Method and device for extracting rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions

Abstract

The application provides a method and device for extracting rotor modulation characteristics of an aerial target under low signal-to-noise ratio conditions. The method comprises: preprocessing the obtained swept-frequency electromagnetic scattering characteristic parameters of the aerial target; imaging the preprocessed swept-frequency electromagnetic scattering characteristic parameters to obtain an RGB range image of the aerial target; converting the color components of the RGB range image to obtain a single-channel component grayscale image; performing multi-threshold segmentation on the single-channel component grayscale image to obtain a binary image of the rotor feature of the aerial target; multiplying the binary image of the rotor feature and the RGB range image to restore the rotor signal intensity information of the aerial target; and extracting the rotor modulation characteristics of the aerial target based on the rotor signal intensity information. The present scheme can quickly and accurately extract the rotor modulation characteristics of the aerial target under low signal-to-noise ratio conditions.

Description

Technical Field

[0001] This invention relates to the field of radar target parameter inversion, and in particular to a method and apparatus for extracting rotor modulation features of airborne targets under low signal-to-noise ratio conditions. Background Technology

[0002] The weak scattering characteristics (or low detectability characteristics) of radar targets refer to the overall strong scattering cross-section (RCS) of the radar target. Since the echo information of weak scattering characteristics is often obscured by noise, clutter, and other interference, the detection of weak signals remains a hot research topic and key issue in the radar field. In the actual detection and identification of targets, the rotor modulation parameters of airborne targets often contain rich target characteristic information, making rotor modulation parameter estimation an essential part of radar target identification.

[0003] When acquiring electromagnetic scattering data of aerial rotor targets, the data is often accompanied by a large amount of clutter interference, making it difficult to obtain pure electromagnetic scattering data similar to that under ideal simulation conditions. Related technologies typically use time-frequency analysis methods to transform the micro-motion signals in the radar echo into the time-frequency domain, combining the characteristics of the time and frequency domains to estimate the rotor's modulation parameters. While this time-frequency transformation method can effectively extract rotor information, the highly nonlinear relationship between the radar echo signal and the estimated rotor parameters makes it difficult to directly obtain the rotor modulation parameters from the radar echo alone. Furthermore, under conditions of strong clutter interference, the extraction of time-frequency information is difficult and inaccurate, leading to large errors in subsequent rotor modulation parameter extraction. In low signal-to-noise ratio (SNR) environments, coherent accumulation is often used to increase the echo accumulation time to improve the SNR of the echo signal, but this also reduces the efficiency of parameter estimation.

[0004] Therefore, based on the above problems, there is an urgent need for a new method and device for extracting the rotor modulation features of aerial targets under low signal-to-noise ratio conditions. Summary of the Invention

[0005] To address the problems of low efficiency, poor accuracy, and low effectiveness in extracting rotor modulation features under low signal-to-noise ratio conditions using traditional estimation methods, this invention provides a method and apparatus for extracting rotor modulation features of aerial targets under low signal-to-noise ratio conditions.

[0006] In a first aspect, embodiments of the present invention provide a method for extracting rotor modulation features of aerial targets under low signal-to-noise ratio conditions, the method comprising:

[0007] Preprocess the obtained swept-frequency electromagnetic scattering characteristic parameters of the aerial target;

[0008] The preprocessed swept electromagnetic scattering characteristic parameters are imaged to obtain the RGB range image of the aerial target;

[0009] The color components of the RGB distance image are converted to obtain a single-channel component grayscale image;

[0010] The single-channel component grayscale image is segmented using multiple thresholds to obtain a binarized image of the rotor features of the aerial target;

[0011] The rotor signal intensity information of the aerial target is recovered by multiplying the binarized image of the rotor features with the RGB distance image.

[0012] Based on the rotor signal strength information, the rotor modulation characteristics of the aerial target are extracted.

[0013] Secondly, embodiments of the present invention also provide a device for identifying the rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions, the device comprising:

[0014] The preprocessing unit is used to preprocess the obtained swept-frequency electromagnetic scattering characteristic parameters of the airborne target;

[0015] An imaging unit is used to image the preprocessed swept-frequency electromagnetic scattering characteristic parameters to obtain the RGB range image of the aerial target;

[0016] The conversion unit is used to convert the color components of the RGB distance image to obtain a single-channel component grayscale image.

[0017] A multi-threshold segmentation unit is used to perform multi-threshold segmentation on the single-channel component grayscale image to obtain a binarized image of the rotor features of the aerial target.

[0018] The recovery unit is used to recover the rotor signal intensity information of the aerial target based on the binarized image of the rotor features and the RGB distance image;

[0019] An extraction unit is used to extract the rotor modulation features of the aerial target based on the rotor signal strength information.

[0020] Thirdly, embodiments of the present invention also provide a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.

[0021] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0022] This invention provides a method and apparatus for extracting the rotor modulation features of an aerial target under low signal-to-noise ratio (SNR) conditions. By imaging the pre-processed swept-frequency electromagnetic scattering characteristic parameters of the aerial target, an RGB range image of the aerial target is obtained. The single-channel component grayscale image obtained by converting the color components of the RGB range image can effectively identify the feature regions and clutter regions of the aerial target. Then, the single-channel component grayscale image is segmented using multiple thresholds to obtain a binarized image of the aerial target's rotor features. Finally, the binarized image depicting the aerial target's rotor features is multiplied by the RGB range image to reconstruct the signal echo intensity information of the rotor features. Thus, the extraction of the rotor modulation features of an aerial target can be completed quickly and accurately under low SNR conditions. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a method for extracting rotor modulation features of aerial targets under low signal-to-noise ratio conditions, provided by an embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of the preprocessing process for the frequency sweep electromagnetic scattering characteristic parameters of an aerial target provided in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the preprocessing process for the frequency sweep electromagnetic scattering characteristic parameters of an aerial target provided in another embodiment of the present invention;

[0027] Figure 4 This is an RGB range image of an aerial target before preprocessing of swept electromagnetic scattering characteristic parameters provided by an embodiment of the present invention; in the figure, the horizontal axis represents time / s, and the vertical axis represents the dynamic aerial target range imaging / m under a fixed radar line-of-sight angle.

[0028] Figure 5 This is an RGB range profile of an aerial target after preprocessing the swept-frequency electromagnetic scattering characteristic parameters, as provided in an embodiment of the present invention.

[0029] Figure 6 This is a grayscale image obtained by passing the red component channel through an RGB distance image, as provided in an embodiment of the present invention.

[0030] Figure 7 This is a grayscale image obtained by passing an RGB distance image through the blue component channel, as provided in an embodiment of the present invention.

[0031] Figure 8 This is a grayscale histogram of the red channel obtained after converting the color components of an RGB distance image according to an embodiment of the present invention; wherein, the horizontal axis represents the pixel value and the vertical axis represents the number of that pixel value in the image;

[0032] Figure 9 This is a blue channel grayscale histogram obtained after converting the color components of an RGB distance image, as provided in an embodiment of the present invention.

[0033] Figure 10 This is a first binarized image obtained by performing multi-threshold segmentation on a grayscale image of the red channel, as provided in an embodiment of the present invention.

[0034] Figure 11 This is a second binarized image obtained by performing multi-threshold segmentation on a grayscale image of the red channel, as provided in an embodiment of the present invention.

[0035] Figure 12 This is a binarized image of the rotor modulation features of an aerial target provided in an embodiment of the present invention;

[0036] Figure 13 This is a rotor signal intensity image of an aerial target provided in an embodiment of the present invention;

[0037] Figure 14 This is a one-dimensional signal map constructed from the rotor signal intensity image of an aerial target, provided by an embodiment of the present invention; in the map, the horizontal axis represents the number of sampling points, and the vertical axis represents the signal intensity.

[0038] Figure 15 This is an image showing the result of extracting the rotor modulation features of an aerial target under a signal-to-noise ratio of 5dB, provided by an embodiment of the present invention.

[0039] Figure 16 This is a diagram showing the result of extracting the rotor modulation features of an aerial target under a signal-to-noise ratio of 10dB, provided by an embodiment of the present invention; in the diagram, the horizontal axis represents the time delay and the vertical axis represents the autocorrelation.

[0040] Figure 17 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention;

[0041] Figure 18 This is a structural diagram of a device for extracting the rotor modulation features of an aerial target under low signal-to-noise ratio conditions, provided by an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] Please refer to Figure 1 This invention provides a method for extracting rotor modulation features of aerial targets under low signal-to-noise ratio conditions. The method includes:

[0044] Step 100: Preprocess the obtained frequency-sweeping electromagnetic scattering characteristic parameters of the airborne target;

[0045] Step 102: Image the preprocessed swept-frequency electromagnetic scattering characteristic parameters to obtain the RGB range image of the aerial target;

[0046] Step 104: Convert the color components of the RGB distance image to obtain a single-channel component grayscale image;

[0047] Step 106: Perform multi-threshold segmentation on the single-channel component grayscale image to obtain a binarized image of the rotor features of the aerial target;

[0048] Step 108: Multiply the binarized image of the rotor features and the RGB distance image to recover the rotor signal intensity information of the aerial target;

[0049] Step 110: Extract the rotor modulation features of the aerial target based on the rotor signal strength information.

[0050] In this embodiment of the invention, the preprocessed frequency-sweeping electromagnetic scattering characteristic parameters of the airborne target are first imaged to obtain an RGB range image of the airborne target. The single-channel component grayscale image obtained by converting the color components of the RGB range image can effectively identify the feature region and clutter region of the airborne target. Then, the single-channel component grayscale image is segmented by multiple thresholds to obtain a binarized image of the rotor features of the airborne target. Finally, the binarized image depicting the rotor features of the airborne target is multiplied with the RGB range image to reconstruct the signal echo intensity of the rotor features. In this way, the extraction of the rotor modulation features of the airborne target can be completed quickly and accurately under low signal-to-noise ratio conditions.

[0051] For step 100:

[0052] In this embodiment, the swept-frequency electromagnetic scattering characteristic parameters of an aerial target hovering under a fixed radar line-of-sight angle are obtained by radar. These swept-frequency electromagnetic scattering characteristic parameters include all electromagnetic scattering characteristic parameters of both the radar target area and the non-radar target area. The radar target area includes the fuselage area and the rotor area, and the electromagnetic scattering intensity of different areas is different. At the same time, since the electromagnetic scattering characteristics of the rotor area are weak, the electromagnetic scattering intensity of the fuselage area and the non-radar target area usually interferes with its identification. Therefore, in this embodiment, it is considered to characterize the strong scattering characteristics of the fuselage area and the clutter scattering characteristics of the non-target area, and then use them to extract the modulation characteristic parameters of the rotor target.

[0053] In some implementations, the preprocessing method is Gaussian filtering.

[0054] Because the rotor feature information is weak due to the strong scattering points in the fuselage area, the detailed information of the obtained electromagnetic scattering characteristic parameters cannot be destroyed, or can only be destroyed to a small extent, when performing noise reduction processing. Therefore, in this embodiment, Gaussian filtering is used to preprocess the obtained swept-frequency electromagnetic scattering characteristic parameters. This not only overcomes the image blurring caused by the simple local averaging method, but also preserves the detailed information in the signal according to different weight information.

[0055] Specifically, assuming the swept-frequency electromagnetic scattering characteristic parameters are represented by letters, for example, a Gaussian filter template with a kernel size of 3×3 is used to preprocess the swept-frequency electromagnetic scattering characteristic parameters. When the signal passes through such... Figure 2 After applying the Gaussian filter template shown, the following is obtained: Figure 3 The filtered signal shown is, where, In this way, the nearby swept-frequency electromagnetic scattering characteristic parameters can be given higher weights, allowing the local weak modulation information of the signal to be preserved.

[0056] Regarding step 102:

[0057] In some implementations, the RGB distance image is obtained by performing an inverse Fourier transform on the preprocessed swept-frequency electromagnetic scattering characteristic parameters using a window function to obtain the RGB distance image.

[0058] In this embodiment, the RGB range image of the aerial target obtained by performing an inverse Fourier transform on the electromagnetic scattering characteristic parameters directly obtained using a window function is as follows: Figure 5 As shown, through comparison Figure 4 and Figure 5 As can be seen, without preprocessing the electromagnetic scattering characteristic parameters, the color energy of different regions of the target in the RGB range image of an aerial target is similar, making effective discrimination impossible. However, after Gaussian filtering preprocessing, the RGB range image can be effectively distinguished. Figure 5 Clearly identifying the color energy of each region of an aerial target is beneficial for subsequent extraction of rotor modulation features.

[0059] Regarding step 104:

[0060] In some embodiments, the single-channel grayscale image includes a red channel grayscale image and a blue channel grayscale image; wherein, the red component grayscale image is obtained by converting the RGB distance image through the red component channel, and the blue component grayscale image is obtained by converting the RGB distance image through the blue component channel.

[0061] In this embodiment, the RGB distance imaging results from the frequency sweep data are used. Figure 5 As can be seen, the intensity of scattering points varies within the imaging range due to the frequency-sweeping electromagnetic scattering characteristic parameters, resulting in different image colors. Combining this with the RGB image imaging principle, it can be known that the RGB range image is composed of the superposition of R (red channel component), G (green channel component), and B (blue channel component). Figure 5 As can be seen from the image, the more prominent yellow color represents the electromagnetic scattering characteristics of the radar target area (including the fuselage and rotor areas), while the more prominent cyan color represents the electromagnetic scattering characteristics of non-radar target areas. The electromagnetic scattering characteristics of the rotor area are relatively weak under the influence of these two colors, making it difficult to discern from the image. Figure 5 Effective identification is achieved. Further analysis of the RGB image imaging principle reveals that the R (red channel component) and G (green channel component) of the three primary colors superimpose to form yellow, while the B (blue channel component) and G (green channel component) superimpose to form cyan. Therefore, to quickly extract weak features from the rotor region, this implementation converts the RGB distance image into a single-color-channel grayscale image by passing the red and blue channels separately. This not only effectively distinguishes the rotor and non-rotor regions but also reduces memory usage, speeds up program execution, and improves the timeliness of feature extraction.

[0062] Regarding step 106:

[0063] In some implementations, step 106 includes:

[0064] Based on the grayscale images of the red channel and the blue channel, the grayscale histogram of the red channel and the grayscale histogram of the blue channel are obtained.

[0065] Based on the red channel grayscale image, blue channel grayscale image, red channel grayscale histogram, and blue channel grayscale histogram, the red channel grayscale image and blue channel grayscale image are respectively subjected to multi-threshold segmentation to obtain a first binarized image and a second binarized image; wherein, the first binarized image is used to characterize the electromagnetic scattering characteristics of the fuselage region and rotor region of the airborne target, and the second binarized image is used to characterize the electromagnetic scattering characteristics of the fuselage region of the airborne target;

[0066] The difference between the first binarized image and the second binarized image is used to obtain a binarized image of the rotor features of the aerial target.

[0067] In this embodiment, firstly, based on the red channel grayscale image and the blue channel grayscale image, the corresponding red channel grayscale histogram and blue channel grayscale histogram are obtained respectively (e.g., Figure 8 and 9 As shown), since the grayscale histogram can accurately represent the number of pixels of different gray levels in the red and blue channel grayscale images, this embodiment combines the red and blue channel grayscale images and their grayscale histograms, and segments the red and blue channel grayscale images separately by limiting multiple thresholds, thereby converting the red and blue channel grayscale images into binary images (such as...). Figure 10 and Figure 11 As shown, the binarized image can accurately depict the strong scattering point information of the airborne target fuselage and the weak scattering point information of the rotor, as well as only depict the strong scattering point information of the airborne target fuselage.

[0068] Therefore, in this embodiment, by subtracting the first binarized image depicting the strong scattering point information of the fuselage and the weak scattering point information of the rotor from the second binarized image depicting only the strong scattering point information of the fuselage, a binarized image depicting the weak scattering point information of the rotor can be obtained (e.g., Figure 12 (As shown); at the same time, by limiting the red channel grayscale image and the blue channel grayscale image to segment by limiting the multi-threshold information, the strong and weak scattering points can be accurately described on the basis of further filtering out clutter, avoiding the huge amount of computation for weak scattering feature extraction in traditional algorithms.

[0069] For steps 108 to 110:

[0070] In this embodiment, after obtaining the binarized image depicting the rotor features of the aerial target, the binarized image depicting the rotor features is multiplied by the RGB range image information formed by the preprocessed imaging using the following formula to reconstruct the echo signal domain.

[0071] P = P 2bw ×P guass

[0072] Where P represents the rotor signal intensity image data, P2bw A binarized image of the rotor features of an aerial target, P guass This is an RGB distance image; thus, the original pixel values ​​of the rotor feature parts can be reproduced, and the constructed rotor signal intensity image of the aerial target is as follows. Figure 13 As shown.

[0073] In some implementations, step 110 includes:

[0074] Based on the rotor signal intensity image, a one-dimensional signal map composed of the rotor signal intensity is constructed;

[0075] The one-dimensional signal graph is processed using an autocorrelation function to determine the rotor's rotation period;

[0076] The rotational speed of the rotor is calculated based on the rotor's rotation period to extract the rotor modulation features of the aerial target.

[0077] In this embodiment, in order to achieve rapid and accurate extraction of the rotor modulation features of an aerial target, the rotor signal intensity image (such as...) is first used as the basis for... Figure 13 The pixel strength information in the image (as shown) was used to construct a one-dimensional signal map composed of rotor signal strength (e.g., ...). Figure 14 As shown), the autocorrelation function is then used to process the one-dimensional signal graph to find the periodicity of signal length and signal strength in the cluttered signal (e.g., ...). Figure 15 and Figure 16 As shown in the figure, this enables the periodic feature inversion of the rotor's characteristic parts.

[0078] Specifically, the periodicity of signal length and signal strength in a one-dimensional signal graph is determined using the following autocorrelation function:

[0079]

[0080] In the formula, R x,x (n) represents the correlation of the function at time m and time m+n, where m is the discrete signal value in the one-dimensional signal graph, and n is the signal delay; for example, combining Figure 15 As can be seen, the autocorrelation function reaches its peak at 0s, with a correlation of 1, indicating the strongest correlation within the function itself. When it reaches 0.0134s, the autocorrelation function again shows a relatively strong correlation peak. Therefore, in this embodiment, it can be considered that... Figure 15 The rotation period of the rotor is 0.0134s.

[0081] In some embodiments, the rotational speed of the rotor is calculated using the following formula:

[0082]

[0083] In the formula, f is the rotational speed of the rotor, and T is the rotational period of the rotor.

[0084] In this embodiment, since the rotation period of the rotor is inversely related to the rotation speed of the rotor, the rotation speed of the rotor can be estimated using the above formula.

[0085] In summary, the embodiments of the present invention realize the extraction of weak modulation characteristic parameters of the rotor of an airborne target under low signal-to-noise ratio conditions, avoiding the problem of reduced inversion timeliness caused by improving the signal-to-noise ratio through time accumulation in related technologies.

[0086] like Figure 17 , Figure 18 As shown, this embodiment of the invention provides a device for identifying the rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions. The device embodiment can be implemented through software, hardware, or a combination of both. From a hardware perspective, such as... Figure 17 The diagram shown is a hardware architecture diagram of a computing device for identifying the rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions, as provided in an embodiment of the present invention. (Except for...) Figure 17 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 18 As shown, a device in a logical sense is formed by the CPU of its computing device reading the corresponding computer program from the non-volatile memory into memory and running it. This embodiment provides a device for identifying the rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions. The device includes:

[0087] The preprocessing unit 301 is used to preprocess the obtained frequency-sweeping electromagnetic scattering characteristic parameters of the airborne target;

[0088] Imaging unit 302 is used to image the preprocessed swept electromagnetic scattering characteristic parameters to obtain the RGB range image of the aerial target;

[0089] The conversion unit 302 is used to convert the color components of the RGB distance image to obtain a single-channel component grayscale image.

[0090] The multi-threshold segmentation unit 304 is used to perform multi-threshold segmentation on the single-channel component grayscale image to obtain a binarized image of the rotor features of the aerial target.

[0091] The recovery unit 305 is used to recover the rotor signal intensity information of the aerial target based on the binarized image of the rotor features and the RGB distance image;

[0092] Extraction unit 306 is used to extract the rotor modulation features of the aerial target based on the rotor signal strength information.

[0093] In one embodiment of the present invention, the preprocessing unit 301 preprocesses the swept-frequency electromagnetic scattering characteristic parameters of the airborne target obtained by Gaussian filtering.

[0094] In one embodiment of the present invention, the RGB distance image in the imaging unit 302 is obtained by imaging in the following manner: using a window function to perform an inverse Fourier transform on the preprocessed swept-frequency electromagnetic scattering characteristic parameters to obtain the RGB distance image.

[0095] In one embodiment of the present invention, in the conversion unit 302, the single-channel grayscale image includes a red channel grayscale image and a blue channel grayscale image; wherein, the red component grayscale image is obtained by converting the RGB distance image through the red component channel, and the blue component grayscale image is obtained by converting the RGB distance image through the blue component channel.

[0096] In one embodiment of the present invention, in the multi-threshold segmentation unit 304, the binarized image of the aerial target rotor features is obtained in the following manner:

[0097] Based on the grayscale images of the red channel and the blue channel, the grayscale histogram of the red channel and the grayscale histogram of the blue channel are obtained.

[0098] Based on the red channel grayscale image, blue channel grayscale image, red channel grayscale histogram, and blue channel grayscale histogram, the red channel grayscale image and blue channel grayscale image are respectively subjected to multi-threshold segmentation to obtain a first binarized image and a second binarized image; wherein, the first binarized image is used to characterize the electromagnetic scattering characteristics of the fuselage region and rotor region of the airborne target, and the second binarized image is used to characterize the electromagnetic scattering characteristics of the fuselage region of the airborne target;

[0099] The difference between the first binarized image and the second binarized image is used to obtain a binarized image of the rotor features of the aerial target.

[0100] In one embodiment of the present invention, when the extraction unit 306 extracts the rotor modulation features of the aerial target, it performs the following operations:

[0101] Based on the rotor signal intensity image, a one-dimensional signal map composed of the rotor signal intensity is constructed;

[0102] The one-dimensional signal graph is processed using an autocorrelation function to determine the rotor's rotation period;

[0103] The rotational speed of the rotor is calculated based on the rotor's rotation period to extract the rotor modulation features of the aerial target.

[0104] In one embodiment of the present invention, the rotational speed of the rotor in the extraction unit 306 is calculated using the following formula:

[0105]

[0106] In the formula, f is the rotational speed of the rotor, and T is the rotational period of the rotor.

[0107] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a device for identifying the rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions. In other embodiments of the present invention, a device for identifying the rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions may include more or fewer components than illustrated, or combine some components, or split some components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0108] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0109] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for extracting rotor modulation features of an aerial target under low signal-to-noise ratio conditions according to any embodiment of this invention.

[0110] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a method for extracting rotor modulation features of an aerial target under low signal-to-noise ratio conditions according to any embodiment of this invention.

[0111] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0112] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0113] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0114] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0115] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0116] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0117] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for extracting rotor modulation features of aerial targets under low signal-to-noise ratio conditions, characterized in that, include: Preprocess the obtained swept-frequency electromagnetic scattering characteristic parameters of the aerial target; The preprocessed swept electromagnetic scattering characteristic parameters are imaged to obtain the RGB range image of the aerial target; The color components of the RGB distance image are converted to obtain a single-channel grayscale image; the single-channel grayscale image includes a red channel grayscale image and a blue channel grayscale image; wherein, the red channel grayscale image is obtained by converting the RGB distance image through the red channel, and the blue channel grayscale image is obtained by converting the RGB distance image through the blue channel; The single-channel grayscale image is segmented using multiple thresholds to obtain a binarized image of the rotor features of the aerial target; The binarized image of the rotor features of the aerial target is obtained in the following way: Based on the grayscale images of the red channel and the blue channel, the grayscale histogram of the red channel and the grayscale histogram of the blue channel are obtained. Based on the red channel grayscale image, blue channel grayscale image, red channel grayscale histogram, and blue channel grayscale histogram, the red channel grayscale image and blue channel grayscale image are respectively subjected to multi-threshold segmentation to obtain a first binarized image and a second binarized image; wherein, the first binarized image is used to characterize the electromagnetic scattering characteristics of the fuselage region and rotor region of the airborne target, and the second binarized image is used to characterize the electromagnetic scattering characteristics of the fuselage region of the airborne target; The difference between the first binarized image and the second binarized image is used to obtain a binarized image of the rotor features of the aerial target; The rotor signal intensity information of the aerial target is recovered by multiplying the binarized image of the rotor features with the RGB distance image. Based on the rotor signal intensity information, a one-dimensional signal map composed of the rotor signal intensity information is constructed; The one-dimensional signal graph is processed using an autocorrelation function to determine the rotor's rotation period; The rotational speed of the rotor is calculated based on the rotor's rotation period to extract the rotor modulation features of the aerial target.

2. The method according to claim 1, characterized in that, The preprocessing method is Gaussian filtering.

3. The method according to claim 1, characterized in that, The RGB distance image is obtained by imaging in the following manner: using a window function to perform an inverse Fourier transform on the preprocessed swept-frequency electromagnetic scattering characteristic parameters to obtain the RGB distance image.

4. The method according to claim 1, characterized in that, The rotational speed of the rotor is calculated using the following formula: In the formula, The rotational speed of the rotor. The rotation period of the rotor is given.

5. A device for identifying the rotor modulation characteristics of aerial targets under low signal-to-noise ratio conditions, used to implement the method as described in any one of claims 1 to 4, characterized in that, include: The preprocessing unit is used to preprocess the obtained swept-frequency electromagnetic scattering characteristic parameters of the airborne target; An imaging unit is used to image the preprocessed swept-frequency electromagnetic scattering characteristic parameters to obtain the RGB range image of the aerial target; The conversion unit is used to convert the color components of the RGB distance image to obtain a single-channel grayscale image; A multi-threshold segmentation unit is used to perform multi-threshold segmentation on the single-channel grayscale image to obtain a binarized image of the rotor features of the aerial target; The recovery unit is used to multiply the binarized image of the rotor features and the RGB distance image to recover the rotor signal intensity information of the aerial target; An extraction unit is used to extract the rotor modulation features of the aerial target based on the rotor signal strength information.

6. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of any one of claims 1-4.

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