refers to the maximum number of grid cells occupied at the direction where the connected domain is located; S2. Calculation of characteristic values ​​of large echo regions. The specific operation process of calculating characteristic values ​​of large echo regions includes: The average signal-to-clutter ratio eigenvalue is calculated by first performing edge detection on the average amplitude value of all grids, retaining the amplitude value with large gradient change, and then the grid area obtained by area filling processing is the target area. The average signal-to-clutter ratio eigenvalue of the large echo area is calculated by counting the number of grids in the large echo area and the number of grids in the target area of ​​the large echo area. Assuming that the number of grids in the large echo area is N and the number of grids in the target area of ​​the large echo area is M, the average signal-to-clutter ratio eigenvalue Snr is expressed as ; Calculation of rectangularity eigenvalue: rectangularity reflects the approximation degree between the shape of the large echo area and a rectangle. Since the shape of the long elliptical area is usually a land coastal target or a bridge target, which is approximately rectangular, the rectangularity can be used as a distinguishing feature to identify the target area. Suppose the number of grids in the large echo area is N, and the maximum length and width of the large echo area are L and H respectively, then the rectangularity Rec is expressed as ; S3. Identify the rain clutter area according to the set threshold. The specific operation process may be: If the average signal-to-clutter ratio eigenvalue of the large echo area is less than the corresponding set threshold a, and the rectangularity eigenvalue of the large echo area is less than the corresponding set threshold b, then except for the target area in the large echo area, the rest of the area in the large echo area is judged as the rain clutter area, and then the Figure 6 The radar echo connected domain judgment image shown in the figure is the radar echo recognition image obtained after feature value judgment. The bright part in the image is the target recognition area, and the dark part is the rain clutter recognition area. The rain clutter area filtered out by the basic curve does not need to be recognized, because this part of the rain clutter will be filtered out by the automatic sea clutter detection curve based on the statistics of the clutter map, and the remaining rain clutter area that is not filtered out by the automatic sea clutter detection curve is the rain clutter area that needs to be recognized. By comparing the radar filtered image and the radar echo recognized image, it can be seen that the remaining rain clutter is accurately recognized. If the recognized rain clutter area is subjected to subsequent statistical processing, a more accurate automatic rain clutter adjustment parameter can be given. If no rain clutter area is recognized in the current scene, the automatic rain clutter adjustment parameter is 0.

[0015] The above undisclosed matters can all be implemented using existing technologies, so they will not be elaborated here.

[0016] For ordinary technicians, several similar variations and improvements can be made without departing from the creative concept of the present invention, and these should also be regarded as the protection scope of the present invention.

Claims

1. A method for identifying rain clutter by a navigation radar, characterized in that: The methods for navigation radar to identify rain clutter include: Identify large echo areas and use basic detection curves to filter radar echoes to obtain large echo area images; Calculation of characteristic values ​​of large echo regions, including calculation of average signal-to-clutter ratio characteristic values ​​and rectangularity characteristic values, to obtain corresponding average signal-to-clutter ratio characteristic values ​​and rectangularity; The rain clutter area is identified according to the set threshold.

2. A method for identifying rain clutter by a navigation radar according to claim 1, wherein: The specific process of identifying the large echo area includes the following steps: Basic detection curve statistics: multiple grids under the radar polar coordinates are obtained based on the division of azimuth and distance. Each grid corresponds to an azimuth distance unit. The echo amplitude data of multiple sampling points within each grid range are averaged to obtain the grid average amplitude value. All grid average amplitude values ​​in each azimuth unit are linearly interpolated and smoothed to obtain the basic detection curve corresponding to all sampling points in each azimuth unit. Filter the echo data using the basic detection curve; Connected domain judgment: Connected domain processing is performed on all filtered grids to obtain multiple connected domains, and each connected domain is judged. If the maximum length of the connected domain is greater than the set threshold 1, and the maximum width of the connected domain is greater than the set threshold 2, then this connected domain is judged as a large echo area.

3. A method for identifying rain clutter by a navigation radar according to claim 2, wherein: The process of filtering echo data using the basic detection curve is as follows: use the basic detection curve in the azimuth unit to filter the first echo data in the azimuth unit, compare the basic curve value of the first sampling point in each grid and the echo amplitude value of the first sampling point of the first echo in each grid, if the echo amplitude value is greater than the basic detection curve value, set the mark position of the current grid to 1, otherwise set it to 0, and filter out the area set to 0.

4. A method for identifying rain clutter by a navigation radar according to claim 2, wherein: The process of calculating the average signal-to-clutter ratio eigenvalue is as follows: firstly perform edge detection on the average amplitude values ​​of all grids, retain the amplitude values ​​with large gradient changes, and then obtain the grid area obtained by area filling processing as the target area. The average signal-to-clutter ratio eigenvalue of the large echo area is calculated by counting the number of grids in the large echo area and the number of grids in the target area of ​​the large echo area. Assuming that the number of grids in the large echo area is N and the number of grids in the target area of ​​the large echo area is M, the average signal-to-clutter ratio eigenvalue Snr is expressed as 。 5. A method for identifying rain clutter by a navigation radar according to claim 2, wherein: The rectangularity characteristic value calculation process is as follows: the rectangularity reflects the approximation degree between the shape of the large echo area and the rectangle. Since the shape of the long strip elliptical area is usually a land coastal target or a bridge target, which is approximately a rectangle, the rectangularity can be used as a differentiating feature to identify the target area. Assuming that the number of grids in the large echo area is N, and the maximum length and width corresponding to the large echo area are L and H respectively, the rectangularity Rec is expressed as 。 6. A method for identifying rain clutter by a navigation radar according to claim 2, wherein: The process of identifying the rain clutter area according to the set threshold is as follows: if the average signal-to-clutter ratio eigenvalue of the large echo area is less than the corresponding set threshold, and the rectangularity eigenvalue of the large echo area is less than the corresponding set threshold, then except for the target area in the large echo area, the remaining area in the large echo area is judged as the rain clutter area.

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