Unmanned aerial vehicle positioning method and device, medium and product

Abstract

The invention discloses an unmanned aerial vehicle positioning method and device, a medium and a product, and relates to the field of unmanned aerial vehicle positioning. An electromagnetic wave signal emitted by the unmanned aerial vehicle is received at each reconnaissance site, and an IQ signal with a timestamp is obtained through digital signal processing; then estimating the absolute time of signal arrival at each reconnaissance site by using a time estimation method based on a Zadoff-Chu sequence; and finally, the absolute time estimated by each reconnaissance site is uploaded to an upper computer system, and the position of the unmanned aerial vehicle is calculated in the upper computer system by using a TDOA method. According to the method, the Zadoff-Chu sequence contained in the unmanned aerial vehicle signal frame is used for positioning, and the method is simple and efficient; and meanwhile, the specific CP sequence characteristic of the OFDM signal segment is utilized, so that the time delay estimation precision is improved, and the positioning precision of the unmanned aerial vehicle is further improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) positioning, and in particular to a method, device, medium, and product for UAV positioning. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] In recent years, the rapid development of drone technology has brought many conveniences, but also new security challenges. Accurate and reliable drone positioning technology has become the key to ensuring airspace safety and efficient management. Compared with traditional active radar-based positioning methods, passive positioning technology only uses electromagnetic wave radiation sources in the environment for positioning. It has the advantages of concealment, anti-interference, and low cost. It has shown great potential in the field of drone positioning and has become a research hotspot. Passive time difference of arrival (TDOA) technology has become the main method used for passive positioning of drones due to its simplicity and high accuracy. This method is based on the difference in distance between each reference base station and the object to be located. The relative position of the object to be located relative to each reference base station is inferred by solving a set of nonlinear hyperbolic equations.

[0004] One of the core technologies of TDOA is time delay estimation. Even using different time delay estimation techniques on the same data can result in significant discrepancies in the final positioning. Currently, time delay estimation techniques mainly use cross-correlation, generalized cross correlation (GCC), adaptive filtering, and maximum likelihood estimation.

[0005] Among them, the TDOA UAV positioning method based on the cross-correlation method estimates the time delay between signals by detecting the peak of the signal correlation function. This method is simple and highly accurate, but it is extremely susceptible to noise, multipath effects, etc. in actual engineering, resulting in large positioning errors in actual engineering. The TDOA UAV positioning method based on the generalized cross-correlation method first calculates the cross-power spectrum of the signal, then adds different weighting functions according to different situations to highlight the correlation peak, and finally performs the generalized cross-correlation function to find the peak position and obtain the signal arrival time. This method is highly complex and easily affected by noise, resulting in poor positioning accuracy.

[0006] The core of the TDOA UAV positioning method based on adaptive filtering is to process the received signal through an adaptive filter to estimate the signal arrival time. This method has high computational complexity and its performance is extremely dependent on the parameter settings of the filter. It may not be applicable to actual engineering projects.

[0007] In summary, the current time delay estimation methods have disadvantages such as high complexity and poor positioning accuracy in UAV positioning. Summary of the Invention

[0008] The purpose of the present invention is to provide a drone positioning method, device, medium and product to solve the above problems, which are high complexity and poor accuracy of current drone positioning methods.

[0009] The technical solutions of the present invention are as follows: A method for positioning a UAV, comprising: Step S1: Set n reconnaissance sites; Step S2: Receive the electromagnetic wave signal transmitted by the UAV at each reconnaissance site and obtain the IQ signal with a time stamp through digital signal processing; Step S3: Estimate the absolute time of arrival of the signal at each reconnaissance station using a time estimation method based on the Zadoff-Chu sequence; Step S4: The absolute time estimated by each reconnaissance station is uploaded to the host computer system, and the position of the UAV is calculated using the TDOA method in the host computer system.

[0010] Furthermore, the time estimation method based on the Zadoff-Chu sequence in step S3 includes: Step S31: Use the multi-template cross-correlation matching method to determine whether the Zadoff-Chu sequence exists. If so, proceed to step S32; otherwise, terminate the process. Step S32: If the Zadoff-Chu sequence exists, the absolute time of signal arrival is obtained using the CP sequence blind matching method.

[0011] Furthermore, the multi-template cross-correlation matching method in step S31 includes: Step S311: Generate a local LFM signal, whose frequency is consistent with the scanning parameters of the reconnaissance site; Step S312: Generate a multi-mode LFM signal consisting of multiple local LFM signals with a frequency difference of 9 MHz, covering a total range of 99 MHz; Step S313: performing time domain cross-correlation on the IQ signal and the multi-mode LFM signal; Step S314: If the cross-correlation peak value in the cross-correlation result exceeds the preset threshold, it is determined that a Zadoff-Chu sequence exists; otherwise, the process is terminated.

[0012] Furthermore, the step S313 includes: make is the IQ signal, For a multi-template LFM signal, the time domain cross-correlation function between the two is The calculation is as follows:

[0013] in: express Take the conjugate after Fourier transform.

[0014] Furthermore, the step S314 includes: like , then it is determined that the Zadoff-Chu sequence exists, otherwise the process is terminated.

[0015] Furthermore, the CP sequence blind matching method in step S32 includes: Step S321: Set the cyclic prefix CP sequence length of the OFDM signal to , the symbol body length is ;in, , ; Step S322: Set the initial value , , two CP sequence signal segments are obtained through IQ signal. and ;in, is the starting sampling point number; Indicates that in the IQ signal, starting from the starting sampling point, the length is sub-signal segment of Indicates that in the IQ signal, after the starting sampling point Length starts from sub-signal segment of Step S323: Calculation and Pearson correlation value of ; Step S324: traverse to obtain all different and Pearson correlation value under the value ; Step S325: Take the maximum The corresponding value is the optimal CP sequence length of OFDM signal, is the optimal symbol length of OFDM signal; Step S326: Based on the optimal CP sequence length and the optimal symbol length , two CP sequence signal segments are obtained through IQ signal. and ;in, Indicates that in the IQ signal, starting from the starting sampling point, the length is sub-signal segment of Indicates that in the IQ signal, after the starting sampling point Length starts from sub-signal segment of Step S327: Calculation and The autocorrelation of is used to estimate the time delay using the peak value, which is the absolute time of signal arrival.

[0016] Furthermore, in step S1, n≥3.

[0017] The present invention further provides an electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory, so that the at least one processor executes a drone positioning method as described above.

[0018] The present invention also proposes a computer-readable storage medium, which is used to store instructions. When the instructions are executed, a drone positioning method as described above is implemented.

[0019] The present invention also provides a computer program product, which implements the above-mentioned drone positioning method when executed by a processor.

[0020] Compared with the existing technology, the beneficial effects of the present invention are: The present invention utilizes the ZC sequence contained in the drone signal frame for positioning, which is simple and efficient. At the same time, it utilizes the CP sequence characteristics unique to the OFDM signal segment to improve the delay estimation accuracy, thereby improving the drone positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of a UAV positioning method; Figure 2 Detailed flowchart of step S3 in an embodiment of the present invention; Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of the present invention; DETAILED DESCRIPTION It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0022] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0023] Example 1 Existing drone passive positioning generally uses the TDOA system, whose core step is to "estimate the time difference of arrival of the same drone signal received by each reconnaissance station." In this system, time delay estimation technology directly determines the final positioning accuracy. Currently, three types of methods are commonly used: (1) Direct cross-correlation method: directly calculate the cross-correlation function of the two received signals, and use the peak value to correspond to the delay; (2) Generalized cross-correlation method: first perform frequency domain weighting on the signal, and then calculate the cross-correlation; (3) Adaptive filtering method - using adaptive filters to dynamically track delay parameters.

[0024] However, the above methods all exhibit the drawbacks of high complexity and poor positioning accuracy in actual drone usage. Therefore, this embodiment proposes a new time-of-arrival measurement process based on the drone signal frame structure and utilizing the inherent Zadoff-Chu sequence and cyclic prefix sequence within the frame. By processing this signal segment, the drone can be located. This method is simple and offers high positioning accuracy, reducing complexity and improving precision.

[0025] In this embodiment, please refer to Figure 1 , a UAV positioning method specifically includes the following steps: Step S1: Set n reconnaissance sites; In this embodiment, specifically, in step S1, n≥3; Step S2: Receive the electromagnetic wave signal transmitted by the UAV at each reconnaissance site and obtain the IQ signal with a time stamp through digital signal processing; Step S3: Estimate the absolute time of arrival of the signal at each reconnaissance station using a time estimation method based on the Zadoff-Chu sequence; Step S4: The absolute time estimated by each reconnaissance station is uploaded to the host computer system, and the position of the UAV is calculated using the TDOA method in the host computer system.

[0026] In this embodiment, please refer to Figure 2 The time estimation method based on the Zadoff-Chu sequence in step S3 includes: Step S31: Use the multi-template cross-correlation matching method to determine whether the Zadoff-Chu sequence exists. If so, proceed to step S32; otherwise, terminate the process. Step S32: If the Zadoff-Chu sequence exists, the absolute time of signal arrival is obtained using the CP sequence blind matching method.

[0027] In this embodiment, please refer to Figure 2 The multi-template cross-correlation matching method in step S31 includes: Step S311: Generate a local LFM signal, whose frequency is consistent with the scanning parameters of the reconnaissance site; Step S312: Generate a multi-template LFM signal consisting of multiple local LFM signals with a frequency difference of 9 MHz, covering a total range of 99 MHz; that is, generate a multi-template LFM signal using multiple local LFM signals, where the frequency difference between adjacent templates is 9 MHz, totaling 99 MHz; Step S313: performing time domain cross-correlation on the IQ signal and the multi-mode LFM signal; Step S314: If the cross-correlation peak value in the cross-correlation result exceeds the preset threshold, it is determined that a Zadoff-Chu sequence exists; otherwise, the process is terminated.

[0028] In this embodiment, specifically, step S313 includes: make is the IQ signal, For a multi-template LFM signal, the time domain cross-correlation function between the two is The calculation is as follows:

[0029] in: express Take the conjugate after Fourier transform.

[0030] In this embodiment, specifically, step S314 includes: like , then it is determined that the Zadoff-Chu sequence exists, otherwise the process is terminated; that is, if , it means that the input signal segment contains a drone signal, and the process continues to the next step; otherwise, the process is terminated; In this embodiment, please refer to Figure 2 The CP sequence blind matching method in step S32 includes: Step S321: Set the cyclic prefix CP sequence length of the OFDM signal to , the symbol body length is ;in, , ; Step S322: Set the initial value , , two CP sequence signal segments are obtained through IQ signal. and ;in, is the starting sampling point number; Indicates that in the IQ signal, starting from the starting sampling point, the length is sub-signal segment of Indicates that in the IQ signal, after the starting sampling point Length starts from sub-signal segment of Step S323: Calculation and Pearson correlation value of ; Step S324: traverse to obtain all different and Pearson correlation value under the value ; Step S325: Take the maximum The corresponding value is the optimal CP sequence length of OFDM signal, is the optimal symbol length of OFDM signal; Step S326: Based on the optimal CP sequence length and the optimal symbol length , two CP sequence signal segments are obtained through IQ signal. and ; in, Indicates that in the IQ signal, starting from the starting sampling point, the length is sub-signal segment of Indicates that in the IQ signal, after the starting sampling point Length starts from sub-signal segment of Step S327: Calculation and The autocorrelation of is used to estimate the time delay using the peak value, which is the absolute time of signal arrival.

[0031] Based on the same technical concept, an embodiment of the present invention also provides an electronic device that can implement a drone positioning method process provided by the above embodiment of the present invention. In one embodiment, the electronic device can be a server, or a terminal device or other electronic device. Figure 3 As shown, the electronic device may include: At least one processor, and a memory connected to the at least one processor. The embodiment of the present invention does not limit the specific connection medium between the processor and the memory. Figure 3 The example in this article is that the processor and memory are connected via a bus. Figure 3 The connections between the other components are shown in bold lines, which are only for illustration and not intended to be limiting. The bus can be divided into address bus, data bus, control bus, etc. Figure 3 The processor is represented by a single thick line, but this does not mean that there is only one bus or only one type of bus. Alternatively, the processor can also be called a controller, without any limitation on the name.

[0032] In an embodiment of the present invention, the memory stores instructions that can be executed by at least one processor, and the at least one processor can execute the drone positioning method discussed above by executing the instructions stored in the memory. The processor can implement Figure 3 The functions of each module in the device shown.

[0033] Among them, the processor is the control center of the device, which can use various interfaces and lines to connect the various parts of the entire control device, and monitor the device as a whole by running or executing instructions stored in the memory and calling data stored in the memory, the various functions of the device and processing data.

[0034] In an optional design, the processor may include one or more processing units, and the processor may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, user interface, and application programs, and the modem processor primarily processes wireless communications. It is understood that the modem processor may not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip, or in some embodiments, they may be implemented on separate chips.

[0035] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the drone positioning method disclosed in the embodiments of the present invention can be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0036] As a non-volatile computer-readable storage medium, memory can be used to store non-volatile software programs, non-volatile computer executable programs and modules. Memory can include at least one type of storage medium, for example, can include flash memory, hard disk, multimedia card, card-type memory, random access memory (Random Access Memory, RAM), static random access memory (Static Random Access Memory, SRAM), programmable read-only memory (Programmable Read Only Memory, PROM), read-only memory (Read Only Memory, ROM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic memory, disk, optical disk, etc. Memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present invention can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0037] By designing and programming the processor, the code corresponding to the drone positioning method described in the aforementioned embodiment can be embedded in the chip, enabling the chip to execute the steps of the method described in the aforementioned embodiment when running. Designing and programming the processor is well known to those skilled in the art and will not be further described here.

[0038] Based on the same inventive concept, an embodiment of the present invention further provides a storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer executes a drone positioning method discussed above.

[0039] In some optional embodiments, the present invention also provides various aspects of a drone positioning method that can also be implemented in the form of a program product, which includes program code. When the program product is run on the device, the program code is used to enable the control device to execute the steps of a drone positioning method according to various exemplary embodiments of the present invention described above in this specification.

[0040] It should be noted that although several units or subunits of the device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of a unit described above can be further divided into multiple units to be embodied. In addition, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0041] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a server, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] Program code for performing the operations of the present invention may be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0044] Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0045] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0047] In addition, in some embodiments, a computer program product is also proposed, which implements the above-mentioned drone positioning method when executed by a processor.

[0048] The above-described embodiments merely represent specific implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection of the present application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the technical concept of the present application, and all such variations and improvements fall within the scope of protection of the present application.

[0049] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of the description in this section that did not constitute prior art at the time of filing are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A method for positioning a drone, characterized in that: include: Step S1: Set n reconnaissance sites; Step S2: Receive the electromagnetic wave signal transmitted by the UAV at each reconnaissance site and obtain the IQ signal with a time stamp through digital signal processing; Step S3: Estimate the absolute time of arrival of the signal at each reconnaissance station using a time estimation method based on the Zadoff-Chu sequence; Step S4: The absolute time estimated by each reconnaissance station is uploaded to the host computer system, and the position of the UAV is calculated using the TDOA method in the host computer system.

2. A method for positioning a UAV according to claim 1, characterized in that: The time estimation method based on the Zadoff-Chu sequence in step S3 includes: Step S31: Use the multi-template cross-correlation matching method to determine whether the Zadoff-Chu sequence exists. If so, proceed to step S32; otherwise, terminate the process. Step S32: If the Zadoff-Chu sequence exists, the absolute time of signal arrival is obtained using the CP sequence blind matching method.

3. A method for positioning a UAV according to claim 2, characterized in that: The multi-template mutual correlation matching method in step S31 includes: Step S311: Generate a local LFM signal, whose frequency is consistent with the scanning parameters of the reconnaissance site; Step S312: Generate a multi-mode LFM signal consisting of multiple local LFM signals with a frequency difference of 9 MHz, covering a total range of 99 MHz; Step S313: performing time domain cross-correlation on the IQ signal and the multi-mode LFM signal; Step S314: If the cross-correlation peak value in the cross-correlation result exceeds the preset threshold, it is determined that a Zadoff-Chu sequence exists; otherwise, the process is terminated.

4. A method for positioning a UAV according to claim 3, characterized in that: The step S313 includes: make is the IQ signal, For a multi-template LFM signal, the time domain cross-correlation function between the two is The calculation is as follows: in: express Take the conjugate after Fourier transform.

5. A method for positioning a UAV according to claim 4, characterized in that: The step S314 includes: like , then it is determined that the Zadoff-Chu sequence exists, otherwise the process is terminated.

6. A method for positioning a UAV according to claim 5, characterized in that: The CP sequence blind matching method in step S32 includes: Step S321: Set the cyclic prefix CP sequence length of the OFDM signal to , the symbol body length is ;in, , ; Step S322: Set the initial value , , two CP sequence signal segments are obtained through IQ signal. and ;in, is the starting sampling point number; Indicates that in the IQ signal, starting from the starting sampling point, the length is sub-signal segment of Indicates that in the IQ signal, after the starting sampling point Length starts from sub-signal segment of Step S323: Calculation and Pearson correlation value of ; Step S324: traverse to obtain all different and Pearson correlation value under the value ; Step S325: Take the maximum The corresponding value is the optimal CP sequence length of OFDM signal, is the optimal symbol length of OFDM signal; Step S326: Based on the optimal CP sequence length and the optimal symbol length , two CP sequence signal segments are obtained through IQ signal. and ;in, Indicates that in the IQ signal, starting from the starting sampling point, the length is sub-signal segment of Indicates that in the IQ signal, after the starting sampling point Length starts from sub-signal segment of Step S327: Calculation and The autocorrelation of is used to estimate the time delay using the peak value, which is the absolute time of signal arrival.

7. The method for positioning a UAV according to claim 1, wherein: In step S1, n≥3.

8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory, so that the at least one processor executes a drone positioning method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store instructions, and when the instructions are executed, a drone positioning method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that When the computer program is executed by a processor, the method for positioning a drone according to any one of claims 1 to 7 is implemented.

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