Helicopter radar pitch angle estimation method and device based on time-frequency characteristics

Abstract

The present invention relates to a method and device for estimating the pitch viewing angle of a helicopter radar based on time-frequency characteristics. A specific implementation method includes: establishing a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor to generate a rotor radar echo signal; performing a short-time Fourier transform on the rotor radar echo signal to obtain a rotor time-frequency image; extracting a time-frequency graph contour from the rotor time-frequency image using a Canny detection algorithm; estimating the rotor size under radar vision according to the time-frequency graph contour, and estimating the radar pitch viewing angle by combining the rotor size under radar vision with the actual size of the rotor. This implementation method can estimate the helicopter radar pitch viewing angle in real time, provide a basis for predicting the helicopter maneuvering direction, and solve the problems of the existing algorithm being complex, low in calculation efficiency, and unable to achieve real-time estimation.

Description

Technical Field

[0001] The invention relates to the technical field of radar target recognition, and in particular to a method and device for estimating the pitch and viewing angle of a helicopter radar based on time-frequency characteristics. Background Art

[0002] In modern and future wars, helicopters, transport propeller aircraft, jet fighters and other aerial targets each have their own role in the war, and helicopters often approach and attack targets at low altitudes, making it extremely difficult to track and destroy helicopters. Accurate estimation of helicopter radar's sight angle is the basis for determining its maneuvering direction, and then tracking and destroying it. Therefore, estimation of helicopter radar's pitch sight angle is of great significance.

[0003] The structure of helicopter targets is relatively complex. In addition to the overall motion characteristics of the flight, its radar cross section (RCS) echo also contains a lot of weak motion characteristic information. Micro-motion is caused by the movement of specific parts of the target and is a reflection of the subtle characteristics of the target. It can serve as an important basis for target classification and identification.

[0004] The existing target radar pitch angle estimation method is generally based on the RCS characteristic data of the target as a whole. The algorithm is relatively complex and the calculation efficiency is low. It is difficult to achieve real-time estimation of the pitch angle, which is limited in actual application on the battlefield. Therefore, a new method for real-time estimation of the pitch angle of helicopter radar is urgently needed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and device for estimating the pitch and direction angle of a helicopter radar based on time-frequency characteristics in view of the defects in the prior art.

[0006] In order to solve the above technical problems, the present invention provides a method for estimating the pitch angle of a helicopter radar based on time-frequency characteristics, comprising:

[0007] Step 1: Establish a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor to generate a rotor radar echo signal;

[0008] Step 2: Perform short-time Fourier transform on the rotor radar echo signal to obtain the rotor time-frequency image; wherein the rotor time-frequency image is expressed as <s(u)g * (ut),e -j2πfu >, S(u) is the rotor radar echo signal, g * (ut) is the complex conjugate narrow window function;

[0009] Step 3: Use the Canny detection algorithm to extract the time-frequency graph contour from the rotor time-frequency image;

[0010] Step 4: Estimate the size of the rotor as seen from the radar according to the time-frequency graph contour, and estimate the radar pitch angle by combining the size of the rotor as seen from the radar with the actual size of the rotor.

[0011] Optionally, step one specifically includes:

[0012] The motion model of a single rotor blade is established with the inner end of the rotor blade as the rotation center o, the XY plane as the rotation plane, the angular velocity ω as uniform circular motion, the azimuth angle of the rotation center o relative to the radar center O as φ, the radar elevation angle as θ, and the distance between the rotation center o and the radar center as R0.

[0013] Based on the motion model of each rotor blade, the rotor radar echo parameter model is obtained; the distance R from any point p on each rotor blade to the radar center t (r); where R t (r)≈R0+rcosθcos(ωt-φ);

[0014] According to the distance R from any point p on each rotor blade to the center of the radar t (r), generates the radar echo signal S of a single rotor blade r (t); where

[0015]

[0016] σ represents the scattering intensity per unit length, λ represents the carrier wavelength, and the length of the rotor blade is L;

[0017] Based on the radar echo signal S of a single rotor blade r (t), and obtain the rotor radar echo signal S R (t); where

[0018] N is the number of rotor blades.

[0019] Optionally, step three specifically includes:

[0020] The amplitude and azimuth of the grayscale gradient of the rotor time-frequency image are calculated; the amplitude is non-maximum suppressed based on the azimuth, and the double threshold algorithm is used to detect and connect the edges to obtain the contour of the time-frequency image.

[0021] Optionally, step 4 specifically includes:

[0022] According to the time-frequency diagram profile, the maximum Doppler frequency f of the rotor is obtained dmax and the rotor angular velocity ω;

[0023] The equivalent radial length of the blade is calculated using the Doppler calculation formula: Among them, L r is the equivalent radial length of the blade, λ is the radar wavelength, and ω is the angular velocity of the rotor;

[0024] Taking the blade equivalent radial length as the rotor size under radar and the length of the rotor blade as the actual rotor size, the radar pitch angle is estimated to be

[0025] In order to solve the above technical problems, the present invention also provides a helicopter radar pitch angle estimation device based on time-frequency characteristics, comprising:

[0026] A generation module, used to establish a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor, so as to generate a rotor radar echo signal;

[0027] The transformation module is used to perform short-time Fourier transform on the rotor radar echo signal to obtain the rotor time-frequency image; wherein the rotor time-frequency image is expressed as <s(u)g * (ut),e -j2πfu >, S(u) is the rotor radar echo signal, g * (ut) is the complex conjugate narrow window function;

[0028] An extraction module is used to extract the time-frequency graph contour from the rotor time-frequency image using a Canny detection algorithm;

[0029] The estimation module is used to estimate the size of the rotor under the radar view according to the time-frequency graph contour, and to estimate the radar pitch angle by combining the size of the rotor under the radar view with the actual size of the rotor.

[0030] Optionally, the generation module is specifically used for:

[0031] The motion model of a single rotor blade is established with the inner end of the rotor blade as the rotation center o, the XY plane as the rotation plane, the angular velocity ω as uniform circular motion, the azimuth angle of the rotation center o relative to the radar center O as φ, the radar elevation angle as θ, and the distance between the rotation center o and the radar center as R0.

[0032] Based on the motion model of each rotor blade, the rotor radar echo parameter model is obtained; the distance R from any point p on each rotor blade to the radar center t (r); where R t (r)≈R0+rcosθcos(ωt-φ);

[0033] According to the distance R from any point p on each rotor blade to the center of the radar t (r), generates the radar echo signal S of a single rotor blade r (t); where

[0034]

[0035] σ represents the scattering intensity per unit length, λ represents the carrier wavelength, and the length of the rotor blade is L;

[0036] Based on the radar echo signal S of a single rotor blade r (t), and obtain the rotor radar echo signal S R (t); where

[0037] N is the number of rotor blades.

[0038] Optionally, the extraction module is specifically used for:

[0039] The amplitude and azimuth of the grayscale gradient of the rotor time-frequency image are calculated; the amplitude is non-maximum suppressed based on the azimuth, and the double threshold algorithm is used to detect and connect the edges to obtain the contour of the time-frequency image.

[0040] Optionally, the estimation module is specifically used for:

[0041] According to the time-frequency diagram profile, the maximum Doppler frequency f of the rotor is obtained dmax and the rotor angular velocity ω;

[0042] The equivalent radial length of the blade is calculated using the Doppler calculation formula: Among them, L r is the equivalent radial length of the blade, λ is the radar wavelength, and ω is the angular velocity of the rotor;

[0043] Taking the blade equivalent radial length as the rotor size under radar and the length of the rotor blade as the actual rotor size, the radar pitch angle is estimated to be

[0044] In order to solve the above technical problems, the present invention further provides a helicopter radar pitch and viewing angle estimation terminal based on time-frequency characteristics, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement a helicopter radar pitch and viewing angle estimation method based on time-frequency characteristics of an embodiment of the present invention.

[0045] In order to solve the above technical problems, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, a method for estimating the pitch and direction angle of a helicopter radar based on time-frequency characteristics of an embodiment of the present invention is implemented.

[0046] The method and device for estimating the helicopter radar pitch viewing angle based on time-frequency characteristics of the present invention have the following beneficial effects: the helicopter radar pitch viewing angle can be estimated in real time, providing a basis for predicting the helicopter maneuvering direction, and solving the problems of the existing algorithm being complex, low in computational efficiency, and unable to achieve real-time estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a schematic diagram of a helicopter radar elevation viewing angle estimation method based on time-frequency characteristics according to an embodiment of the present invention;

[0048] Figure 2 is a schematic diagram of a motion model of a single rotor blade according to an embodiment of the present invention;

[0049] Figure 3 is a schematic diagram of a neighborhood of pixel point 8 according to an embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of main modules of a device for estimating the pitch and viewing angle of a helicopter radar based on time-frequency characteristics according to an embodiment of the present invention;

[0051] Figure 5 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0052] Figure 6 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, a method for real-time estimation of helicopter radar pitch angle of view based on time-frequency characteristics provided by an embodiment of the present invention mainly includes the following steps:

[0055] Step 1: Establish a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor to generate a rotor radar echo signal.

[0056] The helicopter rotor motion characteristic is that the rotor blades perform uniform circular motion around the rotation center. In this step, a motion model can be established for each rotor blade separately. The motion model of each rotor blade constitutes a rotor radar echo parameter model. The radar echo signal of the rotor blade is obtained from the motion model of a single rotor blade, and the rotor radar echo signal is obtained based on the radar echo signal of a single rotor blade.

[0057] Specifically, Figure 2 The motion model of a single rotor blade is shown, with the inner end of the rotor blade as the rotation center o, the length of the rotor blade is L, the XY plane is the rotation plane, and the angular velocity ω is uniform circular motion. The azimuth angle of the rotation center o relative to the radar center O is φ, the radar pitch angle is θ, and the distance between the rotation center o and the radar center is R0. Among them, at time t = 0, the line source L is on the x-axis. In order to study its echo characteristics, the distance between each point on the blade and the radar at each time must be determined. Assume that at time t, the distance between any point p on the rotor blade and the rotation center o is r.

[0058] Then the distance R from any point p on the rotor blade to the radar center O is t (r) can be expressed as:

[0059]

[0060] Since R0>>r, we have R t (r)≈R0+rcosθcos(ωt-φ),

[0061] Without considering the carrier frequency, the radar echo signal Sr(t) of a single rotor blade can be expressed as:

[0062]

[0063] in, σ represents the scattering intensity per unit length, and λ represents the carrier wavelength;

[0064] When the number of blades is N, the rotor radar echo signal can be expressed as:

[0065]

[0066] Step 2: Perform short-time Fourier transform on the rotor radar echo signal to obtain the rotor time-frequency image.

[0067] The target echo signals detected by radar are transient and unstable. They contain many instantaneous frequency components. In order to obtain the instantaneous frequency of each component, Gabor, inspired by quantum mechanics, suggested using two-dimensional coordinates of time and frequency to represent the signal. In 1946, he used Gaussian function as the expansion function to decompose the signal and proposed Gabor expansion. He used a very narrow window function to extract the signal and calculated its Fourier transform. The spectrum is called the local spectrum. His work led to the short-time Fourier transform (STFT), which is a two-dimensional representation of the signal in time and frequency. The definition of STFT is:

[0068]

[0069] In this step, in the short-time Fourier transform, a narrow window function is first used to multiply the source signal (i.e., the rotor radar echo signal) to achieve windowing and translation near u, and then Fourier transform is performed. The resulting rotor time-frequency image can be expressed as: <s(u)g * (ut),e -j2πfu >, S(u) is the rotor radar echo signal, g * (ut) is the complex conjugate narrow window function.

[0070] Step 3: Use the Canny detection algorithm to extract the time-frequency graph contour from the rotor time-frequency image.

[0071] The Canny algorithm is also called the Canny edge detection algorithm or the Canny edge detection operator. It is a multi-level edge detection algorithm. This step uses the Canny detection algorithm to perform edge detection and extract the contour of the time-frequency graph, which significantly reduces the data size of the image while retaining the original image properties.

[0072] In an embodiment of the present invention, step three can be implemented based on the following steps: calculating the amplitude and azimuth of the grayscale value gradient of the rotor time-frequency image; performing non-maximum suppression on the amplitude based on the azimuth, and using a dual threshold algorithm to detect and connect edges to obtain the time-frequency graph contour.

[0073] The Canny algorithm is a first-order derivative gradient operator proposed by John Canny. Its implementation process includes: gradient calculation, non-maximum suppression and double threshold detection. The specific implementation of this step can be referred to as follows:

[0074] 1) Use the finite difference of the first-order partial derivative to calculate the magnitude and direction of the gray value gradient

[0075] Regarding the grayscale gradient of the rotor time-frequency image, the finite difference of the first-order partial derivative can be used for approximation, so that two matrices of partial derivatives of the rotor time-frequency image in the x and y directions can be obtained, the x-direction partial derivative matrix P(i, j) and the y-direction partial derivative matrix Q(i, j), which can be expressed as:

[0076] P(i,j)≈(S(i,j+1)-S(i,j)+S(i+1,j+1)-S(i+1,j)) / 2

[0077] Q(i,j)≈(S(i,j)-S(i+1,j)+S(i,j+1)-S(i+1,j+1)) / 2;

[0078] Calculate the magnitude M(i,j) and azimuth θ(i,j) of the grayscale gradient based on the x-direction partial derivative matrix P(i,j) and the y-direction partial derivative matrix Q(i,j);

[0079]

[0080] θ(i,j)=arctan(Q(i,j) / P(i,j));

[0081] 2) Non-maximum suppression of the amplitude of the gray value gradient

[0082] The amplitude obtained by 1) is a matrix. The larger the element value in the matrix, the larger the gradient value of the corresponding point in the rotor time-frequency image, but this does not mean that the point is the edge. In the Canny algorithm, non-maximum suppression is an important step in edge detection. In a popular sense, it means finding the local maximum value of the pixel point and setting the grayscale value corresponding to the non-maximum point to 0, so that a large part of the non-edge points can be eliminated;

[0083] To perform non-maximum suppression, we must first determine whether the grayscale value of pixel C is the maximum in its 8-neighborhood. Figure 3, the direction of the line in the figure is the gradient direction of point C, so it can be determined that its local maximum value must be distributed on this line, that is, in addition to point C, the values ​​of the intersection points dTmp1 and dTmp2 of the gradient direction may also be local maximum values. Therefore, judging the grayscale of point C and the grayscale of these two points can determine whether point C is the local maximum grayscale point in its neighborhood. If, after judgment, the grayscale value of point C is less than either of the two points, it means that point C is not a local maximum value, then point C can be excluded as an edge. The above is the working principle of non-maximum suppression. But in fact, only the values ​​of 8 points in the neighborhood of point C can be obtained, and dTmp1 and dTmp2 are not among them. To obtain these two values, it is necessary to perform linear interpolation on the known grayscales at both ends of the two points, that is, interpolate dTmp1 according to g1 and g2 in the figure, and interpolate dTmp2 according to g3 and g4, which requires its gradient direction.

[0084] After completing non-maximum suppression, a binary image will be obtained. The grayscale values ​​of non-edge points are all 0, and the grayscale values ​​of local grayscale maximum points that may be edges can be set to 128.

[0085] 3) Use dual threshold algorithm to detect and connect edges

[0086] The detection results obtained in the previous step contain many false edges caused by noise and other reasons, which need further processing. The method to reduce the number of false edges in the Canny algorithm is to use a double threshold algorithm. Select two thresholds (high threshold and low threshold), and get an edge image according to the high threshold. Such an image contains very few false edges, but due to the high threshold, the edges of the generated image may not be closed. To solve such a problem, another low threshold is used. In the high threshold image, the edges are linked into contours. When the endpoint of the contour is reached, the algorithm will find a point that meets the low threshold in the 8 neighborhood points of the breakpoint, and then collect new edges based on this point until the edge of the entire image is closed, and the contour of the target can be obtained.

[0087] Step 4: Estimate the size of the rotor as seen from the radar according to the time-frequency graph contour, and estimate the radar pitch angle by combining the size of the rotor as seen from the radar with the actual size of the rotor.

[0088] The radar pitch viewing angle estimated by the embodiment of the present invention is used to predict the maneuvering direction of the helicopter.

[0089] According to the extracted time-frequency profile, the maximum Doppler frequency f of the rotor can be obtained: dmax And the rotor angular velocity ω, according to the Doppler calculation formula:

[0090]

[0091] Where λ is the radar wavelength, vr is the rotor radial velocity, L r is the equivalent radial length of the blade, ω is the angular velocity of the rotor;

[0092] Then the equivalent radial length of the blade is:

[0093]

[0094] The blade equivalent radial length is taken as the rotor size under radar view, and the length of the rotor blade is taken as the actual rotor size. Figure 2 , the radar elevation angle θ can be expressed as:

[0095]

[0096] like Figure 4 As shown, an embodiment of the present invention provides a helicopter radar elevation viewing angle estimation device 400 based on time-frequency characteristics, which mainly includes a generation module 401, a transformation module 402, an extraction module 403 and an estimation module 404.

[0097] in

[0098] A generating module 401 is used to establish a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor to generate a rotor radar echo signal;

[0099] The transformation module 402 is used to perform short-time Fourier transform on the rotor radar echo signal to obtain a rotor time-frequency image; wherein the rotor time-frequency image is expressed as <s(u)g * (ut),e -j2πfu >, S(u) is the rotor radar echo signal, g * (ut) is the complex conjugate narrow window function;

[0100] An extraction module 403 is used to extract the time-frequency graph contour from the rotor time-frequency image using a Canny detection algorithm;

[0101] The estimation module 404 is used to estimate the size of the rotor as seen from the radar according to the time-frequency graph profile, and estimate the radar pitch angle by combining the size of the rotor as seen from the radar with the actual size of the rotor.

[0102] In the embodiment of the present invention, the generating module 401 may be specifically used for:

[0103] The motion model of a single rotor blade is established with the inner end of the rotor blade as the rotation center o, the XY plane as the rotation plane, the angular velocity ω as uniform circular motion, the azimuth angle of the rotation center o relative to the radar center O as φ, the radar elevation angle as θ, and the distance between the rotation center o and the radar center as R0.

[0104] Based on the motion model of each rotor blade, the rotor radar echo parameter model is obtained; the distance R from any point p on each rotor blade to the radar center t (r); where R t (r)≈R0+rcosθcos(ωt-φ);

[0105] According to the distance R from any point p on each rotor blade to the center of the radar t (r), generates the radar echo signal S of a single rotor blade r (t); where

[0106]

[0107] σ represents the scattering intensity per unit length, λ represents the carrier wavelength, and the length of the rotor blade is L;

[0108] Based on the radar echo signal S of a single rotor blade r (t), and obtain the rotor radar echo signal S R (t); where

[0109] N is the number of rotor blades.

[0110] In the embodiment of the present invention, the extraction module 403 may be specifically used for:

[0111] The amplitude and azimuth of the grayscale gradient of the rotor time-frequency image are calculated; the amplitude is non-maximum suppressed based on the azimuth, and the double threshold algorithm is used to detect and connect the edges to obtain the contour of the time-frequency image.

[0112] In the embodiment of the present invention, the estimation module 404 may be specifically used for:

[0113] According to the time-frequency diagram profile, the maximum Doppler frequency f of the rotor is obtained dmax and the rotor angular velocity ω;

[0114] The equivalent radial length of the blade is calculated using the Doppler calculation formula: Among them, L r is the equivalent radial length of the blade, λ is the radar wavelength, and ω is the angular velocity of the rotor;

[0115] Taking the blade equivalent radial length as the rotor size under radar and the length of the rotor blade as the actual rotor size, the radar pitch angle is estimated to be

[0116] Figure 5An exemplary system architecture 500 is shown to which a helicopter radar elevation and viewing angle estimation method based on time-frequency characteristics or a helicopter radar elevation and viewing angle estimation device based on time-frequency characteristics according to an embodiment of the present invention can be applied.

[0117] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, 503, a network 504 and a server 505. Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505. Network 504 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0118] The user can use the terminal devices 501, 502, 503 to interact with the server 505 through the network 504 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 501, 502, 503.

[0119] The terminal devices 501 , 502 , and 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0120] Server 505 may be a server that provides various services, such as a backend management server that provides support for shopping websites browsed by users using terminal devices 501, 502, and 503. The backend management server may analyze and process received data such as product information query requests, and feed back the processing results to the terminal device.

[0121] It should be noted that the helicopter radar pitch viewing angle estimation method based on time-frequency characteristics provided in the embodiment of the present invention is generally executed by the server 505. Accordingly, the helicopter radar pitch viewing angle estimation device based on time-frequency characteristics is generally arranged in the server 505.

[0122] It should be understood that Figure 5 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0123] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer system 600 of a terminal device suitable for implementing an embodiment of the present invention. Figure 6 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0124] like Figure 6As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0125] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0126] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the system of the present invention are executed.

[0127] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0128] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0129] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be arranged in a processor, for example, they may be described as: a processor including a generation module, a transformation module, an extraction module and an estimation module. The names of these modules do not constitute a limitation on the modules themselves in certain cases. For example, the extraction module may also be described as a "module for extracting the contour of the time-frequency graph from the rotor time-frequency image using the Canny detection algorithm".

[0130] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by a device, the device includes: step 1, establishing a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor to generate a rotor radar echo signal; step 2, performing a short-time Fourier transform on the rotor radar echo signal to obtain a rotor time-frequency image; step 3, extracting a time-frequency graph contour from the rotor time-frequency image using a Canny detection algorithm; step 4, estimating the rotor size under radar view according to the time-frequency graph contour, and estimating the radar pitch angle by combining the rotor size under radar view with the actual size of the rotor.

[0131] In summary, the helicopter radar pitch viewing angle estimation method and device based on time-frequency characteristics of the present invention can estimate the helicopter radar pitch viewing angle in real time, provide a basis for predicting the helicopter maneuvering direction, and solve the problems of complex existing algorithms, low computational efficiency, and inability to achieve real-time estimation.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the pitch and viewing angle of a helicopter radar based on time-frequency characteristics, characterized in that: include: Step 1: Establish a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor to generate a rotor radar echo signal; Step 2: Perform short-time Fourier transform on the rotor radar echo signal to obtain a rotor time-frequency image; Among them, the rotor time-frequency image is expressed as <s(u)g * (ut),e -j2πfu >, S(u) is the rotor radar echo signal, g * (ut) is the complex conjugate narrow window function; Step 3: Use the Canny detection algorithm to extract the time-frequency graph contour from the rotor time-frequency image; Step 4: Estimate the size of the rotor as seen from the radar according to the time-frequency graph contour, and estimate the radar pitch angle by combining the size of the rotor as seen from the radar with the actual size of the rotor.

2. The method according to claim 1, characterized in that Step 1 specifically includes: With the inner end of the rotor blade as the rotation center o, the XY plane as the rotation plane, and the angular velocity ω as a uniform circular motion, the azimuth of the rotation center o relative to the radar center O is The radar pitch angle is θ, the distance between the rotation center o and the radar center is R0, and the motion model of a single rotor blade is established; Based on the motion model of each rotor blade, the rotor radar echo parameter model is obtained; the distance R from any point p on each rotor blade to the radar center t (r); where According to the distance R from any point p on each rotor blade to the center of the radar t (r), generates the radar echo signal S of a single rotor blade r (t); where σ represents the scattering intensity per unit length, λ represents the carrier wavelength, and the length of the rotor blade is L; Based on the radar echo signal S of a single rotor blade r (t), and obtain the rotor radar echo signal S R (t); where is the number of rotor blades.

3. The method according to claim 1, characterized in that Step three specifically includes: The amplitude and azimuth of the grayscale gradient of the rotor time-frequency image are calculated; the amplitude is non-maximum suppressed based on the azimuth, and the double threshold algorithm is used to detect and connect the edges to obtain the contour of the time-frequency image.

4. The method according to claim 1, characterized in that Step 4 specifically includes: According to the time-frequency diagram profile, the maximum Doppler frequency f of the rotor is obtained dmax and the rotor angular velocity ω; The equivalent radial length of the blade is calculated using the Doppler calculation formula: Among them, L r is the equivalent radial length of the blade, λ is the radar wavelength, and ω is the angular velocity of the rotor; Taking the blade equivalent radial length as the rotor size under radar and the length of the rotor blade as the actual rotor size, the radar pitch angle is estimated to be 5. A helicopter radar pitch angle estimation device based on time-frequency characteristics, characterized in that: include: A generation module, used to establish a rotor radar echo parameter model according to the helicopter rotor motion characteristics and the actual size of the rotor, so as to generate a rotor radar echo signal; A transformation module is used to perform short-time Fourier transform on the rotor radar echo signal to obtain a rotor time-frequency image; Among them, the rotor time-frequency image is expressed as <s(u)g * (ut),e -j2πfu >, S(u) is the rotor radar echo signal, g * (ut) is the complex conjugate narrow window function; An extraction module is used to extract the time-frequency graph contour from the rotor time-frequency image using a Canny detection algorithm; The estimation module is used to estimate the size of the rotor under the radar view according to the time-frequency graph contour, and to estimate the radar pitch angle by combining the size of the rotor under the radar view with the actual size of the rotor.

6. The device according to claim 5, characterized in that The generation module is specifically used for: With the inner end of the rotor blade as the rotation center o, the XY plane as the rotation plane, and the angular velocity ω as a uniform circular motion, the azimuth of the rotation center o relative to the radar center O is The radar pitch angle is θ, the distance between the rotation center o and the radar center is R0, and the motion model of a single rotor blade is established; Based on the motion model of each rotor blade, the rotor radar echo parameter model is obtained; the distance R from any point p on each rotor blade to the radar center t (r); where According to the distance R from any point p on each rotor blade to the center of the radar t (r), generates the radar echo signal S of a single rotor blade r (t); where σ represents the scattering intensity per unit length, λ represents the carrier wavelength, and the length of the rotor blade is L; Based on the radar echo signal S of a single rotor blade r (t), and obtain the rotor radar echo signal S R (t); where N is the number of rotor blades.

7. The device according to claim 5, characterized in that The extraction module is specifically used for: The amplitude and azimuth of the grayscale gradient of the rotor time-frequency image are calculated; the amplitude is non-maximum suppressed based on the azimuth, and the double threshold algorithm is used to detect and connect the edges to obtain the contour of the time-frequency image.

8. The device according to claim 5, characterized in that The estimation module is specifically used to: According to the time-frequency diagram profile, the maximum Doppler frequency f of the rotor is obtained dmax and the rotor angular velocity ω; The equivalent radial length of the blade is calculated using the Doppler calculation formula: Among them, L r is the equivalent radial length of the blade, λ is the radar wavelength, and ω is the angular velocity of the rotor; Taking the blade equivalent radial length as the rotor size under radar and the length of the rotor blade as the actual rotor size, the radar pitch angle is estimated to be 9. A helicopter radar pitch angle estimation terminal based on time-frequency characteristics, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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