Rotor motion parameter estimation method and device of unmanned aerial vehicle target

Abstract

The invention provides a method and a device for estimating rotor motion parameters of an unmanned aerial vehicle target. The method comprises the following steps: acquiring a time sequence electromagnetic scattering characteristic parameter of an unmanned aerial vehicle target in a hovering state; calculating the time sequence electromagnetic scattering characteristic parameters by using a smooth pseudo-Wigner-Ville distribution algorithm to obtain a time-frequency distribution diagram of the unmanned aerial vehicle target; performing Fourier transform on the time-frequency distribution diagram of the unmanned aerial vehicle target to obtain a micro-Doppler frequency representation diagram of the unmanned aerial vehicle target; and based on the micro-Doppler frequency representation diagram of the unmanned aerial vehicle target, obtaining the rotor motion parameters of the unmanned aerial vehicle target in the air according to the micro-Doppler frequency spectrum estimation. According to the scheme, accurate estimation of the rotor motion parameters of the unmanned aerial vehicle target in a low signal-to-noise ratio environment can be realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of unmanned aerial vehicle target parameter inversion, and in particular to a method and device for estimating rotor motion parameters of an unmanned aerial vehicle target. Background Art

[0002] UAV radar target recognition is of great significance for low-altitude defense. As one of the important measurement values ​​of radar detection, the radar cross section (RCS) time series is determined by the physical properties and motion state of the UAV. As one of the important factors affecting its motion state, the rapid and accurate estimation of rotor parameters is helpful for the subsequent estimation of the motion state and load configuration of the UAV target.

[0003] In the process of aerial drone target identification, the micro-Doppler information generated by the rotor rotation is weak due to the small size of the rotor. Compared with the strong scattering characteristics of the fuselage, the rotor's scattering characteristics are far weaker than the fuselage in terms of material structure and size, making it difficult to capture in actual radar detection, resulting in difficulty in rotor extraction and poor accuracy. At the same time, the unique physical structure of drones is that almost all drones have one or more propellers. When the drone is in flight, the rotation of the propeller causes multiple blades to rotate simultaneously, causing the radar backscatter signal to produce periodic frequency modulation, which causes the micro-Doppler effect generated on both sides of the Doppler spectrum to alias, making it difficult to directly extract the periodicity of the frequency modulation.

[0004] Therefore, there is an urgent need to provide a method and device for estimating the rotor motion parameters of a UAV target. Summary of the invention

[0005] In order to solve the problem that the rotor parameter identification of UAV targets is difficult and the motion parameter extraction accuracy is poor in actual measurement environment or low signal-to-noise ratio environment, an embodiment of the present invention provides a rotor motion parameter estimation method and device for UAV targets.

[0006] In a first aspect, an embodiment of the present invention provides a method for estimating rotor motion parameters of an unmanned aerial vehicle target, the method comprising:

[0007] Obtain the time-series electromagnetic scattering characteristic parameters of the UAV target in the hovering state;

[0008] The time-series electromagnetic scattering characteristic parameters are calculated using a smoothed pseudo-Wigner-Ville distribution algorithm to obtain a time-frequency distribution diagram of the UAV target;

[0009] Performing Fourier transform on the time-frequency distribution diagram of the UAV target to obtain a micro-Doppler frequency representation diagram of the UAV target;

[0010] According to the micro-Doppler frequency characterization diagram of the UAV target, the micro-Doppler frequency spectrum of the UAV target is obtained, so as to estimate the rotor motion parameters of the UAV target according to the micro-Doppler frequency spectrum.

[0011] In a second aspect, an embodiment of the present invention further provides a device for estimating rotor motion parameters of a UAV target, the device comprising:

[0012] An acquisition unit, used to acquire time-series electromagnetic scattering characteristic parameters of the UAV target in a hovering state;

[0013] A calculation unit, used to calculate the time-series electromagnetic scattering characteristic parameters using a smoothed pseudo-Wigner-Ville distribution algorithm to obtain a time-frequency distribution diagram of the UAV target;

[0014] A transform unit, used for performing Fourier transform on the time-frequency distribution diagram of the UAV target to obtain a micro-Doppler frequency representation diagram of the UAV target;

[0015] The estimation unit is used to obtain the micro-Doppler frequency spectrum of the UAV target according to the micro-Doppler frequency characterization diagram of the UAV target, so as to estimate the rotor motion parameters of the UAV target according to the micro-Doppler frequency spectrum.

[0016] In a third aspect, an embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any embodiment of this specification.

[0018] On the other hand, an embodiment of the present application further provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes any of the methods described in the above embodiments.

[0019] On the other hand, an embodiment of the present invention further provides a computer program product, including a computer program, which implements the steps of the above-mentioned method when executed by a processor.

[0020] The embodiment of the present invention provides a method and device for estimating the rotor motion parameters of a UAV target. First, a smoothed pseudo-Wigner-Ville distribution algorithm is used to analyze and calculate the obtained time-series electromagnetic scattering characteristic parameters, so as to obtain a distribution diagram of the frequency change law of the UAV target over time. Then, Fourier transform is performed on the time series at each frequency point in the distribution diagram to obtain a micro-Doppler frequency characterization diagram of the UAV target. Each frequency in the Doppler frequency distribution diagram is further summed to obtain the micro-Doppler frequency spectrum of the UAV target. Finally, the rotor motion parameters of the aerial UAV target are obtained according to the peak value in the micro-Doppler frequency spectrum. In this way, the present invention realizes the accurate estimation of the rotor motion parameters of the UAV target in a low signal-to-noise ratio environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention 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. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a flow chart of a method for estimating rotor motion parameters of a UAV target provided by one embodiment of the present invention;

[0023] Figures 2 to 3 They are time-frequency analysis comparison diagrams before and after preprocessing of the time-series electromagnetic scattering characteristic parameters provided by an embodiment of the present invention; in the diagram, the horizontal axis is time and the vertical axis is frequency;

[0024] Figures 4 to 5 This is a time-frequency distribution diagram of a drone target obtained by using different time-frequency analysis calculation methods provided by an embodiment of the present invention; in the diagram, the horizontal axis is time and the vertical axis is frequency;

[0025] Figure 6 to Figure 7 This is a micro-Doppler frequency characterization diagram of a drone target at different viewing angles and rotation speeds provided by an embodiment of the present invention; in the diagram, the abscissa is the rhythm frequency, and the ordinate is the speed frequency;

[0026] Figures 8 to 9 This is a rotor micro-Doppler frequency spectrum of a drone target at different viewing angles and rotation speeds provided by an embodiment of the present invention; in the figure, the abscissa is the rhythm frequency, and the ordinate is the frequency amplitude;

[0027] Fig.10 is a hardware architecture diagram of a computing device provided by an embodiment of the present invention;

[0028] Fig.11 It is a structural diagram of a rotor motion parameter estimation device for a UAV target provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] Please refer to Figure 1 The embodiment of the present invention provides a method for estimating rotor motion parameters of a UAV target, the method comprising:

[0031] Step 100, obtaining time-series electromagnetic scattering characteristic parameters of the UAV target;

[0032] Step 102, using a smoothed pseudo-Wigner-Ville distribution algorithm to analyze the time-series electromagnetic scattering characteristic parameters to obtain a time-frequency distribution diagram of the UAV target;

[0033] Step 104, performing Fourier transform on the time-frequency distribution diagram of the UAV target to obtain a micro-Doppler frequency representation diagram of the UAV target;

[0034] Step 106, obtaining the rotor micro-Doppler frequency spectrum of the UAV target according to the micro-Doppler frequency characterization diagram of the UAV target, so as to further estimate the rotor motion parameters of the UAV target.

[0035] In an embodiment of the present invention, a smoothed pseudo-Wigner-Ville distribution algorithm is first used to analyze and calculate the obtained time-series electromagnetic scattering characteristic parameters, thereby obtaining a distribution diagram of the frequency change law of the UAV target over time, and then a Fourier transform is performed on the time series at each frequency point in the distribution diagram to obtain a micro-Doppler frequency characterization diagram of the UAV target, and each frequency in the Doppler frequency distribution diagram is further summed to obtain the micro-Doppler frequency spectrum of the UAV target, and finally, the rotor motion parameters of the aerial UAV target are obtained according to the peak value in the micro-Doppler frequency spectrum; in this way, accurate estimation of the rotor motion parameters of the UAV target in a low signal-to-noise ratio environment is achieved.

[0036] For step 100:

[0037] In some embodiments, before calculating the time-series electromagnetic scattering characteristic parameters using a smoothed pseudo-Wigner-Ville distribution algorithm, a step of preprocessing the time-series electromagnetic scattering characteristic parameters is also included; the preprocessing method is a median filtering method.

[0038] In an embodiment of the present invention, electromagnetic scattering characteristic data of an aerial UAV target in a hovering state under a low signal-to-noise ratio environment at a fixed line of sight angle is first obtained to obtain the time-series electromagnetic scattering characteristic parameters of the UAV target. Since the initially acquired time-series electromagnetic scattering characteristic parameters are low signal-to-noise ratio data, and the scattering signal of the rotor is relatively weak compared to the strong electromagnetic scattering of the fuselage, the data should be preprocessed before signal processing to reduce the interference of clutter on the accuracy of subsequent signal extraction. Specifically, in an embodiment of the present invention, a median filtering signal processing method is selected, which can not only eliminate outliers in the time-series sequence of electromagnetic scattering characteristic parameters, but also minimize the damage to the original signal, so that weak rotor information can be retained, such as Figures 2 to 3 Shown is a comparison chart of the time-series electromagnetic scattering characteristic parameters of the UAV target before and after processing using the median filtering method.

[0039] Regarding step 102:

[0040] In some embodiments, the time-frequency distribution diagram of the drone target is calculated by the following formula:

[0041]

[0042] Where t is the instantaneous time, v is the instantaneous frequency, and s and τ are both integral variables.

[0043] Different from the traditional Wigner-Ville distribution (WVD) algorithm, the smoothed pseudo Wigner-Ville distribution (SPWVD) algorithm has strong applicability in suppressing cross-term interference, which is beneficial to the characterization of subsequent micro-motion characteristic signals. Therefore, in the embodiment of the present invention, the smoothed pseudo Wigner-Ville distribution (SPWVD) algorithm is used to perform time-frequency analysis on the pre-processed time series electromagnetic scattering characteristic parameters to obtain the frequency change law of the human-machine target over time, such as Figures 4 to 5 The time-frequency analysis results obtained by using the WVD and SPWVD calculation methods for the same signal are: Figures 4 to 5 It can be seen from the figure that the instantaneous variation law of the micro-Doppler frequency generated by the rotation of the rotor can be better described by adopting the smoothed pseudo-Wigner-Ville distribution algorithm in the embodiment of the present invention.

[0044] Regarding step 104:

[0045] In some specific implementations, the micro-Doppler frequency characterization diagram of the drone target is calculated by the following formula:

[0046] CVD f0 =fft(f0(t))

[0047] Wherein, f0 is a frequency point in the time-frequency distribution diagram, f0(t) is a time series corresponding to the frequency point in the time-frequency distribution diagram, and fft() is Fourier transform.

[0048] In the embodiment of the present invention, the time series at each frequency point in the time-frequency distribution diagram of the UAV target is further Fourier transformed to obtain the micro-Doppler frequency representation diagram (CVD) of the UAV target, thereby realizing the frequency representation of the micro-Doppler information in the time-frequency diagram, and then the periodic law of the micro-Doppler of the UAV target can be directly obtained from the diagram (see Figure 6 to Figure 7 ).

[0049] Regarding step 106:

[0050] In some embodiments, the micro-Doppler frequency spectrum of the drone target is obtained by the following formula:

[0051]

[0052] In the formula, f CVD (i) is the velocity frequency value corresponding to the i-th rhythm frequency in the micro-Doppler frequency characterization diagram.

[0053] In the embodiment of the present invention, the speed frequency at each rhythm frequency in the micro-Doppler frequency characterization diagram of the drone target obtained in step 104 is further summed to obtain the micro-Doppler frequency spectrum (CFS) of the drone target (e.g. Figures 8 to 9 As shown), finally, the rotor speed information of the UAV target is determined according to the frequency point where the first peak in the frequency spectrum is located. In summary, the embodiments of the present invention solve the problems of difficult extraction of rotor features and poor estimation accuracy of UAV targets in low signal-to-noise ratio environments, and achieve accurate extraction of rotor speed information of UAV targets.

[0054] like Fig.10 , Fig.11 As shown, an embodiment of the present invention provides a device for estimating rotor motion parameters of a drone target. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Fig.10 As shown, it is a hardware architecture diagram of a computing device where a rotor motion parameter estimation device for a drone target provided by an embodiment of the present invention is located, except Fig.10 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the computing device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Fig.11As shown, as a device in a logical sense, the CPU of the computing device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides a rotor motion parameter estimation device for a drone target, and the device includes:

[0055] An acquisition unit 301 is used to acquire time-series electromagnetic scattering characteristic parameters of the UAV target;

[0056] The calculation unit 302 is used to analyze the time-series electromagnetic scattering characteristic parameters by using a smoothed pseudo-Wigner-Ville distribution algorithm to obtain a time-frequency distribution diagram of the UAV target;

[0057] The transform unit 303 is used to perform Fourier transform on the time-frequency distribution diagram of the UAV target to obtain a micro-Doppler frequency representation diagram of the UAV target;

[0058] The estimation unit 304 is used to obtain the rotor micro-Doppler frequency spectrum of the UAV target according to the micro-Doppler frequency characterization diagram of the UAV target, so as to further estimate the rotor motion parameters of the UAV target.

[0059] In an embodiment of the present invention, the acquisition unit 301 can be used to execute step 100 in the above method embodiment, the calculation unit 302 can be used to execute step 102 in the above method embodiment, the transformation unit 303 can be used to execute step 104 in the above method embodiment, and the estimation unit 304 can be used to execute step 106 in the above method embodiment.

[0060] In one embodiment of the present invention, in the calculation unit 302, before calculating the time-series electromagnetic scattering characteristic parameters using the smoothed pseudo-Wigner-Ville distribution algorithm, a step of preprocessing the time-series electromagnetic scattering characteristic parameters is also included.

[0061] In one embodiment of the present invention, the preprocessing method is a median filtering method.

[0062] In one embodiment of the present invention, in the calculation unit 302, the time-frequency distribution diagram of the drone target is calculated by the following formula:

[0063]

[0064] Where t is the instantaneous time, v is the instantaneous frequency, and s and τ are both integral variables.

[0065] In one embodiment of the present invention, in the transformation unit 303, the micro-Doppler frequency characterization diagram of the UAV target is calculated by the following formula:

[0066] CVD f0 =fft(f0(t))

[0067] Wherein, f0 is a frequency point in the time-frequency distribution diagram, f0(t) is a time series corresponding to the frequency point in the time-frequency distribution diagram, and fft() is Fourier transform.

[0068] In one embodiment of the present invention, in the estimation unit 304, the micro-Doppler frequency spectrum of the UAV target is obtained by the following formula:

[0069]

[0070] In the formula, f CVD (i) is the velocity frequency value corresponding to the i-th rhythm frequency in the micro-Doppler frequency characterization diagram.

[0071] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a device for estimating rotor motion parameters of a drone target. In other embodiments of the present invention, a device for estimating rotor motion parameters of a drone target may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0072] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0073] An embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a method for estimating rotor motion parameters of a UAV target in any embodiment of the present invention is implemented.

[0074] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a method for estimating rotor motion parameters of a UAV target in any embodiment of the present invention.

[0075] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0076] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0077] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0078] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0079] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0080] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement a method for estimating rotor motion parameters of a UAV target provided by the above-mentioned method embodiments.

[0081] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0082] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0083] 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 rotor motion parameters of UAV targets, It is characterized in that include: Obtain the time-series electromagnetic scattering characteristic parameters of the UAV target in the hovering state; The time-series electromagnetic scattering characteristic parameters are calculated using a smoothed pseudo-Wigner-Ville distribution algorithm to obtain a time-frequency distribution diagram of the UAV target; Performing Fourier transform on the time-frequency distribution diagram of the UAV target to obtain a micro-Doppler frequency representation diagram of the UAV target; Based on the micro-Doppler frequency characterization diagram of the UAV target, the micro-Doppler frequency spectrum of the UAV target is obtained, so as to estimate the rotor motion parameters of the UAV target according to the micro-Doppler frequency spectrum.

2. The method according to claim 1, It is characterized in that Before calculating the time-series electromagnetic scattering characteristic parameters by using the smoothed pseudo-Wigner-Ville distribution algorithm, the method further includes a step of preprocessing the time-series electromagnetic scattering characteristic parameters.

3. The method according to claim 2, It is characterized in that The preprocessing method is a median filtering method.

4. The method according to claim 1, It is characterized in that The time-frequency distribution diagram of the UAV target is calculated by the following formula: Where t is the instantaneous time, v is the instantaneous frequency, and s and τ are both integral variables.

5. The method according to claim 1, It is characterized in that The micro-Doppler frequency characterization diagram of the UAV target is calculated by the following formula: CVD f0 =fft(f 0 (t)) In the formula, f 0 is the frequency point in the time-frequency distribution diagram, f 0 (t) is the time series corresponding to the frequency point in the time-frequency distribution diagram, and fft() is the Fourier transform.

6. The method according to claim 1, It is characterized in that The micro-Doppler frequency spectrum of the UAV target is obtained by the following formula: In the formula, f CVD (i) is the velocity frequency value corresponding to the i-th rhythm frequency in the micro-Doppler frequency characterization diagram.

7. A device for estimating rotor motion parameters of a UAV target, It is characterized in that include: An acquisition unit, used to acquire time-series electromagnetic scattering characteristic parameters of the UAV target in a hovering state; A computing unit, configured to analyze the time-series electromagnetic scattering characteristic parameters using a smoothed pseudo-Wigner-Ville distribution algorithm to obtain a time-frequency distribution diagram of the UAV target; A transform unit, used for performing Fourier transform on the time-frequency distribution diagram of the UAV target to obtain a micro-Doppler frequency representation diagram of the UAV target; The estimation unit is used to obtain the micro-Doppler frequency spectrum of the UAV target based on the micro-Doppler frequency characterization diagram of the UAV target, so as to estimate the rotor motion parameters of the UAV target according to the micro-Doppler frequency spectrum.

8. A computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, It is characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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